Ivanti Blog: Artificial Intelligence

Inventory to Intelligence: How AI and Automation Improve Endpoint Visibility

Fri, 03 Apr 2026 13:00:09 Z

Endpoint visibility has always been foundational to IT and security. You can’t secure, patch or support what you can’t see.

But as environments have become more distributed and complex, what visibility means has evolved. It’s no longer enough to know that a device exists — IT teams and organizations as a whole need to understand its health, its risk posture and its impact on both security and user experience.

This is where AI and endpoint automation start to make a practical difference. By moving endpoint visibility from static inventory to continuous intelligence, organizations can shift from reactive discovery to proactive, even autonomous operations.

Why traditional discovery practices fall short

Traditional discovery practices were built for a very different IT reality. Their approach is designed for relatively static environments, clearly defined perimeters and manual processes. That strategy doesn’t scale well in today’s hybrid, cloud-first world.

Manual discovery workflows often produce incomplete or outdated inventories. Ivanti’s 2026 Autonomous Endpoint Management Advantage Report reinforces this reality: Only 52% of organizations report using an endpoint management solution today, leaving many environments with limited centralized visibility and persistent blind spots across unmanaged or shadow IT.

In practice, this fragmentation shows up in very familiar ways. Teams often juggle multiple inventories, one from an on-prem client management tool, another from an MDM platform and yet another from identity or access systems, leaving gaps that widen as environments grow more complex.

Common challenges in manual device discovery

Manual discovery relies heavily on human input, which introduces inconsistency and error. As environments grow more distributed, these processes struggle to evolve with them, making it difficult to keep inventories accurate as devices are added, reassigned or accessed remotely. Reconciling changes across large estates becomes time-consuming and brittle, increasing the likelihood that devices fall out of view entirely.

Over time, these limitations compound. Discovery becomes episodic rather than continuous, and visibility lags behind reality. By the time inventories are reconciled, the environment has already changed.

Visibility gaps and security risks

These gaps aren’t theoretical. Ivanti’s research shows that many organizations still struggle with foundational endpoint visibility even after deploying multiple management tools. Endpoint data exists across scanners, MDM platforms and access systems, but it is rarely centralized, continuously updated, or trusted across teams. As a result, shadow IT, unmanaged devices and unknown access paths remain persistent sources of security and compliance risk.

Blind spots create real risk. Many organizations struggle to identify which devices are vulnerable or even actively accessing their environments.

When teams can’t reliably understand device exposure or access patterns, security decisions are made using incomplete or outdated data, increasing risk and delaying remediation. In fact, the above-mentioned Ivanti report highlights how common these blind spots are:

45% of organizations report challenges identifying shadow IT
41% struggle to identify vulnerabilities across devices
35% say data blind spots make it difficult to determine patch compliance.

Device discovery vs. device health monitoring

Discovery is only the first step. Knowing that a device exists doesn't tell you whether it's secure, compliant or even functioning properly. That’s where device health monitoring becomes critical.

Discovery tells you what’s present. Health monitoring adds the context that actually matters, from performance and configuration drift to overall security posture. Research from Ivanti’s 2025 Securing the Borderless Digital Landscape report underscores how significant these visibility gaps remain: Two out of five (38%) of IT professionals say they lack sufficient data about devices accessing the network, and 45% report insufficient visibility into shadow IT.

BYOD and edge devices, especially, are a concern. These can be online and still pose significant risk. It may be missing critical patches, running outdated software, drifting from configuration standards or suffering performance issues that impact users.

Presence data answers the question, “Is it there?” Health data answers, “Is it safe, compliant, and usable?” Without health insights, organizations are effectively managing endpoints in the dark.

Key indicators of endpoint health

To manage endpoints proactively, organizations need continuous visibility into key health indicators.

This includes:

Operating system and application versions
Patch and antivirus status
Configuration drift
Overall security posture

User experience signals such as crashes, latency and performance degradation also provide early warning signs that something isn’t right.

Modern platforms unify these signals into a single view, allowing IT and security teams to understand not just what devices exist, but how they're performing and where risk is emerging.

The risk of tracking only device presence

When organizations focus only on device presence, they expose themselves to both security and operational risks. Visibility without context leads to delayed detection, missed compliance requirements and reactive management.

Negative impacts on security and compliance

Tracking presence alone increases the likelihood that malware, misconfigurations or policy violations go undetected. Devices that are not enrolled in management or out of compliance may still access sensitive resources, creating gaps in enforcement. When access decisions aren’t tied to device state, enforcement becomes inconsistent by default.

Strong endpoint visibility, access and security ensure that only managed and compliant devices can reach sensitive systems and data.

Tying access to management and compliance status is critical. Conditional access, VPN and zero trust controls are only effective when visibility and enrollment are enforced consistently across endpoints.

Patch management is one of the areas where limited visibility creates the most operational strain. Our IT and security research shows that many IT teams struggle to track patch status across their full endpoint estate and to stay compliant as environments become more distributed. For example, of those we surveyed,

38% of IT and security professionals say they have difficulty tracking patch status and rollouts.
35% of teams struggle to stay compliant.

These challenges aren’t about patch availability alone. They stem from gaps in visibility into device state, ownership and real-world exposure, making it difficult to prioritize and verify remediation.

Operational inefficiencies

From an operational perspective, limited visibility leads to inefficiency. IT teams spend time troubleshooting issues that automation could resolve, chasing devices that should have been discovered automatically, and reacting to incidents rather than preventing them.

Without health data, teams are forced into a firefighting mode, responding to problems after they impact users instead of addressing them proactively.

This is exactly where AI and automation can begin to change the equation.

How AI and endpoint automation improve endpoint visibility

AI and automation turn endpoint visibility from a one-time discovery exercise into a continuous, self-sustaining capability. They enable teams to unify data, detect anomalies and maintain accurate inventories without manual effort.

Unified telemetry across multiple sources

Modern endpoint management platforms with AI and automation capabilities consolidate telemetry from discovery, UEM, MDM, patching, vulnerability and security tools into a unified, continuously updated view. This unified telemetry eliminates the need to reconcile siloed inventories and provides a shared, reliable view for both IT and security.

By normalizing data across desktop, mobile, server and IoT devices, organizations gain holistic visibility that supports faster, more confident decision-making.

Our autonomous endpoint management (AEM) research also shows that organizations make the most progress when endpoint visibility is treated as a shared objective. Teams that track metrics such as time to discovery, percentage of fully managed endpoints and exposure duration through shared dashboards are better able to align IT and security around the same data. This shared visibility turns endpoint management from siloed reporting into a coordinated, data-driven process.

AI-Powered automation and autonomous bots

Automation plays a critical role in keeping visibility current. AI-powered bots can automatically rediscover devices, reconcile duplicates, update ownership and location and detect anomalies across the environment.

When agents stop reporting or profiles break, automated workflows can repair or reinstall them without human intervention. This ensures that visibility doesn’t degrade over time and reduces the operational burden on IT teams.

Self-healing workflows for IT productivity

Self-healing workflows extend automation to the endpoint itself. Common issues such as failed updates, stopped services or configuration drift can be detected and resolved automatically, often before users notice a problem.

Endpoint automation enables these self-healing workflows to operate continuously in the background, resolving common issues without waiting for human intervention.

By resolving these issues without tickets, organizations reduce downtime, improve user experience and free IT staff to focus on higher-value initiatives. In fact, over two-thirds of IT teams today believe that AI and automation in ITSM will allow them to deliver better service experiences and give them more time to support business objectives.

Broader impact on security, productivity and user experience

When AI and automation are integrated into endpoint visibility, the benefits extend beyond IT operations. Security posture improves and users experience fewer disruptions — and productivity increases.

By combining endpoint visibility and control, organizations can reduce risk while still supporting productivity and flexible operating models.

Closing visibility gaps

AI-driven insights eliminate blind spots by continuously monitoring endpoint activity and health. Instead of relying on periodic scans or manual checks, organizations maintain real-time awareness of their endpoint environment.

This continuous visibility transforms endpoint management from a static inventory project into a living, breathing capability that adapts as the environment changes.

Improving IT operations and end-user satisfaction

Automation reduces ticket volume and accelerates resolution times, while predictive analytics help prevent downtime before it impacts users. Ring deployments, maintenance windows and self-service catalogs allow changes to be delivered with minimal disruption.

When users experience faster support and fewer interruptions, resistance to endpoint management drops and adoption improves. Over time, this creates a healthier feedback loop where visibility, automation and user experience reinforce each other instead of competing.

This is where autonomous endpoint management takes organizations next. Visibility becomes continuous instead of episodic. Automation keeps inventories accurate, health signals current and risk visible in real time.

With shared data and clear ownership, IT and security teams stop reacting to issues after the fact and start managing endpoints proactively. That shift from inventory to intelligence is what enables autonomous endpoint management, and it’s quickly becoming the standard for modern IT operations.

How AI-Driven Automation Solves Patch Management Silos

Thu, 02 Apr 2026 15:37:11 Z

"We see 10,000 critical vulnerabilities!"

"We patched everything last week!"

This conversation happens in enterprise IT departments every single day. Security teams present dashboards filled with red alerts. IT teams show deployment reports at 98% success. Both teams are looking at real data. Both are absolutely correct. And both are totally blind to what's actually happening across the endpoint environment.

This isn't a people problem — your teams aren't incompetent. It's not a process problem — your workflows aren't broken. It's a technology problem: you're asking two teams to manage the same risk using systems that show them different realities.

Security teams are given one version of reality through vulnerability scanners and threat intelligence. Meanwhile, IT teams see things differently when looking at their device management and patch deployment reports.

The tricky part is that both views can be correct in isolation and still be misleading in practice. That's how you end up in the familiar stalemate: security reports thousands of critical vulnerabilities; IT reports that patches are successfully deployed. The disconnect lives in the gap between those systems.

Why IT and security are misaligned on patching

Most organizations approach patching misalignment between IT and security by improving communication between IT and security. They schedule more meetings. They create escalation paths. They implement SLAs. And six months later, they're having the exact same argument with better PowerPoint slides.

Here's what nobody wants to admit: you can't collaborate your way out of a data fragmentation problem. When IT and security are working from fundamentally different inventories of what exists, what's vulnerable and what's been fixed, adding more coordination overhead just slows down an already broken process.

This is why the same conversation plays out again and again inside many organizations. Both teams are confident in their data, and both are “right” within the narrow context of the tools they rely on.

And that’s the problem. While both views are “right,” neither reflects the full lifecycle of risk. Vulnerability data doesn’t always reflect whether affected devices are managed or reachable. Patch reports don’t always account for unmanaged, misclassified or newly discovered endpoints that still have access to corporate resources. What’s missing is a reliable answer to the only question that actually matters: which endpoints are exposed right now?

Technology silos create conflicting realities

Most enterprises manage endpoints through a hodgepodge of systems that have evolved independently over time, each capturing only a fragment of reality.

One system may surface critical exposure without knowing whether the device is being managed. Another may confirm successful remediation without accounting for newly discovered or misclassified endpoints that still have access. The result? No reliable way to trace risk from detection through deployment to actual exposure.

Consider this: the average organization manages only 60% of their edge devices, according to Ivanti's Securing the Borderless Digital Landscape Report. That means 40% of potential entry points exist outside IT's view and outside their patch workflows. Security sees them. IT doesn't. That's your vulnerability gap. Without that continuity, teams are forced to reconcile partial views manually. Data gets debated instead of acted on.

Different data views lead to friction

Imagine it’s Monday morning: Security discovers a critical zero-day in a widely used VPN client. They send an urgent alert to IT: "30,000 vulnerable endpoints detected — patch immediately."

IT checks their deployment console: "VPN client already updated across 28,000 devices last Thursday."

Both statements are true. Security is scanning the entire network — including contractor laptops, BYOD devices and systems that briefly connected to the VPN but aren't under IT management. IT patched everything in their device inventory.

Meanwhile, 2,000 genuinely vulnerable endpoints remain exposed because they exist in Security's view but not IT's. The patch that should have taken 24 hours now requires three days of manual reconciliation.

When IT and security operate from different data sources, misaligned vulnerability management priorities are inevitable. Security teams focus on vulnerability counts, severity scores and exploit intelligence. IT teams prioritize deployment success, system stability and user impact. Both perspectives are necessary, but without a shared frame of reference, they pull in different directions.

What follows isn’t just tension; it’s decision paralysis. Remediation slows while teams reconcile inventories, validate findings and argue about scope. Vulnerabilities remain open longer than they should, not because patches aren’t available, but because there’s no single view that connects detection, deployment and exposure.

The risk of misaligned patching priorities

Misalignment slows collaboration, but more so, it creates measurable risk that extends well beyond internal friction.

Ivanti’s Autonomous Endpoint Management research reflects this challenge in practice:

38% of IT professionals report difficulty tracking patch status.
35% struggle to meet remediation timelines due to incomplete endpoint visibility.

When vulnerabilities remain open longer than necessary, the window of exposure grows. Attackers don’t wait. The CISA KEV catalog reveals the difficult truth: 30% of vulnerabilities being actively exploited right now were originally disclosed more than five years ago.

That's not a patching problem; it’s a visibility problem. Organizations aren't ignoring available patches; they're missing the endpoints that still need them.

Prolonged exposure windows and breach risk

Fragmentation stretches exposure windows in subtle ways. Devices that were never enrolled in management platforms, such as shadow BYOD, unsecured contractor devices or remote endpoints outside the traditional perimeter, often go unnoticed.

Research from Ivanti shows that only one in three employers have implemented zero trust network access for remote workers, leaving significant gaps in visibility across distributed environments. Newly discovered endpoints appear after patch reports are generated. Systems drift out of compliance between scan cycles. Each delay compounds the risk, extending the time attackers have to weaponize known weaknesses.

Common post-patch issues and IT ticket overload

Even when patches are deployed on schedule, manual patching often creates downstream issues. Failed updates, broken agents, performance problems and unexpected reboots trigger support tickets and emergency fixes. What starts as a security task quickly becomes an operational drain.

IT teams spend time resolving predictable failures instead of improving endpoint posture. Security teams see delays as unresolved risk. Users associate patching with disruption. That friction persists across teams, even when their goals are aligned.

