What is Agentic Vulnerability Management

Every year, thousands of new vulnerabilities are reported, over 29,000 Common Vulnerabilities and Exposures (CVEs) in 2024 alone, a record-breaking figure that shows no signs of slowing down. With expanding attack surfaces from cloud adoption and an explosion in interconnected devices, security teams are struggling to decide which vulnerabilities need to be prioritized and how to remediate them. This means that the time it takes from detection to remediation is still far too long. Now that attackers are leveraging AI to launch attacks faster than ever, defenders must radically change the way they manage vulnerabilities.

Traditional vulnerability management

In most organizations, vulnerability management, the process of finding, assessing, and fixing security flaws, is a juggling act. Analysts must sort through endless lists of potential threats, weigh their actual risk to the business, prioritize remediation efforts, and coordinate with IT teams to patch or mitigate them. This process is labor-intensive, prone to bottlenecks, and often hampered by incomplete information. Worse yet, the time from vulnerability disclosure to exploitation is shrinking. Using AI, attackers are now weaponizing fresh vulnerabilities within hours or even minutes of their discovery.

At its core, vulnerability management is a cyclical process that involves:

1. Discovery: Finding vulnerabilities via scanning tools, penetration tests, threat intelligence feeds, and vendor advisories.
2. Assessment: Determining the severity of each vulnerability, often using scoring systems like CVSS (Common Vulnerability Scoring System).
3. Prioritization: Ranking vulnerabilities based on their potential impact and exploitability, as well as the value of the affected assets.
4. Remediation or mitigation: Applying patches, reconfiguring systems, or implementing workarounds.
5. Verification: Confirming that the vulnerability is no longer exploitable.
6. Reporting and compliance: Documenting the process for internal and regulatory purposes.

In traditional setups, this cycle is managed by security analysts, IT staff, platform engineers, and developers, with multiple handoffs and delays. This human-centric approach is effective in small, static environments but starts to crumble when faced with thousands of assets, hybrid infrastructures, and the need for near-instant response.

Why is traditional vulnerability management no longer enough?

Human-centric vulnerability management struggles to keep pace for several reasons:

• Volume of vulnerabilities: The number of disclosed CVEs is growing exponentially, with tens of thousands discovered annually. Even the most efficient human teams can’t manually assess them all in real time.
• Acceleration of exploits: Attackers are weaponizing vulnerabilities within hours or minutes of disclosure. Human-only workflows can’t match that speed.
• Complex, distributed environments: Hybrid cloud, IoT, and microservices architectures create sprawling attack surfaces that require constant monitoring across different platforms.
• Analyst fatigue and burnout: Sifting through endless alerts, many of which are low priority or false positives, drains focus and leads to missed critical issues.
• Friction between teams: Traditional remediation requires back-and-forth between security, IT, and platform engineering teams, which slows patch deployment.
• Compliance pressure: Regulatory timelines for patching can be tighter than what human-driven processes can realistically meet.

In short, manual approaches can’t deliver the speed, scale, and continuous precision required in today’s threat landscape, making autonomous or Agentic augmentation essential.

What is Agentic vulnerability management?

Enter Agentic vulnerability management, a paradigm shift that uses the power of Agentic AI to address the scale, speed, and complexity of modern cyber threats. Unlike traditional vulnerability management, which relies heavily on human input and linear workflows, Agentic systems can think and act autonomously. They do more than just flag vulnerabilities; they can autonomously monitor systems, understand business priorities, decide on remediation steps, execute them, and verify that the issue has been resolved.

This is not about replacing humans in cybersecurity - it’s about augmenting them. Agentic AI enables security teams to focus on strategic initiatives and high-value decision-making, while AI agents handle the grind of detection, prioritization, and patch orchestration. For organizations battling shrinking security budgets, growing attack surfaces, and the relentless pace of cybercrime, this shift is not just an efficiency upgrade, it’s a survival mechanism.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems that operate as autonomous ‘agents’. These agents perform the following functions: 

1. Monitor environments through data inputs, sensors, or system integrations.
2. Make decisions using rules, statistical models, or learned patterns.
3. Act independently to achieve objectives, often coordinating with other agents.
4. Learn and adapt, improving performance based on new data and feedback.

Think of Agentic AI as moving beyond narrow AI tasks like answering a single query or classifying an image. It also goes beyond traditional automation, which requires complex setup and is often more rigid. An Agentic system can:

• Choose the best actions from multiple options
• Execute those actions
• Adjust strategies dynamically when conditions change
• Explain reasoning behind decisions and actions
• Execute multiple, consecutive tasks
• Learn from previous actions

In the context of vulnerability management, Agentic AI doesn’t just identify vulnerabilities, it understands whether findings matter in the business context, the reasons why, how to fix them, and how to apply patches safely, while weighing risks versus unwanted disruptions.

