Ivanti Blog: Posts by

Exposure Management vs. Vulnerability Management: Which Delivers Real Risk Reduction?

Thu, 29 Jan 2026 13:00:01 Z

Vulnerability management has served organizations and the cybersecurity industry for years. It is a capable practice that has helped companies defend their attack surface and prevent threat actors from exploiting vulnerabilities.

But technology and IT infrastructure have evolved. Vulnerability management no longer can meet the challenges that come with this evolution. Now, exposure management is here to provide an even more holistic approach to endpoint security that covers the areas vulnerability management falls short in.

Let’s dive into the distinctions so that you can decide how to protect your organization.

What is vulnerability management?

Vulnerability management is a cybersecurity practice that includes continuous and proactive identification, assessment, prioritization and remediation of vulnerabilities hackers can use to infiltrate your organization.

However, it’s important to note that there are two different types of vulnerability management:

Legacy vulnerability management	Risk-based vulnerability management
Involves attempting to remediate as many vulnerabilities as possible. This often results in substantial effort and unrealistic expectations for success while presenting a false sense of security.	An evolved vulnerability management practice that accounts for risk in vulnerability prioritization. This allows organizations to patch the critical vulnerabilities that pose a real-world threat, protecting your organization from threat actors while also ensuring a strong security posture and effectively managing resources.

A risk-based vulnerability management approach goes beyond legacy vulnerability management, providing your organization with the following benefits:

Continuously monitors vulnerabilities for proactive security.
Identifies actively exploited exposures.
Enables effective remediation efforts.
Reduces risk.
Assists organizations with reaching compliance.

While risk-based vulnerability management covers a lot of bases, it still doesn’t offer the holistic approach to cybersecurity that organizations need to stay safe and secure. That’s where exposure management comes into the picture.

What is exposure management?

Exposure management is an evolving cybersecurity practice that provides comprehensive visibility across your entire attack surface. It allows IT and Security teams to identify exactly where your organization may be exposed while including risk-based prioritization, remediation and more. Exposure management focuses on maintaining an organization’s self-determined risk appetite. Therefore, it encompasses four stages:

Like risk-based vulnerability management, exposure management helps prioritize which vulnerabilities and exposures should be addressed first based on real-world risk, but it goes further by factoring in what is most relevant to your specific business. This cybersecurity approach ensures that the highest-risk exposures are remediated proactively, before they can be exploited by attackers.

Exposure management vs. vulnerability management: What’s the difference?

Exposure management represents the next evolution beyond traditional vulnerability management. While vulnerability management primarily focuses on identifying and addressing weaknesses in servers and endpoints, exposure management expands this scope by delivering complete visibility across the entire attack surface.

In terms of key differences, these include:

Exposure management is designed for newer types of assets: Modern IT environments have grown increasingly complex, now including assets such as Software-as-a-Service (SaaS) applications, IoT devices, cloud infrastructure and more. Exposure management is designed to account for these newer kinds of assets, ensuring IT and security teams can identify risks wherever they exist in the organization. By doing so, exposure management provides a comprehensive understanding of all potential entry points. This empowers organizations to manage and reduce risk more effectively than ever before.
Exposure management understands the reality and champions a risk appetite approach: Again, vulnerability management is centered around patching vulnerabilities. While risk-based vulnerability management provides risk prioritization and remediation orchestration, the practice doesn’t acknowledge the fact that it’s not realistic for an organization to patch every vulnerability. The term risk appetite is an organization’s self-determined measurement of how much risk it is willing to accept. This is a significantly more realistic approach that rallies the organization together to achieve shared KPIs to measure success consistently across teams.
Exposure management goes beyond CVEs and CVSS: Vulnerability management focuses primarily on common vulnerabilities and exposures (CVEs). While CVEs are an important target for most organizations, they are not the only catalysts that threat actors can use to cause damage to your organization. Hackers can still leverage the following exposures (that vulnerability management doesn’t cover) to infiltrate your organization:
Misconfigurations.
Application security issues.
IT system policies.
Privileged access controls.

Tying it back to the holistic approach, exposure management covers all these modern assets. Furthermore, vulnerability management is heavily reliant on the Common Vulnerability Scoring System (CVSS) for remediation prioritization. While CVSS is a solid measurement for severity, it provides an effective risk-adjusted perspective.

Risk is an important factor to keep in mind since it includes whether a vulnerability has been exploited, if it has ties to ransomware/malware or is currently trending. Not factoring risk creates a false sense of urgency with CVSS, causing IT and security teams to waste time and resources on vulnerabilities that are not truly urgent.