Transforming patch management with autonomous endpoint management

AI and automation address the core disconnects in patch management by unifying visibility and reducing manual coordination. When endpoint discovery, vulnerability data, device health and patch status are correlated into a unified view, IT and security teams can work from the same facts instead of reconciling partial data across tools.

Autonomous endpoint management (AEM) brings clarity to the confusion by using AI intelligence and automation to give IT and security a single, continuously updated view of endpoints, their health and their exposure.

How AI improves patching decisions

AI improves patching decisions by prioritizing vulnerabilities based on real-world risk rather than severity scores alone. By factoring in exploit activity, asset criticality and exposure context, teams can align on what to patch first and focus effort where it will reduce risk fastest.

With autonomous endpoint management, that same Monday morning scenario plays out differently:

The vulnerability is detected, and AI immediately cross-references it against a unified endpoint inventory. It identifies 1,560 devices running the vulnerable version, including 217 devices that were previously unmanaged.

Automated patch workflows simultaneously: enroll the unmanaged devices, prioritize patching based on exposure risk and asset criticality. They then schedule deployment during low-usage windows, and begin ring-based rollout.

By the time the security team sends the alert, IT already has a real-time dashboard showing remediation in progress — with the same device count, the same exposure data and the same prioritization logic. No reconciliation necessary.

How automation accelerates remediation

Automation then turns those decisions into action. Patch workflows can be orchestrated end to end: identifying affected devices, deploying updates and validating remediation without constant manual intervention.

AI-powered intelligent patch scheduling minimizes user impact by aligning deployments with device usage patterns, maintenance windows and operational constraints. Ring-based rollouts allow patches to be validated on smaller groups before wider deployment, reducing disruption while accelerating remediation. The result is faster patching, less downtime and a more predictable process for both teams.

Self-healing workflows detect and resolve common issues automatically, such as restarting services, reinstalling agents or correcting misconfigurations. These workflows prevent avoidable incidents before they turn into support tickets.

From data debates to unified intelligence and shared visibility

AI-driven platforms unify endpoint visibility by correlating discovery data, vulnerability context, device health and patch status into a single endpoint record, with enrollment and access controls ensuring that devices are continuously discovered and managed throughout their lifecycle. IT and security teams see the same devices, the same exposure and the same remediation status in real time.

This unified intelligence eliminates debates over whose data is correct and replaces them with agreement on which risks to address first. By integrating remediation into broader endpoint workflows, teams reduce manual effort and maintain consistent patch outcomes at scale. By integrating remediation into broader endpoint workflows, teams reduce manual effort and maintain consistent patch outcomes at scale.

Shared patch ownership: powering IT and security collaboration

AI and automation only improve patch management when they’re paired with shared ownership. When IT and security teams operate from the same endpoint data and remediation workflows, accountability shifts from defending individual reports to jointly reducing exposure.

A data-driven patch process starts with mutual goals. Instead of tracking success in isolated tools, organizations align IT and security around common metrics that reflect real-world risk and operational impact. This shared measurement creates clarity on priorities and removes ambiguity around ownership.

Effective collaboration depends on metrics both teams trust and act on together. Common KPIs include:

Mean Time to Remediate (MTTR): How quickly critical vulnerabilities are resolved
Patch compliance rates: Across both managed and previously unmanaged endpoints
Exposure duration: How long high-risk vulnerabilities remain open
Endpoint visibility: Percentage of devices fully discovered and managed

These metrics shift conversations from patch volume to measured risk outcomes and help teams focus on outcomes instead of activity.

Joint ownership requires workflows that span the entire patch lifecycle. AI-driven platforms support this by automating routine tasks while surfacing exceptions that require human judgment.

IT and security leaders define guardrails for automation, including approval thresholds, testing requirements and rollout constraints. Within those boundaries, automation executes remediation consistently and at scale, without constant manual coordination. Over time, trust in the process grows, coordination overhead decreases, and patching becomes a cooperative operational responsibility rather than a point of friction.

Visit our solutions page to discover how Ivanti's autonomous endpoint management solutions give IT and security teams the unified visibility they need to eliminate patching silos and close vulnerabilities faster.

How to Implement an AI Governance Framework Using Safe, Ethical and Reliable AI Guardrails

Tue, 24 Feb 2026 13:00:02 Z

In my time at Ivanti, I've witnessed firsthand how AI acts as a force multiplier across enterprise organizations. When deployed strategically, AI accelerates decision-making and operational execution at scale in a way that teams simply can't sustain manually. However, without clear and enforceable AI guardrails, implementing AI opens organizations up to serious new risks.

Ivanti’s 2026 State of Cybersecurity Report highlights a growing disconnect I’ve observed across the industry: optimism about AI is rising, yet governance and preparedness are not keeping pace. Currently, just 50% of organizations say they have formal guardrails in place to guide the deployment and operation of AI systems and agents.

As adoption accelerates faster than governance, I'm seeing organizations face growing internal risks — shadow AI use, inconsistent data quality, biased outputs and uneven employee training to name a few.

From where I sit — spanning legal, security and HR — I can tell you this: AI governance isn't an abstract compliance exercise. It's a core requirement for trust, accountability and control.

The state of enterprise AI: a risky Wild West

Responsible AI at scale requires deliberate governance with enforceable guardrails for all employees. Ignore that, and shadow AI use will continue to grow. Our 2025 Technology at Work research report revealed that 46% of office workers use AI that aren't employer-provided. Even more concerning, nearly a third of employees (32%) keep their use of AI tools at work a secret from their employers.

Too many organizations are deploying AI without an overarching governance, and the consequences of this approach are real. Organizations can expose sensitive data. They can violate regulatory obligations. It could potentially erode market trust. A team deploys an AI platform without proper guardrails, and suddenly you have biased outputs or degraded performance. Without human oversight, AI systems generate inaccurate recommendations or trigger inappropriate actions. That creates dangerous false confidence in AI-driven outcomes.

What is an AI governance framework?

An AI governance framework is the blueprint for how we design, deploy and oversee AI systems across their lifecycle. Its purpose is to align AI use with business objectives, legal obligations and enterprise risk tolerance — with transparency and accountability built in from day one.

At Ivanti, our framework clarifies:

Who is accountable for AI decisions and outcomes
How risks are identified, assessed and mitigated
What guardrails must be in place before AI systems go live
How AI performance, behavior and impact are monitored over time

In practice, governance enables scale. Clear frameworks let us move beyond fragmented pilots and operationalize AI across the enterprise. Without it, adoption stalls.

Our position is simple: governance doesn't block innovation. It makes innovation sustainable.

3 layers of AI guardrails in an AI governance framework

As part of Ivanti’s AI Governance Council, I've learned that a comprehensive framework requires multiple layers of guardrails. Each addresses a different category of risk. Together, they form the foundation for safe, reliable AI use.

Technical Guardrails

Technical guardrails keep AI systems within predefined safety and operational parameters.

Data guardrails: Data guardrails protect data integrity and ensure AI systems are trained and operated on trusted inputs. These guardrails are typically owned by data and security teams, who establish standards for data sourcing, validation, access controls and ongoing quality monitoring. Poor data quality remains a major barrier to effective AI deployment, particularly in security, where incomplete, biased or unvalidated data can skew outcomes and degrade detection accuracy over time.

Model guardrails: Model guardrails address robustness, explainability, and bias detection to ensure AI systems behave as intended over time. These guardrails are typically designed by security, data science and platform teams, who define testing requirements for drift, bias and performance degradation before deployment and continuously thereafter, especially as models are retrained or exposed to changing operational data.

Application and output guardrails: application and output guardrails validate AI-generated outputs, particularly in decision-support or automated response scenarios. These guardrails are typically implemented by security and operations teams, who define approval thresholds, escalation paths, and human-in-the-loop controls. Without them, systems may generate inaccurate recommendations or take inappropriate actions, reinforcing false confidence in automation.

Infrastructure guardrails: infrastructure guardrails protect the systems that host and support AI workloads and are typically owned by IT and security teams. These teams enforce secure deployment practices, access controls, logging and auditability across cloud and on-prem environments, while ensuring AI services are integrated into existing security monitoring and incident response workflows.

Ethical guardrails

Ethical guardrails align AI behavior with organizational standards and define accountability when AI affects people, customers or business outcomes.

Ivanti’s AI Governance Council plays a central role here. We navigate the “gray areas” of autonomous agents. We bring together legal, security, HR, and business leaders to define acceptable use, escalation paths and accountability. When should humans intervene? How are decisions audited? Who ultimately owns the outcome when things go wrong?

When that governance is missing, the consequences escalate quickly.

Recent incidents show the cost of unclear ethical guardrails. For example, Grok, an AI chatbot developed by xAI, drew widespread criticism after generating unconsented and inappropriate images of real individuals. The failure was not only technical — it was governance-related due to ethical boundaries that weren’t sufficiently defined.

The same issue arises inside enterprises. When AI blocks a user account, flags an employee, or restricts customer access, we must know who owns the decision if it's wrong. Whether AI is used in security, HR or customer-facing systems, the ethical principles are consistent. Governance ensures accountability is defined before automation causes harm.

Regulatory and legal guardrails

Regulatory and legal guardrails ensure AI use complies with evolving global regulations, sector rules and data protection laws. Because these requirements change rapidly, teams can't operate in functional silos.

Legal must lead AI governance early. At Ivanti, we work closely with security and IT to interpret obligations and translate them into enforceable controls. Success depends on aligning from the outset to ensure compliance requirements are embedded into AI design and deployment.

Recent incidents show why regulatory guardrails cannot be an afterthought. European and UK regulators confirmed that Clearview AI’s facial recognition operations, built on scraping billions of images, were subject to privacy laws like GDPR and took enforcement actions based on violations, showing the legal risk organizations face when governance doesn’t align with regulatory expectations.

The lesson is clear. Legal and product development teams must work together early to embed regulatory obligations into AI design, deployment and operations. Governance ensures compliance requirements are enforced by default, not retroactively after regulatory scrutiny begins.

AI governance vs. AI risk management risk

Governance and risk management are closely related but distinct. Here's my take: governance sets the rules and accountability structures. Risk management focuses on identifying and mitigating specific AI-related threats throughout the system lifecycle.

Common AI risks include data leakage, bias, unreliable outputs, over-reliance on automated decisions and security weaknesses introduced through unmanaged tools or integrations. As AI systems become more autonomous, these risks compound.

Integrating AI risk mitigation into governance ensures risks are not addressed in isolation. We evaluate them alongside business impact, operational resilience and organizational risk appetite. This lets us prioritize controls where they matter most and avoid blanket restrictions that slow progress without reducing risk.

Challenges in scaling AI governance

Many organizations start with narrow AI pilots in individual teams. Scaling to enterprise-wide adoption introduces new challenges

Silos are the fastest way to undermine governance. Security, IT, legal and business teams often operate on conflicting assumptions. We need shared ownership across teams. As my colleague Sterling Parker explains, a successful vision requires involving stakeholders across the business to prevent "AI sprawl."

This transition demands a human-centric operating model. Our governance body clearly defines where AI can amplify existing roles, where additional training is required and where human oversight remains essential. Continuous feedback from employees helps ensure AI is applied where it delivers value without creating gaps in accountability or trust. We prioritize upskilling to replace fear with active adoption.

Our cybersecurity research shows that mature organizations approach these challenges differently. Organizations that rank themselves as the most advanced in cybersecurity (Level 4s) are nearly 3x as likely to use comprehensive AI guardrails compared to organizations with an intermediate level of cybersecurity maturity (Level 2s).

They invest early in governance, align leadership around shared frameworks and treat AI as a strategic capability rather than a collection of tools. These organizations are far more likely to operationalize AI across the enterprise while maintaining trust and control.

How to implement responsible AI

Building the framework is table stakes. Execution is where AI governance lives.

Start with clear policies on acceptable use and escalation. These must be practical and tied directly to your existing risk structures.

Governance must be accessible. Responsible AI is an enterprise-wide mandate, not a specialist silo. Targeted training ensures every user understands their role in upholding these guardrails.

Take a governed approach to AI enablement. “Governed enablement” assumes AI is already in use across the enterprise and defines where and how it can operate safely. It requires continuous monitoring and enforcement to ensure systems remain aligned with policy as usage and risks evolve. This is an ongoing discipline, not a one-time project.

The future of responsible AI starts now

AI is reshaping how organizations operate at a pace that cannot be ignored. The question is no longer whether to adopt it, but how to scale it safely. Organizations with strong governance scale without sacrificing trust. Those that delay widen the gap between threat and preparedness.

At Ivanti, we're committed to building AI governance that enables innovation while protecting what matters most — our people, our customers, and our operations. This is critical work and the time to act is now.

To learn more about the AI deployment gap and how leading organizations are closing it, explore Ivanti's 2026 State of Cybersecurity Report.

How Agentic AI for ITOps Unlocks Value at Scale

Mon, 05 Jan 2026 17:00:02 Z

Here’s a paradox for the AI era: organizations are obsessed with the promise of AI as the key to unlocking productivity and enterprise transformation, and IT teams are all-in on the advantages AI and automation offer — yet those same organizations are the ones holding that transformation back.

While the majority of IT workers advocate for AI adoption, operational, cultural and budgetary barriers stand in the way of enterprises implementing AI at scale. The result: most companies today have yet to operationalize breakthrough AI and automation use cases that deliver true business value.

That’s not to say; most companies aren’t using AI — but mainly for completing lower-level tasks and boosting individual productivity. While this is an important step, enterprises will need to think bigger and longer-term to see significant ROI and digital transformation from their AI investments.

Automation and outputs vs. agentic AI autonomy

IT teams are ahead of the curve when it comes to generative AI adoption. 84% of IT professionals use generative AI tools at work, according to a 2025 Ivanti research report. That same research shows that IT professionals are also overwhelmingly positive toward AI and automation:

83% expect AI to boost productivity in the next year.
70% say it will make their work more satisfying.

Yet, for all that progress, companies aren’t tapping into the deeper potential of AI-driven technology. Many teams have mastered task-level automation (ticket routing, password resets, log summarization), but few have embraced agentic AI, which goes beyond execution to autonomous reasoning and optimization.

That gap between automation and transformation mirrors a larger problem occurring across industries. While most organizations are already using AI in some capacity, only a few have seen measurable business impact from their AI tools. McKinsey has referred to this situation as “the gen AI paradox.”