How Agentic AI transforms vulnerability management

When Agentic AI and vulnerability management are combined, the result is a continuous, intelligent, and largely self-driven system for keeping an organization’s digital infrastructure secure. Here’s how:

• Continuous detection: Agents constantly scan for vulnerabilities, not only on a scheduled weekly run, but continuously, adapting to real-time threat data.
• Contextual risk analysis: Instead of treating all ‘critical’ CVEs equally, AI agents consider the asset's business impact, attack surface, exploitability, whether it is actually in use, and other important contextual factors to rank which vulnerabilities pose the greatest risk to the organization.
• Collaborative workflow integration: Agents interact with IT Service Management (ITSM) tools like Jira, GitHub issues, and Azure DevOps, opening and closing tickets automatically, syncing updates, verifying fixes, and reopening tickets when drift occurs.
• Autonomous remediation: Where policy allows, agents can apply patches, adjust configurations, or disable vulnerable components with or without waiting for human approval.
• Learning over time: As AI agents observe how the organization responds to different vulnerabilities, it refines its prioritization logic to match operational preferences and compliance requirements.

Are attackers using Agentic AI?

Attackers are starting to leverage Agentic AI to find vulnerabilities, although its widespread autonomous use for complex attacks is still developing. They are also enhancing their already automated attacks with AI, adding new fuzzing techniques and real-time exploitation of newly found vulnerabilities. Here are a few ways in which attackers are using Agentic AI to launch attacks:

• Accelerated vulnerability research and discovery: AI, with its ability to analyze vast amounts of data and identify patterns, can potentially expedite the process of finding security weaknesses in systems and applications. Attackers can use it to scan for exposed web servers, open ports, identify misconfigurations, and analyze codebases for vulnerabilities.
• Automated hacking tools: While not yet fully realized for complex, precision attacks, the potential exists for AI agents to automate the hacking process itself. This includes generating attack plans and exploiting vulnerabilities.
• Enhanced social engineering and phishing: GenAI has become a powerful tool for impersonation and tailored phishing attacks that blow everything we have seen so far out of the water. Agentic AI can facilitate sophisticated and personalized social engineering even further by autonomously analyzing information from various sources to craft highly tailored and convincing attacks. 
• Creating AI-powered malware: AI-powered malware can autonomously scan networks, identify weak points, and launch targeted attacks without human intervention, potentially modifying their own code to bypass pattern detection.
• Lowering the bar for attackers: The enhanced capabilities of Agentic AI also lowers the technical expertise required to execute certain attacks, enabling a broader range of individuals to engage in cybercrime (scripters on steroids).

Agentic AI vulnerability management workflow

Agentic vulnerability management doesn’t just automate the vulnerability management cycle, it fundamentally reshapes it. Let’s break down each stage in detail:

1. Continuous monitoring and discovery

Traditional vulnerability scanning is often scheduled (weekly, monthly, quarterly) and heavily dependent on manual initiation. This means that there are windows, sometimes days or weeks, where vulnerabilities exist but remain undetected.

Agentic systems change this by operating continuously, scanning not just for known CVEs but also for anomalous patterns that may indicate zero-day issues. To assist with this, an agent can ingest data from the following sources:

• Public vulnerability databases like the NVD (National Vulnerability Database)
• Vendor-specific advisories (e.g. Microsoft Patch Tuesday)
• Threat intelligence feeds highlighting active exploitation campaigns
• Internal telemetry such as application logs, API error rates, and configuration drifts

The agent doesn’t simply detect, it correlates these inputs to determine whether a new vulnerability is relevant to your specific asset inventory. This precision eliminates unnecessary alerts and reduces analyst fatigue. It also breaks down silos between security tools and their data, making findings more actionable.

2. Asset inventory correlation

A vulnerability’s severity means little without knowing which systems it affects. Agentic AI automatically maintains an up-to-date asset inventory by:

• Interfacing with CMDBs (Configuration Management Databases)
• Pulling data from cloud provider APIs (AWS, Azure, GCP, etc.)
• Accessing configuration from services, clusters, and management tools
• Performing network discovery scans or inspecting network device configurations
• Cross-referencing container registries, development environments, and SaaS configurations

By mapping vulnerabilities to assets in real time, the agent can instantly answer:

• Are we vulnerable to this CVE?
• Which systems are affected?
• Is there a patch available?