How to safeguard your organization

Now that we have covered the differences between exposure management and vulnerability management, it’s time to leverage the advantages that exposure management provides. Learn how Ivanti’s exposure management portfolio can elevate your IT and security teams.

Understanding External Attack Surface Management: How It Works and Why It’s More Critical Than Ever

Tue, 02 Dec 2025 15:06:33 Z

Attack surfaces can expand without your organization even realizing it. And, lacking visibility into your external-facing assets and the vulnerabilities they may contain can lead to significant security risks.

External attack surface management (EASM) is a cybersecurity approach designed to safeguard your external assets and strengthen your organization's overall security posture. It does this by providing full visibility into these assets (and associated vulnerabilities) that could be exploited by threat actors.

In this article, we’ll walk you through how EASM works, the risks involved with overlooking your external attack surfaces, the benefits as well as where EASM sits in the broader practice of exposure management.

How external attack surface management works

EASM is the practice of identifying and managing your external-facing assets (e.g., websites, APIs, etc.) to prevent security breaches. Additionally, the process includes identifying attack surface gaps that can expose your organization to cybersecurity risks.

EASM helps fight unwanted expansion of your attack surface through visibility, enabling your organization to stay up to date on your potential vulnerabilities. Leveraging EASM provides the following benefits:

Additional source of discovery and asset visibility.
Curbs cloud sprawl and shadow IT.
Reduces AI-powered phishing tactics.
Analyzes and prioritizes exposures.
Detects data leakage.
Reduces phishing and social engineering attacks.
Adheres to regulatory compliance requirements.
Extend your vendor risk management by providing external risk perspective of third-party vendors.

EASM involves multiple key stages, including Discovery, Assessment, Prioritization, Reporting and Remediation.

Discovery

As mentioned above, EASM involves monitoring your external attack surface to identify those assets to both catalog them and uncover vulnerabilities that could lead to a hacker infiltrating your organization.

It doesn’t involve an invasive scan. Rather, it involves a passive crawl of your external attack surface, and all you need is a URL to start the process. EASM solutions, for example, use public data in combination with security intelligence. The assets that make up your external attack surface include:

Web servers.
DNS servers.
IoT devices.
Network edge devices.
Application servers.
Certificates.
Cloud-based tools.
Shadow IT.

Learn more: How to Identify Your Organization’s Attack Surface

Assessment

Thorough and continuous assessment is essential to understand your organization's risk landscape and effectively prioritize remediation efforts. At this stage, your organization evaluates whether the assets identified during the discovery process are in use and if they are harboring vulnerabilities. EASM solutions do this by identifying publicly disclosed security weaknesses, outdated software versions and more.

By examining these assets for vulnerabilities and other potential security risks, you gain crucial insights into your security posture.

Prioritization

Once vulnerabilities are identified, the next step is to determine which ones to address first based on their risk to your organization. Since it’s often impractical to remediate every vulnerability, risk scoring methods help you assess the urgency and impact of each exposure. This allows your security teams to focus on the most critical issues, streamlining the remediation process and ensuring that resources are allocated effectively.

Reporting and remediation

The next stage in EASM is to report on these risks and begin remediation. EASM solutions enable you to generate comprehensive reports that offer an overview of your external attack surface, along with detailed breakdowns of critical vulnerabilities. These reports are invaluable for communicating the nature and urgency of potential threats, helping stakeholders understand the importance of prompt remediation and informing decisions.

The risks involved with not monitoring your external attack surface

If your organization does not have full knowledge of the external attack surface, you risk having unknown or unmonitored assets or misconfigurations that open you up to attack, resulting in reputational damage, financial loses and more.

The lack of visibility into shadow IT, misconfigured or forgotten services allows for easy entry points for attackers. According to Computer Weekly, identity and access management company Okta was exposed to multiple security breaches due to shadow IT.

Furthermore, these assets are visible to anyone on the internet. It doesn’t require any special skills for someone to obtain this information about your external attack surface, meaning it is straightforward for a threat actor to gain access to your organization if you don’t enact proper measurements.

Now that you have an overview of external attack surface management, it’s important to understand that it’s just one part of your larger attack surface, which is where exposure management comes into play.

How EASM plays into exposure management

Exposure management focuses on asset visibility, exposure aggregation, risk-based prioritization and remediation of exposures. It’s a comprehensive cybersecurity practice that helps organizations define their risk appetite and keep levels within acceptable bounds.

EASM is just one part of exposure management (visibility, as shown in the graphic above). In cybersecurity, you can’t protect what you can’t see. So, let Ivanti help you get full visibility into your external attack surface with Ivanti Neurons for EASM.

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