This disconnect between AI applications and ROI is further validated by an MIT report published in August 2025, which finds that a staggering 95% of organizations found zero return on generative AI despite $30-40 billion worth of investment in it.

The reason for the problem is straightforward: generative AI creates. It produces content, automates tasks, and accelerates workflows. However, it doesn’t learn, reason, or adapt on its own. The next phase of enterprise AI will be driven by systems that can interpret, predict and act dynamically: what’s now being defined as agentic AI.

Ivanti’s research further underscores the fact that most companies today have yet to integrate more complex AI use cases into their IT workflows.

While 67% of organizations automate ticket routing, fewer than one-third apply AI to root-cause analysis or other predictive use cases. This signals that most teams are still prioritizing standard automation for tasks rather than enabling systems to think and act autonomously via agentic AI.

Standard automation and generative AI tools are frequently employed by IT teams to improve individual productivity and handle low-level, repetitive tasks at greater speeds than human intervention alone. The ability to boost efficiency with less time and resources is a critical advantage of AI — particularly for strained IT teams who are always being asked to accomplish more with less. But efficiency gains are only one piece of the puzzle.

Ivanti data also reveals a deeper organizational issue: nearly half of IT teams say their organizations lack resilience — the ability to adapt quickly to change, recover from disruption and maintain business continuity without excessive manual intervention.

True enterprise agility demands autonomous and adaptive AI solutions that can anticipate problems, reason through potential solutions and continuously learn how to deliver the most optimal outcomes.

This is where many leading organizations are turning to agentic AI. Autonomous AI agents offer teams the opportunity to reshape traditional IT operations from reactive automation to proactive, goal-driven collaboration. Realizing that transforming ITOps with AI means moving from isolated automation wins to system-wide intelligence.

High-impact agentic AI use cases in ITSM and ITOps

Agentic AI is already moving the needle by reducing downtime, cutting costs and improving organizational agility.

Five high-impact use cases for agentic AI in IT follow:

1. Autonomous incident remediation

Autonomous remediation is where AI begins to move from support to strategy. In traditional IT operations, identifying and fixing an issue can take hours of human triage and escalation.

With agentic AI, those same incidents can be detected, diagnosed and resolved in real time — often before users notice any disruption.

Agentic AI doesn’t not only detect anomalies within that IT space and diagnose the root cause — it intelligently devises solutions and then executes fixes without human intervention. Moreover, machine learning enables the AI to learn from previous incidents and use this experience to continually refine and improve response efforts going forward.

2. Proactive problem prevention

Proactive problem prevention happens when AI can anticipate rather than just react to potential tech problems. Instead of solving a known issue, agentic AI continuously monitors data patterns and detects early warning signals in the form of small deviations that might evolve into service disruptions or security issues.

In other words, it moves ITOps toward proactive prevention, monitoring leading indicators and addressing issues before they become significant.

3. End-to-end lifecycle management

Autonomous AI agents will provide a more comprehensive and effective approach to asset lifecycle management.

This lifecycle view of automation extends beyond individual tickets or incidents to touch every stage of IT operations.

From onboarding new devices to patching and decommissioning outdated infrastructure, agentic AI ensures systems remain secure, compliant and cost-effective.

It not only fixes issues but also detects, diagnoses, and optimizes IT systems from provisioning to retirement. It acts as a continuous improvement engine, learning from patterns across the IT ecosystem to proactively optimize resources, streamline updates and reduce the long-term burden on IT staff.

4. Dynamic change and release management

Dynamic change and release management is where agentic AI truly shows its orchestration capabilities. In most enterprises, change management remains a high-friction process, requiring coordination across multiple teams, tools and environments.

Agentic automation transforms this by allowing AI agents to collaborate on complex workflows including cybersecurity incident response and software deployment, working together with minimal human oversight to resolve incidents, provision resources and ensure compliance.

These agents act as intelligent coordinators: synchronizing updates across systems, validating configurations and rolling back changes automatically when anomalies occur. The result is faster, safer and more predictable change cycles that free IT teams to focus on innovation rather than resource-heavy firefighting.

5. Autonomous resource and capacity management

Resource and capacity management are some of the most critical — and often overlooked — dimensions of IT performance. Using AI, companies can anticipate future resource needs by analyzing historical usage trends, workload fluctuations, and demand surges. Agentic systems can automatically allocate compute power, storage and bandwidth before bottlenecks appear, maintaining optimal performance without constant human oversight.

Over time, these self-adjusting systems learn from operational data to continuously fine-tune capacity, reducing waste, minimizing costs and ensuring service continuity even during unexpected spikes.

Research from IBM’s report, Agentic AI’s strategic ascent, underscores this pace of change: by 2027, twice as many executives expect AI agents will make autonomous decisions in workflows. Today, only 24% of executives report that level of autonomy; within two years; 67% expect this to be the norm.

Overcoming barriers and delivering enterprise impact

Yet progress often stalls; not for lack of intent, but because of structural barriers. IT leaders must first overcome the obstacles that stand in the way of their evolution to value-focused work. These barriers are multifold: technical, cultural and operational.

Even AI-forward IT organizations can lack the structural readiness for deep automation. For example, Ivanti’s “2025 Technology at Work Report” found that:

38% of IT professionals point to complex tech stacks as an issue in effective IT operations.
And 72% say their IT and security data is siloed within their organization.

Building a sustainable AI strategy requires more than a positive attitude – it demands alignment between IT and the broader business. Successful organizations are the ones aligning technology goals with tangible outcomes, supported by clear data structures, unified processes and teams equipped to manage new AI-driven workflows. Without that alignment, even the best tools struggle to deliver enterprise-wide impact.

IBM research reveals another layer: 45% of executives cite a lack of visibility into AI decision-making as a major barrier. This AI “black box” problem isn’t just a technical one. It’s also about trust, clear communications and AI guardrails. Scaling agentic AI requires governance frameworks where automated decisions can be understood, audited and explained.

Such transformation must keep humans in focus: design for augmentation — not replacement.

The agentic AI operating model

Think of agentic ITOps as much more than an efficiency upgrade, but as a total reshaping of traditional IT workflows. Organizations leading the next phase of transformation are the ones rethinking how their systems make decisions, collaborate and adapt autonomously across the enterprise.

That level of digital transformation requires leadership from the top. CIOs and executive stakeholders must pivot from experimentation to execution. They must look at embedding agentic AI not as a side project, but as a core operating model that aligns technology, data and people toward shared outcomes.

That pivot marks the real test ahead.

The future ROI of agentic AI initiatives

Even with the productivity boost enjoyed with generative AI tech, IT teams don’t need more tools. They need intelligence that delivers measurable outcomes. Teams should establish before-and-after metrics that resonate with IT and business leaders. Beyond efficiency gains (time saved, faster resolution, lower costs), measure labor savings, fewer resource-intensive outages and reduced tool sprawl.

Transformational organizations see greater impact across every business metric, including productivity, efficiency, revenue growth, brand strength and customer loyalty, than those that limit AI to incremental and even superficial gains.

In fact, organizations that excel in three key AI adoption areas are 32 times more likely to achieve top-tier business performance, according to that same IBM report referenced earlier.

The bottom line

The next 12 to 24 months will test IT leaders’ ability to translate experimentation into sustained value. Those who embrace agentic AI early will build organizations that learn faster, adapt continuously, preempt potential issues and recover from disruption instinctively.

IT has regularly shown that they're fully willing and committed to AI adoption. Now it must lead again, in depth. Agentic AI marks the next maturity stage: self-learning, self-healing and self-optimizing systems that enable greater agility and resilience across the enterprise.

This isn’t “set and forget.” IT teams must build, train, monitor, measure and refine agentic AI to ensure value realization.

To learn more about the role of AI as a transformative tool for IT operations and breakthrough use cases for agentic AI and automation in IT, see Ivanti’s research report: “AI: The Future of ITSM Automation.”

AI-Driven IT Cost Management: Aligning Spend with Strategic Value

Wed, 22 Oct 2025 13:00:01 Z

Optimizing IT costs is now the top priority for IT teams, according to Ivanti’s 2025 Technology at Work Report. Our survey found that cost optimization was cited as a critical strategic goal for 2025, outranking improving cybersecurity, investing in infrastructure and deploying AI / ML technology.

However, despite many organizations viewing AI and automation in terms of short-term operational improvements, many organizations haven't yet tapped into AI’s full potential to transform how IT manages, predicts and optimizes costs.

It’s clear that IT teams are already bullish on AI's value in everyday operations. IT is currently leading the charge in AI implementation, with 36% of IT teams using generative AI, according to McKinsey research from March 2025 — more than any other function.

While IT teams regularly use AI for coding, support tickets and other day-to-day work, many organizations don’t see beyond these short-term efficiency gains. Too often IT leaders overlook AI's untapped potential for enterprise-wide optimization.

This shift from incremental productivity gains to strategic cost optimization frees resources and grants IT leaders the flexibility to focus on innovation, long-term transformation and consistent alignment of operations with fast-evolving business strategy.

With CIOs under mounting pressure to “do more with less,” AI-driven IT cost optimization has the power to reduce wasteful IT spending, justify investments and deliver strategic value across the entire organization.

However, before this transformation can happen, companies must first understand the biggest sources of inefficient spending and identify the most valuable areas where they can use AI capabilities to better allocate resources and make the most of their IT budget.

Major barriers to IT cost optimization

Cost management challenges are systemic. Inefficient IT spending results from compounding breakdowns across systems, processes and leadership structures. More than half of IT professionals said that wasteful IT spending was a significant problem at their organization, according to 2025 Ivanti research.

Wasteful IT spend is especially egregious in high-complexity sectors like telecoms and government, where legacy systems and sprawling vendor relationships make it difficult to track and control spend. These sectors face greater pressure to cut costs, as unchecked waste limits their ability to fund strategic initiatives and modernize critical infrastructure.

Monthly vendor reviews, quarterly budget reconciliations and annual contract negotiations struggle to keep pace with today's dynamic IT landscape. Organizational cloud spend can spike overnight, and tech complexity compounds rapidly due to decentralized procurement, shadow IT and poor asset visibility. For instance, 38% of IT professionals Ivanti surveyed pointed to “tech complexity” as a major barrier to effective IT operations.

Looking toward the future of AI-powered ITOps, many IT professionals recognize AI's transformative potential to meet these challenges and eliminate unnecessary spending.

AI and machine learning for predictive cost management

Manual cost reviews alone are backward-looking, labor-intensive and prone to human error. Human oversight alone cannot recognize and analyze patterns that emerge through large-scale AI data analysis. AI-powered cost management flips the script. Instead of reporting overspending after it happens, AI predictive analysis can detect potential anomalies and provide organizations with accurate budget forecasting to avoid redundant, unnecessary spending. AI can also surface redundant tools, highlight lifecycle costs and flag spending anomalies that human reviews would likely miss.

Consider this scenario: you can use AI to analyze your IT asset management data to uncover shelfware and unused software licenses, or to mine support tickets for patterns showing when certain device types typically begin to fail. Those insights can inform smarter replacement cycles, reduce downtime and ensure you only pay for what you actually use.

The speed difference is dramatic. Organizations using AI-driven cost optimization are seeing measurable reductions in operational expenses, while others are still compiling last quarter's reports on spreadsheets.

Real-time visibility consistently outperforms reactive reviews. When you can predict and prevent waste instead of just documenting it, that's when IT shifts from expense management to strategic value creation.

AI cloud cost management and resource management

Cloud environments are the single largest source of budget variance and waste. Nearly nine in 10 (88%) of organizations see significant variance between actual spend and forecasted spend, and only 30% know where their cloud budget is going, according to CloudZero’s State of Cloud Costs in 2024.

Ivanti’s own findings expand on these IT resource management challenges:

39% of IT professionals cited outdated hardware as a considerable source of wasteful spend.
Outdated, unused and expired software is also a major factor, with nearly one in three (31%) IT professionals reporting their organizations don't track unused or underused software licenses.
Even more concerning, 48% of IT teams reported that their companies use software that’s reached end of life (EOL).

The combination of multi-cloud environments, unpredictable usage over time, complex and multilateral pricing within the same services and drastically varied costs makes the need for AI and automation clear.

AI-enabled technology can help cut through this complexity. AI solutions can proactively identify underutilized software licenses, over-provisioned cloud resources and redundant applications across environments that'd take human analysts weeks to map.

What’s more, AI combined with ITSM automation can suggest resolutions to inefficient IT spending through intelligent recommendations for rightsizing and deprovisioning. Leading CIOs use strategic IT cost management to fund high-impact initiatives, and AI automation makes this achievable at scale.

Software license optimization delivers significant returns. AI tracks actual software usage against entitlements, identifies downgrade opportunities and predicts future needs based on growth patterns. Organizations pay for what they use, not what they purchased months ago.

AI’s impact on streamlining IT processes

While AI adoption increases among IT teams, its cost optimization impacts aren't yet as widespread as basic use cases like predictive maintenance and automating routine self-service tasks — both popular, according to Ivanti research.

Delayed widespread AI adoption in ITOps prevents organizations from reaping the long-term financial benefits and opportunities for strategic business growth. IT automation solutions can reduce operational costs by up to 90%. The technology handles ticket triage and automates incident response while predicting system failures and managing routine maintenance.

AI elevates IT operations, and the impacts are felt across the entire business. Asset performance monitoring becomes intelligent and proactive. Instead of static thresholds and scheduled checks, AI systems continuously analyze performance data, predict hardware failures and optimize maintenance schedules.

This prevents both downtime costs and unnecessary replacement spending. Process automation also frees IT time, reducing costs from human error: manual misconfigurations, missed patches and delayed responses caused by limited resources.

How AI empowers IT to deliver strategic value

IT cost management isn't the endgame. It's the starting point for strategic transformation. AI enables companies to optimize costs at scale while freeing up budgets for growth investments.

Organizations embracing AI for cost management are repositioning how their businesses view and leverage technology. When every investment must justify its existence, AI enables IT leaders to become architects of value, not just managers of expense by cutting costs and creating value. AI promotes agile resource allocation. When wasteful costs are eliminated proactively, IT executives gain flexibility to fund emerging opportunities and respond to changing business priorities.