This eliminates the lag of manually matching CVEs to infrastructure, a process that can take days in a large enterprise.

3. Contextual risk scoring and prioritization

While CVSS scores are a useful baseline, they don’t account for a deeper environmental context, exploitability, or business impact. Agentic AI considers:

• Asset criticality: Is it a production database holding customer PII or a staging server?
• Exposure: Is it behind multiple firewalls or directly accessible from the internet?
• Exploitability: Is there a proof-of-concept exploit in the wild, or is this only theoretical?
• Active threat activity: Are attackers actively scanning for this vulnerability now?
• Regulatory impact: Would exploitation cause a compliance violation (GDPR, HIPAA, PCI-DSS)?

This allows for smart prioritization that considers all relevant factors to decide which CVEs need to be remediated first. For instance, a CVSS 7.5 on a critical payment system should take precedence over a CVSS 9.0 on a test server that’s air-gapped. Agentic AI can assess all the relevant data and quickly make smart decisions so teams are focused on fixing the most dangerous vulnerabilities in their specific environment.

This also means that once issues or tickets are forwarded to platform engineers or IT teams, they can ask the agent questions such as:

• Why is this CVE on this asset a priority?
• How should we mitigate if we can’t patch right away?
• What software version is installed on the system?

This reduces the back-and-forth that often happens between security, IT, and platform engineering teams, decreasing friction and accelerating remediation.

4. Autonomous remediation

Here’s where Agentic AI truly departs from legacy approaches. It can autonomously or semi-autonomously remediate vulnerabilities much faster than any human can.

Remediation may include:

• Patching: Downloading and installing vendor updates.
• Configuration changes: Disabling vulnerable services or vulnerable configuration options, modifying permissions or affected components.
• Access control adjustments: Blocking specific IP ranges, disabling user accounts, or blocking external access.
• Temporary mitigations: Applying firewall rules or disabling features until a permanent fix is available.

For example, if a critical zero-day is being actively exploited in the wild, the agent could immediately add a protective rule to the firewall, documenting the change, while waiting for a vendor to patch the issue at the core.

Crucially, guardrails are required to ensure AI agents only act within defined parameters:

• Avoid accidentally destroying other components while trying to protect a system (e.g. deleting production databases via uncontained AI agents)
• Require human approval for impactful changes
• Exclude assets that have exceptions configured
• Rollback mechanisms if remediation causes disruption
• Compliance with change management protocols

5. Verification and resolution

After remediation, the agent re-scans affected systems to ensure the vulnerability is resolved. If unsuccessful, it can escalate the issue to a human analyst and suggest alternative mitigation strategies. Once the agent confirms that the issue is resolved, they can close tickets automatically, and notify stakeholders with a clear, auditable report.

6. Governance and reporting

From a compliance perspective, Agentic systems can ensure that systems continuously remain compliant. They can:

• Auto correct policy drift: By continuously checking whether configurations comply with policies, agents can automatically open tickets and (semi-) autonomously fix any issues to ensure that systems are compliant at all times. 
• Track SLA timelines for patching: Agents can automatically notify when vulnerabilities are about to miss their SLA, and suggest mitigations that can be applied with or without human approval.
• Store remediation evidence: Agents can store detailed information such as patch logs and configuration differences that they can quickly analyze and present on if required.
• Produce audit-ready reports: Gathering data and delivering this in a consumable report can be a complex process. AI agents can quickly produce these types of reports, such as those required for ISO 27001, SOC 2, PCI-DSS, or HIPAA compliance.
• Report on SLAs: Agents can track mean-time to remediation for vulnerabilities by severity and present this in a report for executives or auditors.

This not only eases regulatory burdens but also strengthens security posture.

Benefits of Agentic vulnerability management

The adoption of Agentic vulnerability management delivers benefits that are both operational and strategic.