Visibility is equally important in transforming IT into a strategic business driver. AI provides the data and insights needed to quantify IT's business value. When investments are tracked and tied to business outcomes, CIOs can demonstrate the true ROI of the IT department and secure strategic support.

The real transformation happens when IT moves from reactive expense control to proactive value creation. AI-optimized IT operations have become the foundation for innovation, enabling teams to experiment and deliver competitive advantages.

To learn more about the major challenges contributing to wasteful IT spending and how AI and automation can help IT teams boost efficiency, improve asset visibility and optimize IT cost management, see Ivanti’s 2025 Technology at Work Report.

AI threat hype: why chasing ghosts leaves real vulnerabilities exposed

Mon, 02 Jun 2025 13:00:03 Z

With AI at the center of media and industry focus, cybersecurity teams are increasingly putting pressure on themselves to prepare for AI-fueled cyber attacks.

According to Ivanti’s 2025 State of Cybersecurity research, half of IT security professionals ranked “yet unknown weaknesses” as a high or critical threat – the same as or higher than compromised credentials, supply chain risks, DDoS attacks and other real-world threats.

These “unknown” concerns remain more hype than substance for the moment. In fact, the Picus 2025 Red Report found no notable uptick in the use of AI-driven malware techniques in 2024. The report goes on to state that “AI enhances productivity but doesn't yet redefine malware.”

In other words, adversaries are leveraging AI in their attacks – automating phishing content, debugging malicious code, accelerating reconnaissance – but they’re not creating fundamentally new attack classes. Traditional attack techniques still dominate the cyber landscape, yet many teams remain fixated on speculative threats.

The big takeaway? Stop worrying about hypothetical AI-powered launches from beyond the front lines and focus on defending against the threats that are putting your organization most at risk today.

Real threats vs. AI hype: misaligned risk prioritization

Ever since ChatGPT started capturing headlines in November 2022, the world has been saturated with anticipation of the potential impact of AI. Analysts are projecting AI will inject $15.7 trillion into the global economy by 2030, and the tech is also increasingly shaping daily workflows across industries.

So, it’s no surprise that AI is dominating boardroom talk. Since CISOs and security leaders are tasked with preparing for worst-case scenarios, they’re understandably alert to what AI could do in the wrong hands. However, an issue arises when much of this attention is focused on novel AI-generated threats. Less focus is being paid to the main way that we’re already seeing attackers use AI — as a tool to amplify familiar existing threats.

AI synthetic digital content — such as manipulated media and deepfakes — and AI-based spoofing attacks — such as using gen AI to mimic someone’s voice and tone — were both top-ranked predicted AI-related threats for 2025, with 53% of security professionals rating their threat levels as “high / critical.”

Yet in practice, these AI-generated threat types are not yet as commonplace as traditional phishing techniques and ransomware attacks where we see AI speed up attackers’ efforts to an unprecedented rate. The result is a risk prioritization model that skews toward theoretical rather than real-life considerations.

Critical gaps in threat preparedness persist

Ivanti’s report also paints a troubling picture: real-world threats are outpacing organizational preparedness across multiple critical categories.

The problem isn’t a lack of awareness; rather, it’s that security hygiene is inconsistent. The gaps are still there: weak credential management, patch delays, untested incident plans and API / third-party blind spots. In short, new risks coming from AI aren’t the issue – it’s more that AI is strengthening existing threats.

Consider the top five areas in which our research found defenders are falling behind – not because security teams lack awareness, but rather, the fundamentals are being inconsistently enforced, and AI is helping adversaries exploit them more efficiently:

1. Ransomware attacks

Ransomware remains a top threat with 58% of security professionals ranking it as high/critical. Yet only 29% say their teams are prepared to defend themselves against ransomware attacks. Despite this, many organizations still lack tested backup and recovery protocols, haven’t segmented their networks effectively and run outdated IR plans.

When it comes to gen AI’s ability to ramp up ransomware threats, security teams may face even quicker code iteration, more automated vulnerability chaining and adaptive payload construction that can circumvent defenses. Attackers are also using AI to create virtual simulations for testing ahead of deployment – and adjusting their strategies accordingly.

2. API-related exposures

The increasing use of API-supported software has also increased the risk of API exposures. API-related vulnerabilities was the second highest ranked threat type by the security professionals Ivanti surveyed with 52% rating it a “high / critical” threat. Yet just 31% of security teams said they felt “very prepared” to defend against API attacks — a 21% preparedness gap.

Threat actors can now automate the discovery of endpoints through traffic analysis, reverse engineering poorly documented APIs and generating fuzzing inputs to identify logic flaws. Once they’re inside, they exploit permission creep, unvalidated input or weak auth mechanisms.

Still on the cybersecurity front, AI can also be a useful tool to analyze large amounts of API traffic data in real-time to identify issues and patterns that may indicate an attack.

3. Software vulnerabilities

Overall software vulnerabilities had a worrying 19-point gap between “high / critical” threat level and preparedness. Security teams still struggle with critical issues around departmental silos, inaccessible data and tech debt that make managing their expanding attack surface challenging. Ivanti’s report found that 45% of security teams said they lacked data to confidently identify specific vulnerabilities.

Furthermore, more than half of organizations surveyed — 51% — admitted to using software that has reached end-of- life and therefore would not be regularly updated and patched to ensure security compliance.

Security and IT teams are struggling to maintain patch hygiene, especially across legacy systems and shadow IT environments. This isn’t only a tooling problem. Patching cycles and security response times are slowed by security and IT having misaligned priorities – and AI is widening the gap between those who patch quickly and those who wait.

4. Compromised credentials

Like software vulnerabilities, the preparedness gap for compromised credentials also sits at 19%. Stolen credentials are an easy way in for attackers, even more so when paired with AI’s capabilities to scale credential stuffing attacks, simulate human-like login behavior and adapt to multi-step authentication flows.

Gen AI is also utilized to analyze leaked datasets and identify reused passwords or credential patterns across platforms. What was once a manual grind is now becoming a fully automated infiltration process. And yet, MFA coverage remains patchy, and identity governance is often an afterthought. AI is simply exposing how poorly it’s implemented.

5. Phishing

Phishing has long been a mainstay method for attackers to breach an organization’s defenses. Yet even now, only 37% of cybersecurity teams say they’re readily prepared to defend against phishing threats.

This lag has never been more of a concern as we’ve seen how phishing methods evolve with AI and attackers have the capabilities to create convincing deepfake content and personalize attacks using scraped public data.

Organizations clearly have a lot of work to do to bridge the gaps in their defenses and combat prominent threats because we know that these threats are not going away but continuing to evolve and develop. However, there is some positive news behind the AI threat hype which is that AI and automation can also serve as powerful tools to bolster cybersecurity teams' efforts as well.

AI cybersecurity tools: foe or friend?

Exploring the other side of the AI conversation, cybersecurity leaders have begun to view AI as an asset in bolstering defenses. Ivanti’s 2024 report “Gen AI and Cybersecurity: Risk and Reward” found that 90% of security professionals believe that gen AI benefits security teams as much or more than threat actors.

Some ways organizations today use AI tools include automating threat detection and triage, accelerating log analysis, increasing anomaly spotting and simulating different attack methods. Gen AI does not need to be only a looming threat but should be seen as another tool to help security teams more effectively identify weaknesses in their defenses and proactively address vulnerabilities.

Recommendations to refocus on cybersecurity fundamentals

AI threats are of course real threats, and cybersecurity teams shouldn’t disregard the growing use of AI in cyber attacks. However, with cybersecurity and IT teams dealing with a lack of talent / skills and battling burnout, they need to prioritize security resources on the most prominent and impactful types of threats.

Rather than spend time, budget and personnel crafting a security strategy around speculative AI threats, defenders should be doubling down on security best practices and fundamentals such as:

Identifying attack surface gaps
Remediating existing exposures
Ensuring rigorous credential protection and access controls

Your vulnerability and threat management strategy needs to focus primarily on proactively identifying and managing the most pressing threats to your organization rather than on trying to guard against speculated future threats, especially when unknown.

Security teams can’t know the threat level of abstract unidentified threats and thus can’t act on them. It’s more beneficial to gain a comprehensive, real-time view of your attack surface and use that to understand your organization’s current risk posture.

Real cybersecurity leadership isn’t about chasing hypotheticals. Rather, it’s about systematically reducing exposure through full visibility, quick validation and risk-based prioritization.

Here’s what today’s leading security leaders are doing, and what you should do to stay ahead:

1. Continuously monitor your complete attack surface

Teams can’t protect what they’re not able to see. As such, ensure a real-time, continuously updating view of every exposure point to have visibility into the entire landscape. With limited resources, you cannot prepare for every unknown potential threat. Instead, teams need a framework for assessing their attack surface and classifying known and unknown vulnerabilities.

2. Prioritize threat response based on overall impact threat and risk to business

Go beyond scanning for existing gaps and opportunities – run simulations, use red teams and adopt behavior-based analytics to validate which exposures are actually exploitable. Leverage frameworks like MITRE ATT&CK, threat intelligence and your own readings to identify exposures and prioritize remediation efforts based on an established risk framework that considers critical factors such as exploitability and overall impact to your organization’s wider business objectives.

3. Leverage AI to accelerate defenses

AI is a threat vector, but it’s also a force multiplier for cybersecurity teams. Use AI in cybersecurity to its fullest potential, surfacing anomalies in real time, automating log analyses, generating simulations and reducing manual obligation. The hype is real, but especially in the way you apply it in such a way that it speeds up and tightens response.

Real cybersecurity leadership is forged in present-day focus and near-future developments. This means making surgical decisions under pressure, identifying actual risk and driving measurable reduction.

Forget the future unknowns – focus on what’s known

Research reveals that many security teams are overestimating the predicted risks of AI-powered threats, and they’re underprepared to defend against the actual threats targeting them today. Cybersecurity teams need to realign cyber defense strategies to reflect reality or risk unnecessary damages.

The truth is, AI may redefine the scale and speed of cyber attacks, but even with new AI capabilities, today’s attackers are still using the same old tricks. The winning cybersecurity strategies won’t put all of their time and resources into building AI-resistant walls – they’ll be the ones proactively readying defenses to fend off the threats right in front of them.

Check out Ivanti’s 2025 Cybersecurity Report to benchmark your readiness and close the preparedness gap.

AI Cybersecurity Best Practices: Meeting a Double-Edged Challenge

Thu, 17 Oct 2024 12:28:03 Z

Artificial intelligence is already showing its potential to reshape nearly every aspect of cybersecurity – for good and bad.

If anything represents the proverbial double-edged sword, it might be AI: It can act as a formidable tool in creating robust cybersecurity defenses or can dangerously compromise them if weaponized.

Why is AI security important?

It’s incumbent upon organizations to understand both the promise and problems associated with AI cybersecurity because of the ubiquity of all iterations of AI in global business. Its use by bad actors is already a source of concern.

According to McKinsey, AI adoption by organizations surged to 72% in 2024, up from about 50% in prior years across multiple regions and industries. But the intricate nature and vast data requirements of AI systems also make them prime targets for cyber-attacks. For instance, input data for AI systems can be slyly manipulated in adversarial attacks to produce incorrect or damaging outputs.

A compromised AI can lead to catastrophic consequences, including data breaches, financial loss, reputational damage and even physical harm. The prospect for misuse is immense, underscoring the critical need for robust AI security measures.

Research by the World Economic Forum found that almost half of executives worry most about how AI will raise the risk level from threats like phishing. Ivanti’s 2024 cybersecurity report confirmed those concerns.

Despite the risks, the same Ivanti report found that IT and Security professionals are largely optimistic about the impact of AI cybersecurity. Almost half (46%) feel it’s a net positive, while 44% think its impact will be neither positive nor negative.

Potential AI cyber threats

AI introduces new attack vectors that require specific defenses. Examples include:

Site hacking: Researchers have found OpenAI’s large language model can be repurposed as an AI hacking agent capable of autonomously attacking websites. Cyber crooks don’t need hacking skills, only the ability to properly prompt the AI into doing their dirty work.
Data poisoning: Attackers can manipulate the data used to train AI models, so they malfunction. This could involve injecting fake data points that influence the model to learn incorrect patterns or prioritizing non-existent threats, or subtly modifying existing data points to bias the AI model toward outcomes that benefit the attacker.
Evasion techniques: AI could be used to develop techniques that evade detection by security systems, such as creating emails or malware that don't look suspicious to humans but trigger vulnerabilities or bypass security filters.
Advanced social engineering: Since it can analyze large datasets, an AI can identify targets based on certain criteria, such as vulnerable past behaviors or susceptibility to certain scams. Then, it can automate and personalize an attack using relevant information scraped from social media profiles or prior interactions so it’s more believable and likely to fool the recipient. Plus, generative AI can draft phishing messages without grammar or usage mistakes to look legitimate.
Denial-of-service (DoS) attacks: AI can be used to orchestrate large-scale DoS attacks that are more difficult to defend against. By analyzing network configurations, it can detect vulnerabilities then manage botnets more effectively as it tries to overwhelm a system with traffic.
Deepfakes: AI can generate convincing visual or sonic imitations of people for impersonation attacks. For example, it could mimic the voice of a high-level executive to trick employees into wiring money to fraudulent accounts, sharing sensitive information like passwords or access codes or approving unauthorized invoices or transactions. If a company uses voice recognition in its security systems, a well-crafted deepfake might fool these safeguards and access secure areas or data. One Hong Kong company was robbed of $26 million via a deepfake scam.

A “soft” threat presented by AI is complacency. There's always a risk of over-reliance on AI systems, which might lead to laxity in monitoring and updating them. One of the most important measures for protecting an enterprise from AI issues is through continuous training and monitoring, whether AI is being deployed in cybersecurity or other operations. Ensuring that AI operates with the organization's best interests in mind demands ongoing vigilance.

Watch: Generative AI for InfoSec & Hackers: What Security Teams Need to Know

AI cybersecurity benefits

AI cybersecurity solutions deliver the most significant value to an organization in the following ways:

Enhanced threat detection

AI excels at identifying patterns in vast datasets to detect anomalies indicative of cyber-attacks with unprecedented accuracy. While human analysts would be overwhelmed by the volume of data or alerts, AI improves early detection and response.

Improved incident response

AI can automate routine incident response tasks, accelerating response times and minimizing human error. By analyzing past incidents, AI can also predict potential attack vectors so organizations can strengthen defenses.

Risk assessment and prioritization

AI can evaluate an organization's security posture, identifying vulnerabilities and prioritizing remediation efforts based on risk levels. This helps optimize resource allocation and focus on critical areas.

Security considerations for different types of AI

Security challenges associated with AI vary depending on the type being deployed.

If a company is using generative AI, the focus should be on protecting training data, preventing model poisoning and safeguarding intellectual property.

In the case of weak (or “narrow”) AI such as customer support chatbots, recommendation systems (like Netflix), image-recognition software, assembly line and surgical robots, the organization should prioritize data security, adversarial robustness and explainability.

Autonomous “strong” AI (aka Artificial General Intelligence) is a work in progress that doesn’t yet exist. But if it arrives, companies should focus on defending control mechanisms and addressing existential risks and ethical implications.

Watch: How to Transform IT Service Management with Generative AI

Latest developments in AI cybersecurity

The rapid evolution of AI is driving corresponding advances in AI cybersecurity that include:

Generative AI threat modeling: AI cybersecurity tools can simulate attack scenarios to help organizations find and fix vulnerabilities proactively.
AI-powered threat hunting: AI can analyze network traffic and system logs to detect malicious activity and potential threats.
Automated incident response: AI cybersecurity solutions can automate routine incident response tasks like isolating compromised systems and containing threats.
AI for vulnerability assessment: Can analyze software code to find possible vulnerabilities so developers can build more secure applications.

AI cybersecurity courses

Investing in AI cybersecurity education is crucial for building a workforce that understands how to use these tools. Numerous online platforms and universities offer courses covering various aspects of AI security, from foundational knowledge to advanced topics.

Top cybersecurity solution providers will offer a wide range of courses and training to give your team the skills it needs to get the most out of your platform.

AI cybersecurity best practices

Implementing a comprehensive strategy for putting AI into action for cybersecurity is essential.

1. Set out data governance and privacy policies

Early in the adoption process, establish robust data governance policies that cover data anonymization, encryption and more. Include all relevant stakeholders in this process.

2. Mandate AI transparency

Develop or license AI models that can provide clear explanations for their decisions, rather than using “black box” models. This is so security professionals can understand how the AI arrives at its conclusions and identify potential biases or errors. These "glass box” models are provided by Fiddler AI, DarwinAI, H2O.ai and IBM Watson tools such as AI Fairness 360 and AI Explainability 360.

3. Stress strong data management

AI models rely on the quality of data used for training. Ensure you're using diverse, accurate and up-to-date data so your AI can learn and identify threats effectively.
Impose robust security measures to protect the data used in training and operating an AI model, as some may be sensitive. Any breaches could expose it, compromise AI effectiveness or introduce vulnerabilities.
Be mindful of potential biases in your training data. Biases can lead the AI to prioritize certain types of threats or overlook others. Regularly monitor and mitigate bias to ensure your AI is making objective decisions.

Learn about: The Importance of Accurate Data to Get the Most From AI

4. Give AI models adversarial training

Expose AI models to malicious inputs during the training phase so they’re able to recognize and counteract adversarial attacks like data poisoning.

5. Implement continuous monitoring

Conduct continuous monitoring and threat detection systems to identify bias and performance degradation.
Use anomaly detection systems to identify unusual behavior in your AI models or network traffic patterns to detect potential AI attacks that try to manipulate data or exploit vulnerabilities.
Regularly retrain your AI cybersecurity models with fresh data and update algorithms to ensure they stay effective against evolving threats.

6. Keep humans in the loop

AI is not infallible. Maintain human oversight, with security professionals reviewing and validating AI outputs to catch potential AI biases, false positives or manipulated results the AI might generate.

7. Conduct regular testing and auditing

Routinely assess your AI models for vulnerabilities. Like any software, AI cybersecurity products can have weaknesses attackers might exploit. Patching them promptly is crucial.
AI models can generate false positives, identifying non-existent threats. Adopt strategies to minimize false positives and avoid overwhelming security teams with irrelevant alerts.
Conduct frequent security testing of your AI models to identify weaknesses that attackers might exploit. Penetration testing expressly designed for AI systems can be very valuable.

8. Have an incident response plan

Create a comprehensive incident response plan to effectively address AI-related security incidents.

9. Emphasize employee training

Educate employees about the risks associated with AI and how social engineering tactics might be used to manipulate them into compromising AI systems or data security.
Conduct red-teaming exercises that simulate AI-powered attacks, which help test your security posture and spot weaknesses attackers might exploit.
Collaborate with industry experts and security researchers to stay abreast of the latest AI threats and best practices for countering them.

10. Institute third-party AI risk management

Carefully evaluate the security practices of third-party AI providers. Do they share data with other parties or use public datasets? Do they follow Secure by Design principles?

11. Other best practices

Integrate your AI solution with threat intelligence feeds so it can incorporate real-time threat data and stay ahead of new attack vectors.
Ensure your AI solution complies with relevant industry standards and regulations. This is mandatory in certain sectors. For instance, in the automotive and manufacturing sectors, an AI must comply with ISO 26262 for automotive functional safety, General Data Protection Regulation (GDPR) for data privacy and National Institute of Standards and Technology guidance. AI in healthcare must comply with the Health Insurance Portability and Accountability Act in the U.S., GDPR in Europe and FDA regulations for AI-based medical devices.
Track metrics like threat detection rates, false positives and response times. This way, you’ll know the effectiveness of your AI and areas for improvement.

Win by being balanced

For any organization venturing into this bold new AI cybersecurity frontier, the way forward is a balanced approach. Leverage the copious strengths of AI – but remain vigilant as to its limitations and potential vulnerabilities.

Like any technology, AI is not inherently good or bad; it is used by both good and bad actors. Always remember to treat AI like any other tool: Respect it for what it can do to help but stay wary of what it can do to harm.

Read: Ivanti’s Position on Artificial Intelligence

The Importance of Accurate Data to Get the Most From AI

Wed, 19 Jun 2024 13:00:00 Z

The IT service community is rightly buzzing about the wealth of possibilities AI offers to automate service management operations and better assist customers — especially in this age of expanding Everywhere Work. But without sound data, the path to leveraging AI for IT service management becomes quite daunting.

With so many more people working outside a traditional office setting, more end users than ever are logging in more often to access employee services. According to Ivanti research, all this complexity affects IT significantly: 39% of IT professionals report too many logins, 47% report too many digital notifications and 42% report using too many tools/platforms.

So AI-driven service management is a welcome tool to help IT personnel serve their end users. Sound data for AI is the bedrock of such a solution.

Organizations thrive on accurate data for AI. Without it, your service teams can only be reactive. That means you have manual processes in the organization — and they are taking a ton of time away from people who should be focusing on outcomes within that organization.

Not only that, but poor data means your service resolutions are inaccurate because the information you have doesn’t match up to the right resolution. The result? Users are unable to get back to work because of slower ticket resolution. Mismatched data is also a hazard when information is transferred from one specialist to another within an organization.

Good data, bad data, no data

Data is the fuel on which AI runs. A data-savvy organization uses data to define a problem and ensure that it goes to the right process and speeds your resolution.
Conversely, an organization without well-managed data for AI has tons of blind spots. Not only does that increase that organization’s security risk, but it also drives up the cost of the service desk. Manual processes are costly; they require human input, and you have no way to control you spend. That type of organization also probably doesn’t know what software or hardware it has and is likely going to overspend there as well.

In IT operations especially, accurate and complete data for AI is critical, because AI requires significant training. If you don't have the right data, your AI is not going to give you the right answers. And those data needs are continuous as well. It's not enough to have a single data source feeding your AI. You constantly have to feed it with the right data so it can detect changes over time and react appropriately and differently.

Conflicting data within some data sources is another challenge you must resolve. As you bring data into a single data pool that you can learn from, you have to ensure that that data is reconciled.

Ultimately, all these hurdles slow digital transformation within the organization. Visibility into and accuracy of the data is vital to the success of any AI investment. AI needs to be trusted. If it's making recommendations and predictions — even suggestions about how you resolve issues — trust in those outputs is vital. If you leave this unaddressed — if the accuracy of your data for AI is not correct and the predictions are not correct — it's really going to impact your return on investment and hinder digital transformation in your organization.

The first 4 steps to implementing AI

If you are considering how to leverage AI for service management in your organization, you must address some critical initial steps:

Eliminate blind spots: Remove the data gaps within your company by getting better visibility into your data assets, then monitoring those assets. Collecting and understanding the data that drives AI is a continual process — this isn’t a one-time deal. You have to continually ensure that you have the necessary data feeds coming into your AI solutions.
Remove data silos: To achieve more powerful outcomes from the data you acquire, you should not have just a single view of an asset. For a fuller, more rounded view of an asset, you must combine your data silos. Those include your asset inventory, asset management software inventory, data center inventory service maps, tickets, information, knowledge bases and monitoring logs. Commonality exists among all these types of data — which is one of the reasons organizations are turning to the power of platforms that provide data seamlessly across different applications.
Protect privacy: Ensure that your data doesn't include sensitive or personal information in it, as that can be baked into the predictions and outcomes your AI delivers.
Create data governance: Many organizations are crafting governance frameworks, policies and processes to set a standard and guidelines for how they use data — and they’re including AI data governance in that process.

AI Knowledge Management: How to Use Generative AI for Knowledge Bases

Mon, 10 Jun 2024 20:33:57 Z

Interest in generative AI has skyrocketed since the release of tools like ChatGPT, Google Gemini, Microsoft Copilot and others. Along with the hype comes concerns about privacy, personal identifiable information (PII), security and accuracy.

Organizations are treading cautiously with generative AI tools despite seeing them as a game changer. Many seek the “sweet spot” – enabling benefits right now while identifying more strategic future uses, all without compromising security.

One area in which gains can be immediate: Knowledge management, which has traditionally been challenging for many organizations. However, AI-based knowledge management can deliver outstanding benefits – especially for IT teams mired in manually maintaining knowledge bases.

How generative AI and knowledge management intersect

Generative AI refers to a type of artificial intelligence that can create new content, such as images, video, text or music, based on existing data. It uses machine learning algorithms to analyze and learn from large datasets, then uses that to generate new content.

Knowledge management is the process of capturing, organizing and sharing knowledge within an organization. It involves collecting information from various sources, storing it in a centralized database and making it easily accessible to employees as needed.

Many organizations carry out knowledge management manually, resulting in out-of-date or poorly written content. By automating knowledge management processes, generative AI can improve their efficiency and effectiveness.

Generative AI knowledge management benefits

Here are some of the specific ways generative AI can achieve these ends:

Automate creation of knowledge articles

Generative AI can automatically draft knowledge articles from existing data sources like product documentation, customer support tickets and employee training materials. This frees IT professionals for more strategic tasks, such as developing new knowledge management initiatives and improving existing articles.

With 56% of IT workers saying that helpdesk ticket volume is up, and 78% blaming hybrid/remote work for the jump, enhancing a knowledge base can enable quicker and more effective issue resolution and free teams to address more strategic tasks.

Create more personalized and engaging content

Generative AI can personalize content for each user to improve his or her experience. Knowledge articles, particularly for HR, can be personalized by region or language. Being able to generate content unique to an employee persona will enhance that employee’s usage and experience.

Improve the quality of knowledge

Generative AI can identify and correct errors, add context and additional information to knowledge articles and archive outdated information. So, employees will only access accurate and up-to-date information.

Generate new ideas and insights

Generative AI can combine existing knowledge in new ways. For example, HR, Facilities and IT might all have articles about onboarding and offboarding employees. Generative AI can use these to produce a merged knowledge article that covers all three departments, so an employee won’t have to search through multiple articles.

Solve problems quicker

Generative AI can rapidly solve problems by identifying data patterns to help improve decision-making and performance. For instance, it might examine IT incidents over a given period and identify a common resolution for similar issues. It can then generate a knowledge article for service desk agents on how to expedite resolutions, or for employees on how to do so via self-service.

Improve search accuracy

Generative AI can improve search accuracy by personalizing knowledge delivery based on each employee’s needs and preferences. Since an average worker spends 3.6 hours a day searching for information, any time savings here is a win that enhances their digital experience.

Enhance automation

Generative AI can help automate routine tasks, even those not directly related to creating knowledge management articles. Spiraling workloads are why 92% of IT workers see automation as “necessary” or “very necessary,” so identifying new ways of streamlining processes can free them from routine tasks to tackle other work while cutting costs.

Generative AI knowledge management drawbacks

Generative AI for knowledge management has the potential to revolutionize many industries and fields – but it’s not without issues that are important to address in implementation.

Security and privacy

AI knowledge management systems might contain sensitive or confidential information, so it's crucial to ensure they’re secured against cyberthreats. Also, there may be privacy concerns if the AI is generating content using personal or identifying information.

Quality and accuracy

While generative AI models can produce impressive outputs, quality and accuracy can vary widely depending on the quality of input data and task complexity.

Risk of misinformation

Generative AI can potentially produce incorrect or misleading information, leading to serious consequences for IT – for example, by introducing malware or incorrectly recommending turning off functionality used to secure the IT environment against malicious actors. Say a user is trying to install a printer driver and asks AI for help. Among its instructions, AI might tell the user to disable antivirus software or a firewall, providing a window for malware to be installed.

Dependence on AI-generated content

Companies that are too reliant on this may not prioritize human-generated content or critical thinking skills, potentially leading to lost expertise. Human oversight is still essential in validating and approving AI-generated outputs.

Data bias

Generative AI models can inadvertently reflect biases present in their training data, leading to skewed or inaccurate results. This can be an issue in knowledge management, where accuracy is critical. If an AI knowledge management model is trained on data predominantly from the United States, for instance, it may generate outputs less relevant to people in other countries.

Ethical concerns

These include potential bias in AI knowledge management training data, perpetuating existing inequalities.

Get the Interactive Report: AITSM: How AI is Redefining IT Service Desk Automation

Making AI knowledge management work

Despite the concerns, generative AI knowledge management can be a powerful tool. By carefully considering potential drawbacks and taking steps to mitigate them, organizations can make good use of generative AI in knowledge management.

Here are five things to consider when deploying AI knowledge management:

Identify data types used to train the generative AI model: Identification of data types will help to ensure that the data used is accurate and reliable. Are you going to use existing knowledge articles, incident data, problem data or a combination of types?

Ensure identified data is accurate, complete and up to date: Generative AI is only as good as the data it's trained on; “garbage in, garbage out” still applies.

Monitor the output of your generative AI knowledge management model: Check it for bias, misinformation, completeness and accuracy to ensure the information being generated is reliable.

Develop policies and procedures to manage risks: This is crucial to address issues such as data security, privacy, and ethical considerations in using generative AI for knowledge management.

Put an approval process in place: Review and authorization must happen before any generated outputs are shared publicly.

Implementation tips for generative AI knowledge management

There’s no doubt that generative AI knowledge management can be a valuable tool – or that there’s still much to be learned about both its benefits and possible pitfalls.

An organization must evaluate the potential impacts and pick an AI knowledge management solution that satisfies its distinct needs for privacy, accuracy, and security.

That said, there are a few more best practices to support successful adoption:

Start small and scale up: It's best to start with a small generative AI knowledge management pilot project, then scale up as you gain experience.

Get buy-in from stakeholders: It's important to get stakeholder buy-in before deploying generative AI to assure it's used effectively and its outputs are trusted.

Continuously improve the model: It's vital to continuously enhance your AI knowledge management model by retraining it on new data and addressing any potential problems that might crop up.

Create Personalized Knowledge Articles Faster and Smarter with Gen AI

Wed, 29 May 2024 13:00:00 Z

Maintaining a deep catalog of knowledge articles to inform end users is an essential — yet often tedious — requirement for IT teams. But generative AI is offering a big assist.

By using AI in knowledge bases, service desk agents who used to spend two or three hours creating each knowledge article from scratch can create drafts in minutes. And not only is today’s AI faster, but it also lets you personalize the knowledge you share — including translating your articles for a global workforce.

Now, you can tailor the knowledge you share based on context — on the persona the system is interacting with — to serve unique needs and preferences.

Why is this more important than ever? Because according to Ivanti research, more than three in four IT professionals say work stress is affecting their physical or mental health, and 68% say they feel burned out by their work. These mental stressors can drive up turnover and put pressure on productivity.

Removing some of the manual tasks previously required to manage your knowledge materials relieves a great burden from IT support personnel — while also improving the overall employee experience. A purpose-built knowledge management AI can automatically update an organization's knowledge base using existing helpdesk data, then turn the knowledge base into a helpful copilot for IT analysts, arming them with relevant information to handle higher-order tasks.

Multimodal models change the game

Early large language models (LLMs) were focused more on processing, meaning they were confined to solving linguistic tasks. New multimodal models can process much more — including audio and video.

Gen AI can keenly analyze user behavior to provide personalized support and smart suggestions, for instance:

Multiple responses can be generated from a single document to personalize messages for diverse audiences and channels.
By training gen AI with data from use cases across your company, it can then apply LLM to interpret queries from your end users, establish the best knowledge base match and retrieve the most relevant information.

Users expect everything they need to be available at their fingertips on their channel of choice. For instance, users might rely on a mobile communication application like Teams or Slack. However your users communicate, they expect the information to be presented on their channel of choice in a way they can quickly absorb.

Say an IT team must troubleshoot an issue flagged by a digital assistant, a chatbot or a copilot. AI can support an intelligent ticket routing solution so the IT helpdesk can respond faster. And, when the ticket progresses to the next level, the AI solution can help automatically triage the ticket for resolution.

The three R’s guide AI for knowledge management

Guiding the lifecycle management of knowledge articles using AI can be achieved easily by following these three principles, which I call the three R’s: relevance, revision, and retirement.

Relevance: AI helps serve articles to users based on preference and usage and can recommend articles based on what is relevant for the user.
Revision: AI can provide information about articles that are not being used much but could become more useful if the content is tweaked — and then be used to revise those articles.
Retirement: AI can be used to indicate which articles simply need to be removed from circulation.

Five steps to writing knowledge articles with AI

Now that you know how AI can help your IT service desk automate the creation of knowledge articles, here's how I would approach creating a workflow to implement that capability.

Identify specific challenges and opportunities you want to address. Focus on your key pain points. You can start with a data-gathering brainstorming session that involves multiple departments in your organization all looking at your existing system.
Define clear goals and expectations for integrating AI into your knowledge management, such as increasing adoption of knowledge articles and improving productivity. Are you, for instance, trying to increase the number of knowledge articles in your database, or refresh a set number of knowledge articles while removing a specific number of outdated articles?
Choose the right AI technology for your knowledge system. For instance, you might introduce natural language processing, the semantic search vector DB, machine learning for predictive analytics, automatic recommendations or a chatbot that integrates well with your knowledge article solution.
Data security and access control. Make sure the data you provide is the most relevant and the most accurate and is presented in a well-structured manner to the AI. At the same time — since it is gathering data from different sources — it is vital to protect sensitive information and ensure you are complying with data privacy and other regulations. Ensure you put proper entitlement in place so users access only the information that they are allowed to view.
Finally, it comes down to execution. Start small by looking to solve a minimum sellable use case — a pressing pain point. Then, try that with your initial set of users in a phased rollout. Try to fail fast. If something doesn’t work, iterate through it. Gradually, you will be able to expand your user base to all parts of the business, then apply continuous monitoring.

Once you’re running comfortably, that’s when you’ll start seeking feedback from users. As you start seeing improvements in user experience and productivity, you’ll know you’re on the right track. Just keep iterating through that process, and you will see your efficiency take off.

Read more in our report “How AI is redefining IT service desk automation”

COVID-19's lasting effect on cybersecurity and what to expect in the new year

Tue, 29 Dec 2020 16:52:02 Z

As 2020 comes to an end, it’s normal to reflect upon the year’s challenges, innovations, and achievements. When we look back on 2020, the hardships we all endured collectively across the globe seem to stack higher than other recent years. A pandemic swept across the world, rapidly shifting the ways we communicate, work, and connect with others. And hacks, scams, and ransomware attacks were at an all-time high.

Although this past year presented many challenges, there were some silver linings. At Ivanti, we recently acquired MobileIron and Pulse Secure, solidifying our position as a market leader in in Unified Endpoint Management, Zero Trust Security, and IT Service Management.

Here's a roundup from our experts on what to expect in 2021. With employees increasing working from anywhere – driving an explosive growth of endpoints, edge devices and data – and cybersecurity threats reaching catastrophic new heights, organizations must prepare for another busy year.

The Everywhere Enterprise is here to stay

“The enterprise is now everywhere - data resides everywhere, work takes place everywhere, and employees communicate with customers everywhere. People will continue working remotely from anywhere, not necessarily the office. We won't see 100% of companies go back to the office in 2021, and we also won't see 100% stay remote. Employees will have the autonomy to decide where they want to work — and enterprises will need to take the security measures needed to make it happen. The 2021 working persona will be someone who works on the go using a range of mobile devices, such as tablets and phones. The stagnant desktop employee will no longer be a reality.” – Bill Harrod, VP of Public Sector
“A hybrid work environment will be considered the gold standard and we will continue to see hybrid work environments leverage remote workers with on-site sessions to help drive innovation and employee mental health” - Rex McMillan, Senior Director, Product Management
“The world has been permanently changed by the COVID breakout in 2020 and the ripples will continue throughout 2021. Lockdowns, working from home and remote learning for school will continue well into next year until a vaccine is largely distributed and immunity grows. Activity in this highly disbursed/distributed computing environment will continue to grow for both good and bad. Productivity and social media software will flourish, filling the gap to keep people connected; the explosion of Zoom, WebEx, Chime and others were just the tip of the iceberg from 2020. IT and security operations have been slowly adjusting their tools and processes but will rapidly accelerate in both areas in 2021.” - Todd Schell, Senior Product Manager

Hybrid cloud environments will be a prime target for hackers

“The world has been shifting away from traditional networks to hybrid cloud-prem and device agnostic, an existing trend that was accelerated by COVID-19. Since COVID, perimeters have changed more drastically. Threat actors will continue to target remote workers. Remote Desktop Protocol (RDP) and Virtual Private Network (VPN) technologies alone will leave companies exposed. To regain control and provide more fluid perimeters to securely control who has access to applications and data, companies will turn to Zero Trust Access Control.” - Chris Goettl, Senior Director Product Management
“The growing adoption of cloud services combined with the increased use of mobile devices and laptops amid COVID-19 will make unsecured cloud users a prime target. Many companies use identity access management to combat potential security breaches, but the data part often goes unencrypted. We should expect to see hackers put a lot more emphasis on attacking web applications sitting on the cloud and applications that are distributed in nature. These actions will put a lot of pressure on information security groups, making it even more critical for companies to ensure there are no gaps or silos in their security strategy.” - Mike Riemer, Global CTO
“In the new WFH era, we're constantly working on the go using a range of mobile devices, such as tablets and phones, relying on public Wi-Fi networks, remote collaboration tools, and cloud suites for work. As we settle into a new year of this reality, mobile workers will be the biggest security risk as they view IT security as a hindrance to productivity and believe that IT security compromises personal privacy.” Bill Harrod, VP of Public Sector

Health institutions will be the most targeted group for ransomware attacks

“Generally, as a vertical becomes more important, prominent attackers leverage it as their greatest opportunity — so it is no secret that hospitals will be a huge target for ransomware attacks in 2021. With increasing use of tele-medicine, sensitive patient data is flowing throughout modern mobile endpoints and legacy infrastructures 24/7, health institutions will be a hacker's dream.” Bill Harrod, VP of Public Sector
“2020 saw the early threat actor forays with the expansion of ransomware riding on the desire of people to learn more about COVID. 2021 will have at least one major security breach with worldwide impact and will make the news across the globe.” - Todd Schell, Senior Product Manager
“Security will become a priority in crisis management. Cyberattacks against healthcare organizations spiked during the pandemic. As organizations (not just in healthcare) assess lessons learned in the aftermath, disaster recovery and crisis management plans will elevate security measures that could compromise systems in future crises.” - Rob DeStefano, Senior Product Marketing Manager

Automation will be the key to a successful cybersecurity program

“Amid the growing cybersecurity skills gap, the broader theme in 2021 will be the increased adoption of technology that capitalizes on artificial intelligence and machine learning to automate key security functions. COVID-19 resulted in a massive, global shift to a remote workforce. However, next year we will enter a completely new normal when we start to see more workers return to the office while others, who are not yet able or willing to make the transition, remain home. This will result in a split that forces IT departments to handle the demands of both full-scale on-premise and full-scale remote access. The only way to be efficient in the new world of work will be to utilize solutions with automation capabilities instead of relying solely on in-house security teams. Companies will turn to newer technologies such as Zero Trust Network Access and Artificial Intelligence Markup Language (AIML) techniques to work smarter in leveraging their workforce.” - Mike Riemer, Global CTO
“The needed move to modern management has been slow and cautious, but adoption of InTune, Workspace One, Ivanti Neurons and similar tools will pick up pace to handle all the remote devices. With the introduction of new software players and evolving security management processes, the door has opened wider for exploitation.” - Todd Schell, Senior Product Manager

Delivering contextualized, personalized, consumer-like experiences in the enterprise will be crucial

“Experience-centric service management will become a priority. Monitoring of employee productivity and IT ‘experience’ will increase in importance. The days of ‘IT Satisfaction Surveys’ are numbered, as the technology itself can help IT understand the real individual effectiveness of IT.” - Simon Geddes, Senior Director, Product Management
“In 2021, advances in AI and machine learning will allow devices to self-heal and self-secure by as much as 80%, allowing IT to set policies and know their devices and data are secure. Not only will this mean that IT can focus on transforming their business to be more competitive in their market, users can expect to receive a more ambient, personalized device experience where they can remain productive regardless of where they are working or the device they use.” - Alan Braithwaite, Senior Director, Product Management

2021 is the year when Ivanti, MobileIron, and Pulse Secure become better together as Ivanti. It’s the year that we enable organizations to proactively and autonomously self-heal, self-secure, and self-service devices in the everywhere enterprise. Security will become a priority in crisis management. Most importantly, our customers will collaborate and innovate more freely, while reducing the risk of data breaches and enhancing employee experiences. We look forward to helping our customers achieve bigger goals next year because we are committed, and We Are Ivanti.

Ivanti Neurons Get More Synapses Sparking

Fri, 21 Aug 2020 15:11:28 Z

It’s been a whirlwind of excitement since our Ivanti Neurons announcement last month. We’ve been delighted by the responses received from customers, partners, and industry press. Among the many thoughts shared back, the ideas organizations have for solving operational challenges are getting creative. And why not? Imagine the possibilities with a platform that offers so much power to discover, manage, secure and service endpoints from the cloud to the edge.

Hyper for Automation

Sure, automation has been around a while – workflow automation has been helping various areas in organizations for years. But hyper-automation is different. It’s bringing the collaborative robot, or “cobot” experience into the realm of IT service in a way previously dependent on manual human intervention. These automation-powered bots have the ability to anticipate and respond to users’ requests within the context of a dialogue. IT staff are equally hyped about Ivanti Neurons for Healing because hyper-automation opens up career growth – replacing routine, reactive tasks with more dynamic projects that keep each day different from the last.

Forward-Thinking Insights

The potential for technological expansion is only limited by boundaries set in the mind. The accelerated migration to remote work has added proof to boundless connectivity and a future where work is done anytime, from anywhere. So, how do you keep end users productive, today and tomorrow? Ivanti Neurons for Edge Intelligence keeps a real-time focus on the performance of users’ devices, providing the details necessary for IT analysts to keep systems optimized. And when there is an incident, the aggregated insights help determine whether an outage is isolated to a user, or a situation affecting a larger population, and speed the time to resolution. This kind of more flexible, responsive (even predictive) support is the opportunity to change the relationship between end users and IT.

Intelligence and Awareness

Most of us can appreciate the experience of bringing a car to the repair shop, only for the auto mechanic to tell us “I ran some diagnostics, but couldn’t get the car to do what you described”. Many of us have experienced the same with our office tech – the slow login, the app that crashes (but only sometimes), the infamous blue screen. In the past, we have opened IT tickets, and maybe the problem is able to be reproduced by the support analyst. The Ivanti Neurons Workspace disrupts the historical pattern of diagnostics. It is designed with operational awareness to collect real-time data equipping first line analysts with the information to resolve more issues, faster. Think not only of the time savings for both IT and the end user, but the cost savings that comes from reducing the escalations previously sent up to specialists.

“A-ha!” Moments

Discovering new devices is a cornerstone of endpoint management – not to mention security. Preventing a rogue device from introducing threats to the enterprise go back to even before infamous security breaches from the mid-2000’s. Now, with Ivanti Neurons for Discovery, there are really unique new ways to use this same detection capability. The easy discovery example is the employee who has bought a new device. The device easily becomes associated with its users at first connection – IT then recognizes and knows it belongs. More unique use cases incorporate nuances resulting from the pandemic and incorporating them with security. Consider the vendor arriving onsite at an office location. As the visitor’s vehicle is shifted into Park, the driver’s mobile device receives an alert from the office’s facilities management department asking them some COVID-19 clearance questions. That device is not only discovered, but now has been tethered to an authorized site guest, identifying him/her as being on the corporate campus and confirmed healthy for building entry.

By now, the wheels of imagination are spinning in your own mind. You know you need to keep pace with the explosive growth of data and devices we’re witnessing. What cool, new ways will you address an increasingly remote work environment? How can you entice Gen-Z talent with a more consumer-like experience? Let’s keep brainstorming. We’re excited to build these ideas together!

Ivanti Neurons Recap: This Summer's Greatest Hits

Fri, 21 Aug 2020 02:43:52 Z

It's been an eventful few months at Ivanti! If you need a refresher, we've got the ICYMI version below.

July 21: Ivanti Neurons Launch

The sizzle began with the Ivanti Neurons launch in July. Maybe you didn't hear about Ivanti Neurons, in which case we'll just assume you had no Internet access... or were in a coma. Because even someone in the remote vicinity of a laptop or smartphone was picking up on the buzz of Ivanti Neurons. See all of the Ivanti Neurons blogs here, or check out some of our favorites below:

July 23: Strategic Partnership with Intel vPro

On the heels of the Ivanti Neurons launch, we announced a new strategic partnership with Intel. Intel Endpoint Management Assistant (Intel EMA) now integrates with the Ivanti Neurons hyper-automation platform in a partnership that enables IT organizations to self-heal and self-secure with Intel vPro platform-based devices—both inside and outside of the corporate firewall. Read more in the press release or the blog.

July 30: Extended Partnership with Qualys VMDR Platform

On July 30 we announced the integration of Ivanti Patch Management technology within the Qualys VMDR (Vulnerability Management Detection Response) platform. As a result, customers will be able to quickly detect vulnerabilities—from endpoint to datacenter—and automatically deploy expertly pre-tested Mac patches across dozens of third-party applications. Read more in the press release or the blog.

August 4: Ivanti Neurons Integrates with Splunk Security Operations Suite

It was just a week later that we announced the integration of the Ivanti Neurons platform with Security Operations Suite solution enabled by Ivanti’s integrations with Splunk Enterprise Security (SIEM) and Splunk Phantom (SOAR), two core components of the Splunk suite. Read more in the press release or the blog.

August 11: Ivanti Launches Biometric Authentication, Self‑Service Automation Bots and Automated Asset Discovery

Using the power of Ivanti Neurons, we recently released enhanced capabilities for the Ivanti Enterprise Service Management portfolio. The new releases give service desks greater visibility to actionable information across their device estate to improve the accuracy, speed, and efficiency of services delivered. Read more in the press release or the blog.

August 13: Clearlake Capital‑backed Ivanti Announces Strategic Investment from TA Associates

On August 13 we announced that TA Associates, a leading global growth private equity firm, signed a definitive agreement to make a strategic growth investment. The funding will provide additional capital that will enable Ivanti to increase its investment in innovation, accelerate its global growth, and continue its acquisition strategy. Read more in the press release.

Ivanti Neurons Workspace: Deliver Faster Resolutions Without Disrupting Your Customers

Tue, 18 Aug 2020 10:44:04 Z

When was the last time an IT conversation reminded you of the movie “Casablanca”? That happened to me recently when I was talking with another IT professional about Ivanti Neurons. We discussed how Ivanti Neurons will make life easier for IT by delivering more self-servicing, self-healing and self-securing experiences. He agreed Ivanti Neurons would be a big help reducing incidents but then said, “We’ll always have tickets.” Ok, maybe this isn’t on the same level as “We’ll always have Paris” but cue the Casablanca flashback.

He was correct, of course. End users will still have issues IT will need to resolve. But add rising expectations for better customer-service experiences, ideally resolving incidents without any disruption or escalation delays, and the demands on the first-line team keeps growing.

Helping the first-line team do more on their own is one outcome Ivanti Neurons Workspace delivers. IT teams using Ivanti Neurons Workspace are finding it’s helping them become more efficient and effective, and greatly reduce escalations by safely providing first-line teams with the required tools for more first-call resolutions, using automated tasks pre-defined by specialists, so they can resolve incidents faster and easier, and deliver great experiences to end users.

Ivanti Neurons Workspace augments IT teams by providing—in a ‘single pane of glass’—the real-time device and end-user insights provided by Ivanti Neurons for Edge Intelligence, with automation-powered bots for pre-defined actions, and also full remote control functionality for those times when IT just needs to reach out and take control.

Offering examples always helps me understand a new solution. Let me share a few where Ivanti Neurons Workspace has delivered better IT outcomes and end-user experiences.

“I was misinformed”

How often does your team start working an incident by asking the end user a bunch of questions to figure out what’s going on? Often the end user may not know the answers, or your analyst must guide them through the steps, especially now since ‘just stopping by’ isn’t an option these days. Or your analyst needs to use several tools to discover what’s going on, and you have a recipe for an engagement that may take a while before the end user is back to work and the analyst can move onto other tasks.

The ‘After’ picture with Ivanti Neurons Workspace is much easier. First-line analysts have immediate access to key IT information on users and their devices, such as:

All associated devices for the user
Device state, including running processes and services
Active Directory status
Group memberships
Incident history by user or device
Installed software, applications, and patches
Network status
Application connectivity

All this information and more is presented to an analyst in one screen, sometimes faster than if the analyst had the device in front of them. Add in the ability to ‘drill down’ into specific attributes, all without bothering or asking the end user to do anything. Your analysts will be a big step ahead in figuring out what’s going on and understanding the end user’s experience.

“Round up the usual suspects”

Quick insight is the first step to resolving incidents. Most incidents aren’t unique, so there are likely common actions a specialist could take to resolve incidents and requests. Ivanti Neurons Workspace safely provides many of these specialist-level actions to the first-line analyst within the context of an incident without providing full access. Such actions include:

Resetting passwords
Unlocking accounts
Changing or assigning group memberships
Flushing the DNS
Updating drivers
Stopping processes and services

These actions employ the same automation engine as used with Ivanti Neurons for Healing so the underlying workflows can likewise be updated, extended, or created anew to provide more automated actions to augment first-line analysts. If an incident isn’t one the first-line analyst can resolve, it can be escalated to a specialist with all the actions and diagnostic information logged.

One example is repairing a device encountering too many crashes or BSOD (Blue Screen of Death). Okay, one crash may be too many, but several is obviously too many. Either way, having the BSOD dump files helps IT diagnose the cause. A workflow in Ivanti Neurons Workspace could reach out to the device, get the needed files, compress them, and send them in an email to IT, all without bothering the end user or doing a remote session. The end user keeps on working while IT analyzes the dump files.

“Play it, Sam”

The extensibility of Ivanti Neurons Workspace means IT teams will be able to automate more and more diagnostic and remediation activities and make them more easily and safely available to first-line analysts. Your specialists can develop the automated actions the first-line team needs to resolve more issues on their own, turning escalations into first-call resolutions.

And with fewer escalations, specialists will have more time to conduct other investigations and innovations. Take the earlier BSOD example. Specialists could use Ivanti Neurons to see if other similar devices are having too many BSODs, identify those devices, analyze their dump files (again, without bothering end users), and see if there’s a common cause, like a device driver that needs updating. Oh, and that driver update? That can be automated with Ivanti Neurons Workspace as well.

“I think this is the beginning of a beautiful friendship”

Ivanti Neurons Workspace, along with the rest of the Ivanti Neurons solutions, promises to make IT more efficient and effective. Some IT teams are already seeing results such as:

Deeper device and user insight in real time
Faster issue resolutions
Fewer escalations
Quicker identification of problem trends
Greater security compliance

And delivering more “Shift Left” outcomes and exceptional user experiences.

Learn more and see Ivanti Neurons Workspace in action here. Hopefully it’ll be the beginning of a beautiful friendship.

Adapting Your Cybersecurity Habits With Ivanti Neurons

Wed, 12 Aug 2020 20:46:29 Z

Tuesday morning you roll out of bed, turn off your alarm, tiptoe down the stairs, retrieve your paper, flip the hallway lights on, feed the dog, and start making coffee—in five minutes. Performing this series of early-morning tasks is called chunking. Certain neurons in the brain “bookend” the habit for you, signaling the routine has started (the alarm) and ended (coffee). This chunking helps you perform habits each day almost unconsciously, giving your brain more time to learn and potentially create new habits.

Survey Says: Adapt

Have you formed any new habits recently to help your security program advance?

A recent Ivanti remote work survey cited 66% of IT professionals experienced a rise in security issues this year since the workforce became mostly remote. In another study, more than half of IoT devices were found vulnerable to either medium- or severe-rated attacks. And earlier this year, a hacker published a list of credentials for more than 500,000 home routers, servers, and IoT devices. What is clear: threat actors have adapted to the recent climate and are pursuing your unsecured endpoints, old routers, and unencrypted VPNs. You must adapt as well.

Forming New Habits: Self-Secure

Intelligent organizations understand security should not be a reactive pursuit, but an adaptive, ever-changing function. You cannot predict every cybersecurity attack, but you can prepare. Enterprises should adapt to respond to threats they face as well as anticipate future ones. One way to prepare is to follow the CIS Critical Security Controls best practices. The top five controls are the most important:

inventory and control your hardware assets
inventory and control your software assets
apply continuous vulnerability management practices
control the use of administrative privileges
implement and manage the security configurations of your devices

The other smart thing to do is respond sooner. Most threat actors exploit vulnerabilities within a median of 22 days and most exploits have a seven-year shelf life according to a research study from RAND. The longer you take to patch, the more vulnerable you become.

In order to adapt, take steps to also become self-secure. At Ivanti we help our customers adapt into a self-securing stance to better understand threats. We break down our self-securing offering into three parts: sensing, prioritization, and remediation.

Sensing what’s in your environment includes discovering and detecting items such as software and devices. This stage analyzes vulnerabilities, and continuously monitors for change.
Prioritization involves seeing what is actively being exploited and responding to risk in the right order. Algorithms using predictive methodologies help anticipate changes and give guidance on the best strategies to resolve challenges.
Remediation determines what actions will fix the problem based on prioritized risk. Assessment of threats need to adapt to environments and circumstances. The more automated responses become, the easier it is to combat threat actors.

For more information on these steps, Ivanti’s patch management expert Chris Goettl reviews these steps in more detail.

Applying Security Habits

Our self-secure adaptive design is part of Ivanti’s overall hyper-automation platform, Ivanti Neurons. Security plays a role throughout each component of this offering:

Ivanti Neurons for Discovery finds and inventories all IT assets in real time to improve clients’ security posture.
Ivanti Neurons for Edge Intelligence helps with endpoint encryption and device queries to obtain real-time operational awareness.
Ivanti Neurons for Healing detects if endpoints and edge devices have potential security vulnerabilities so they can be remediated automatically.

Here’s an example of security in action from one of our manufacturing customers of Ivanti Neurons for Healing. This customer had challenges receiving laptops back from former employees. In one case a former employee was 3,000 miles away. Typically, 30% of assets are “ghost” assets that are lost or unaccounted for. These types of “ghost assets” cause data and security risks. The Ivanti team helped our customer disable the accounts in question by locking laptops and rebooting the machines remotely. Within hours, our customer received notifications indicating laptops were shipping back to corporate. As a result, Ivanti improved the customer’s overall security posture.

Conclusion

Cybersecurity threats will remain challenging as remote working extends and data from endpoints and edge devices grows. Ivanti can help you adapt your habits into a self-securing stance. For assistance, request your free Ivanti Neurons trial.

Ivanti Neurons for Edge Intelligence for Our Fast-Changing Future

Thu, 06 Aug 2020 22:00:04 Z

The demands on enterprise IT are set to grow exponentially.

The Edge Comes into View

Edges. Many times, they represent an unknown—like the edge of space or a horizon for sea captains and pilots. And they give us mixed feelings—fear, anxiousness, direction, a sense of adventure. Most often, they represent a future state or location we are moving towards.

For IT there is a new edge. The Edge of computing. The place where things and people are connecting with our networked digital world. Devices of all kinds, the next generation of workers, automated bot assistants! And as IT professionals, that future place or state is rapidly coming into view. It’s predicted that 41.6 billion connected devices, or “things,” will be generating 79.4 zettabytes (ZB) of data by 2025 .

This rapid growth creates IT complexities in the forms of ongoing management and integration challenges, which not only affect devices but IT staff and end-user experiences. You might be asking how your IT team will adapt. What could you do if you were connected at the Edge?

Life on the Edge

Ivanti’s solution to these questions? One way is to help organizations mature from basic automation to a fusion of hyper-automation and deep learning capabilities. With this, organizations can self-heal and self-secure devices, and self-service end-users proactively, predictably, and continuously.

Ivanti Neurons for Edge Intelligence leverages our hyper-automation platform approach, making it easy for today’s IT Analyst to query all devices, in seconds, to get real-time insights, connecting them with users and devices from cloud to edge. And smart dashboards provide quick operational awareness to provide real-time insights that can lead to immediate action.

A Case for Edge Intelligence

Consider the following example. If company employees were experiencing excessive logon times to their devices, which hurts productivity, how would you know unless they submitted a ticket? Would you be able to determine whether this is an isolated event, or a larger systematic issue without a surge in help desk requests? With Edge Intelligence, you’d receive logon-time breakdowns across all on-prem, cloud, and edge devices. You’d see if the incident was singular or happening company-wide and be able to take the proper actions quickly to remediate.

In this actual enterprise example, the cause of excessive logon times was identified as single-threaded logon scripts and many Group Policies being used for desktop configuration. Quickly identifying and fixing the issue improved logon times to just seconds instead of minutes.

Wrapping Up

As it seems with emerging technologies, we’ve heard a lot about the future of the Edge over the years. Well, the Edge is now in sight.

Organizations can benefit greatly from Edge Intelligence and the power of hyper-automation. Accurately and quickly identifying the issues and moving to swift remediation reduces costs and improves IT teams and end-user experiences. In short, it’s a competitive advantage by limiting loss of productivity within your organization.

I asked myself the other day—do I have any IoT Edge devices at home? I had never given much thought to it, and I surprised myself by identifying four such devices, which was 25% of all my connected devices in my home. All connected to the same network I connect to every day while working from home. The growth is real.

Next Steps

Learn more and see Ivanti Neurons for Edge Intelligence in action here.

Ivanti Integrates Ivanti Neurons Platform with Splunk Security Operations Suite

Tue, 04 Aug 2020 20:15:39 Z

We have unveiled several exciting announcements recently, from the launch of Ivanti Neurons to all of our strategic partnerships and integrations (see the details on Intel and Qualys). In fact, we have had so much content to announce that we've needed to create a whole new blog category!

But this train is not slowing down any time soon.

Ivanti Integrates Ivanti Neurons Platform with Splunk Security Operations Suite

Today we announced that the Ivanti Neurons platform now integrates with the Splunk Security Operations Suite. Together, the solution enables organizations to realize an autonomous edge, which offers hyper-automated self-healing, self-securing and self-service capabilities. The solution is enabled by Ivanti’s integrations with Splunk Enterprise Security (SIEM) and Splunk Phantom (SOAR), two core components of the Splunk suite.

From Nayaki Nayyar, executive vice president and chief product officer, Ivanti:

“With the explosive growth of endpoints, edge devices and remote workers, IT operations and security teams need to apply advanced automation to improve workflow, auto-remediate security issues and enhance the remote user experience. By integrating the hyper-automated Ivanti Neurons platform with Splunk Security Operations Suite, we are taking a big step towards enabling truly self-healing autonomous edge so that enterprises can increase speed and accuracy of services delivered to end users.”

The Ivanti Neurons integration with the Splunk Security Operations Suite automates the discovery, management, and security of broadly heterogeneous endpoint environments. Triggered alerts for endpoint events are analyzed, correlated with other security data and visualized in Splunk Enterprise Security. Prioritized endpoint events are automatically passed on to Splunk Phantom for enhancement and the automated generation of enriched remediation tickets in Ivanti Service Manager. This enables teams to fully automate the discovery, monitoring and servicing of a broad array of traditional and modern endpoints.

This Ivanti Neurons integration with the Splunk Security Operations Suite is available now for customers licensed on both solutions.

Ivanti Neurons for Discovery: The Single Source of Truth in Minutes, Not Days

Mon, 03 Aug 2020 22:28:53 Z

Who doesn’t like a good ole’ game of “Guess how many M&Ms or marbles are in that jar”? Are you off by 20, 50, or by a couple hundred? Or maybe you’re fairly close estimating the number of spheres in a confined space. Now think about your IT assets in your organization. Are all of them accounted for? Where are they located? Take a guess. Guess again. A miss is as good as a mile.

Keeping track of all your IT assets, understanding what hardware and software is used, and keeping them protected is not an easy feat. Now add on the next layer of complexity with more remote devices and edge devices accessing your network. Employees working anytime, connecting from anywhere, requiring contextual awareness to provide better overall experiences.

There’s no way to manually keep up with the device proliferation and real-time changes. You need a way of quickly scanning and tracking what’s connecting to your network to keep your environment secure, compliant, and cost efficient.

What if you could have accurate and actionable visibility of all your IT assets in minutes? Well, let me introduce you to Ivanti Neurons for Discovery.

1. Ivanti Neurons for Discovery: Find Everything

An IT staffing company once told me about an asset that was stuck in a cabinet behind a box of Oreo cookies. Right, not sure how Oreo cookies can last that long. Definitely not in my household. Or another example, an organization was still accounting for assets that were no longer with the company—decommissioned, lost, or never returned. Would you be OK with former employees still holding onto your hardware and accessing corporate information? Wouldn’t you want to know when new or unknown devices join your network?

With Ivanti Neurons for Discovery, you can say goodbye to your tracking spreadsheets once and for all, and to those ghost cookies…I mean ghost assets. It discovers devices quickly in real time on any network segment.

You might say you already have multiple discovery solutions in your organization. How is this one different? Ivanti Neurons for Discovery uses passive and active discovery to find all managed or unmanaged devices. It detects network-connected devices in real time, even if they’re behind a firewall. Ivanti Neurons for Discovery scans your network without requiring you to install agents on all devices on your network. Think of it as your neighborhood watch program with an assigned representative and not consuming your bandwidth. Always on, always working for you. If something is there, even for a moment, you'll catch it and inventory what's on it.

2. Ivanti Neurons for Discovery: Aggregate Information

A transportation company was sure they captured everything they had. When we then conducted a discovery scan, we found 30% more devices than their endpoint solution and Active Directory showed them. They were sure their environment was properly protected, but how do you secure devices you don’t even know about?

With Ivanti Neurons for Discovery, once a device is discovered, the in-depth inventory service interrogates each IP-connected device or service to learn everything it can, including information about the network connection, hardware details, manufacturer, and what software is running on the device. When under management, inventory will also provide software-usage information to help you manage your software assets efficiently. Ivanti Neurons for Discovery “connects the dots” and aggregates accurate asset information from multiple data sources. Ongoing discovery scans ensure you are aware of any new or unknown devices that join your network.

3. Ivanti Neurons for Discovery: Augment Data

A university was tracking their IT assets with spreadsheets and another department managed their contracts repository. They didn’t proactively manage end-of-life dates or warranties and often spent time fixing devices that were still under warranty. In our “Aligning ITSM and ITAM Processes Survey”, we found that half of the respondents admitted to sometimes fixing devices that later were discovered to still be under warranty, resulting in time and money lost.

In this same study, 20% of respondents admitted not having the insights into which assets were out of date, and 28% reported spending hours per week supporting out-of-warranty and out-of-support-policy assets. With Ivanti Neurons for Discovery, you can augment your data from third-party systems using connectors to B2B data like hardware warranty information, vendor licensing rules, or purchasing information from resellers. With this enriched asset data, you can optimize purchases, warranties, contract data, and end-of-life cycles as needed so your service and IT teams have everything they need to log an issue against an asset and get it resolved quickly and accurately.

4. Ivanti Neurons for Discovery: Make Sense of Data

Analyst firm Enterprise Management Associates found that on average, IT spends 10 hours per week resolving data-accuracy issues. That’s a quarter of a person year at minimum that I’m sure you can find other use for. For many, it’s taking a lot of time as data can be in so many different places—in their procurement system, in IT endpoint and network management systems, in contract repositories, in spreadsheets, and more.

It’s tough to normalize the data, reconcile it, and make sure you understand the delta, and where the data is coming from. Good tools can automate this process for you, to give you robust data you can work with.

Ivanti Neurons for Discovery fully automates normalization and reconciliation processes to clean up your data and reduce the number of names used to describe the same thing. Convert all your asset data to have a standard name, mapping structure, and classification.

Ivanti Neurons for Discovery helps you reduce the time spent on aggregating and harmonizing asset data from weeks down to minutes. No more cross-referencing and manual reconciliation activity among devices that interact with every system in your IT estate. The output helps you identify devices that are missing and unaccounted for, and that pose significant security risks. Ivanti Neurons for Discovery provides accurate asset information leading to improved cost efficiency through better utilization of existing assets and improves your security posture.

5. Ivanti Neurons for Discovery: Provide Actionable Insights

Asset Discovery and inventory capabilities are foundational to effective endpoint, asset, service, and security management. To efficiently assist your end users and remediate issues quickly, you need to have all the necessary asset data in context at your fingertips. We asked 1,600 survey respondents if service management processes and request workflows have automatic visibility into asset information and relationships. According to the findings, 43% reported “sometimes,” but more than one third of respondents don’t have any visibility into this information.

Ivanti Neurons for Discovery provides turnkey discovery functionality to easily populate configuration management (CMDB) and asset management databases. Our customers report that it only takes them minutes to populate the CMDB with insightful data that helps them provide service in context with asset information to make informed decisions. When service and asset processes and data are closely aligned with accurate data in the CMDB, many activities and processes become more automated, efficient, and responsive, leading to better outcomes and better service experiences.

Try It Out Today

With Ivanti Neurons for Discovery, you have complete asset visibility and are equipped to optimize your asset value and improve your security posture. No more tracking spreadsheets and no more ghost assets. Ivanti Neurons for Discovery provides you with that single source of truth in minutes not days, for accurate and actionable insights. So, stop guessing, eat your M&Ms, and let Ivanti Neurons for Discovery take care of your asset-discovery needs.

Qualys Integrates Ivanti Patch Management Into Qualys VMDR Platform

Thu, 30 Jul 2020 12:07:27 Z

Our latest announcement comes in the form of another exciting partnership, this time with Qualys! A leading provider of disruptive cloud-based IT, security, and compliance solutions, Qualys has integrated Ivanti Patch Management technology into their Qualys VMDR (Vulnerability Management Detection Response) platform.

Qualys President and Chief Product Officer, Sumedh Thakarsaid, said the following:

"The seamless integration of Ivanti and Qualys technologies in VMDR takes endpoint security to the next level. By extending Ivanti’s leading patch management technology from Windows to Mac, Qualys VMDR now orchestrates the entire process of discovery, prioritization and patching of critical vulnerabilities in real time across hybrid IT environments including macOS devices. Furthermore, it allows us to bridge IT and SecOps workflows to more efficiently secure and manage today’s explosion of remote workers."

The Ivanti and Qualys partnership provides for tight integration of Ivanti Patch Management into Qualys VMDR to automate and simplify the patch remediation process. As a result, customers will be able to quickly detect vulnerabilities, from endpoint to datacenter, and automatically deploy expertly pre-tested Mac patches across dozens of third-party applications. Current Microsoft patch coverage includes both security and non-security content for all supported products, operating systems, and applications, including Microsoft Office. The Ivanti patch catalog is one of the industry’s most comprehensive catalogs currently securing over 180 million endpoints globally.

The next phase of the integration will allow Qualys customers to patch MacOS systems, as well as over 70 third-party Mac applications, directly through Qualys VMDR.

Nayaki Nayyar, EVP and Chief Product Officer, Ivanti, had this to say about the expanded partnership:

“Ivanti’s expanded partnership with Qualys further underscores our mission to help deliver end-to-end security solutions. Our recently announced Ivanti Neurons platform powers security teams with advanced automation capabilities to discover, prioritize, remediate vulnerabilities and self-heal endpoints and edge devices.”

As mentioned in a recent press release, Ivanti Neurons transforms the way IT gets work done. Powered with hyper-automation capabilities, it delivers:

Learn more in the interview below:

Ivanti Blog: Artificial Intelligence

Inventory to Intelligence: How AI and Automation Improve Endpoint Visibility

Why traditional discovery practices fall short

Common challenges in manual device discovery

Visibility gaps and security risks

Device discovery vs. device health monitoring

Key indicators of endpoint health

The risk of tracking only device presence

Negative impacts on security and compliance

Operational inefficiencies

How AI and endpoint automation improve endpoint visibility

Unified telemetry across multiple sources

AI-Powered automation and autonomous bots

Self-healing workflows for IT productivity

Broader impact on security, productivity and user experience

Closing visibility gaps

Improving IT operations and end-user satisfaction

How AI-Driven Automation Solves Patch Management Silos

Why IT and security are misaligned on patching

Technology silos create conflicting realities

Different data views lead to friction

The risk of misaligned patching priorities

Prolonged exposure windows and breach risk

Common post-patch issues and IT ticket overload

Transforming patch management with autonomous endpoint management

How AI improves patching decisions

How automation accelerates remediation

From data debates to unified intelligence and shared visibility

Shared patch ownership: powering IT and security collaboration

How to Implement an AI Governance Framework Using Safe, Ethical and Reliable AI Guardrails

The state of enterprise AI: a risky Wild West

What is an AI governance framework?

3 layers of AI guardrails in an AI governance framework

Technical Guardrails

Ethical guardrails

Regulatory and legal guardrails

AI governance vs. AI risk management risk

Challenges in scaling AI governance

How to implement responsible AI

How Agentic AI for ITOps Unlocks Value at Scale

Automation and outputs vs. agentic AI autonomy

High-impact agentic AI use cases in ITSM and ITOps

1. Autonomous incident remediation

2. Proactive problem prevention

3. End-to-end lifecycle management

4. Dynamic change and release management

5. Autonomous resource and capacity management

Overcoming barriers and delivering enterprise impact

The agentic AI operating model

The future ROI of agentic AI initiatives

The bottom line

AI-Driven IT Cost Management: Aligning Spend with Strategic Value

Major barriers to IT cost optimization

AI and machine learning for predictive cost management

AI cloud cost management and resource management

AI’s impact on streamlining IT processes

How AI empowers IT to deliver strategic value

AI threat hype: why chasing ghosts leaves real vulnerabilities exposed

Real threats vs. AI hype: misaligned risk prioritization

Critical gaps in threat preparedness persist

1. Ransomware attacks

2. API-related exposures

3. Software vulnerabilities

4. Compromised credentials

5. Phishing

AI cybersecurity tools: foe or friend?

Recommendations to refocus on cybersecurity fundamentals

1. Continuously monitor your complete attack surface

2. Prioritize threat response based on overall impact threat and risk to business

3. Leverage AI to accelerate defenses

Forget the future unknowns – focus on what’s known

AI Cybersecurity Best Practices: Meeting a Double-Edged Challenge

Why is AI security important?

Potential AI cyber threats

AI cybersecurity benefits

Enhanced threat detection

Improved incident response

Risk assessment and prioritization

Security considerations for different types of AI

Latest developments in AI cybersecurity

Forget the future unknowns – focus on what’s known