• Dramatically reduced MTTR: Mean Time to Remediate (MTTR) is one of the most important metrics in cybersecurity. In traditional setups, MTTR for critical vulnerabilities can range from days to weeks. Agentic systems can reduce that to hours or minutes by removing manual bottlenecks.
• Higher accuracy in triage: By incorporating asset context, threat intelligence, and environmental scoping, AI agents reduce false positives and low priority alerts. Analysts no longer need to spend hours chasing vulnerabilities that don’t pose real-world risk.
• Scalability with automation: As infrastructure grows, whether through cloud adoption, M&A activity, or IoT expansion, Agentic AI scales automatically. These tools uplevel existing vulnerability management staff and allow them to fight modern threat actors.
• 24/7 operation: Attackers don't sleep.  Agentic systems operate continuously at all hours of the day. This is crucial for responding quickly to threats that emerge outside of normal business hours.
• Reduced friction between security and IT teams: Agentic AI reduces the number of issues that are forwarded to IT Ops to the ones that are actually critical. Created tickets include all the required information for remediation, including asset details, prioritization reasons, as well as complete remediation steps and code. Platform engineers can ask agents directly if more information is needed.
• Stronger compliance posture: With automated evidence gathering and reporting, organizations can more easily demonstrate compliance with security regulations, often a major pain point during audits.

What are the dangers of Agentic AI?

While Agentic AI brings immense capability, it also introduces new categories of risk. For this reason, many organizations are weary of implementing Agentic AI, and understandably so. Dangers of Agentic AI include:

• Breaking systems: If AI agents have too much freedom, they can apply patches, disable services, or reconfigure systems in ways that disrupt operations or create new vulnerabilities. Example: An agent patches a critical server during peak business hours, accidentally causing downtime.
• Transparency gaps: Executives and security leaders may struggle to understand why an agent made a decision, making it harder to ensure alignment with business risk appetite and compliance obligations. Example: The board asks why a high-profile vulnerability wasn’t patched, but the AI agent’s reasoning is unclear or doesn’t make sense.
• Integration risks: If agentic systems aren’t carefully integrated with existing IT workflows, they may conflict with DevOps pipelines, change management processes, or patch approval workflows. Example: An agent pushes an update that breaks compatibility with an application managed by another team.
• False prioritization: Agents may misinterpret context (or have access to limited data), leading to the wrong vulnerabilities being prioritized, or overlooking risks that carry regulatory or reputational impact. Example: An AI agent patches a low-risk issue quickly, but delays addresWhat are the dangers of Agentic AI?
• sing a compliance-related flaw that later results in a fine.
• Adversarial exploitation: AI-driven systems themselves can be targeted by attackers, either by feeding them misleading data (to wrongly prioritize vulnerabilities) or by directly exploiting the automation layer. Example: A sophisticated attacker manipulates telemetry so the agent ignores a critical vulnerability that they later exploit.

How can you protect against Agentic AI risks?

Although these are all genuine risks that need to be taken seriously, there are many ways to protect against these dangers. Remember that manual systems are also prone to human error, and patches that have been applied by humans can also break things. To minimize the risks of Agentic AI as much as possible, follow these guidelines:

• Set guardrails: Define which actions can be fully automated (e.g. low-risk patches, small configuration updates) versus those requiring human approval. Use a tiered automation model where critical systems always have a human in the loop. Even with high autonomy, humans must remain in the loop for high-impact changes, unusual situations outside predefined parameters, and ongoing policy refinement.
• Prioritize transparency: Choose platforms that provide transparency (such as policy as code) and clear reasoning for every action taken. Ensure reporting ties decisions back to business impact and compliance requirements.
• Embed governance & oversight: Establish policies for change management and exception handling. Regularly audit the AI agent’s decisions and outcomes to catch mistakes early. In certain industries (e.g., healthcare, finance), there are strict rules about who can make system changes. Agentic systems must respect these legal and ethical limits.
• Gradual rollout: Start with less critical assets before extending agentic management to mission-critical infrastructure. Gradually move from semi-autonomous to more autonomous agents. Measure performance against KPIs like MTTR (mean time to remediate) and incident rates.
• Security of the agent Itself
  Ensure that agentic infrastructure is hardened and zero-trust principles are applied. Monitor for tampering or adversarial manipulation attempts.

Getting started with Agentic vulnerability management

It’s certainly not advisable to go from manual processes to full end-to-end automation in one go. Transitioning to Agentic vulnerability management requires planning, governance, and cultural readiness. We recommend following these steps:

1. Start with a pilot

Start with low priority systems, then move on to very specific use cases with human oversight. Monitor results, transparency, and reasoning. If everything is making sense, start expanding scope.

2. Define guardrails early

Decide which actions can be automated without approval and which require human sign-off. At first, you’ll probably want to require human approval for every action. It’s also important to ensure that the Agentic AI system offers rollback procedures when things go wrong.

3. Ensure transparency

Make sure that the agent provides transparency, such as showing remediation code before it’s going to be applied. Verify how well your team can reason with these systems and guide their actions.

4. Upskill your team

With Agentic AI, security teams can evolve from manual actions into orchestrating and strategizing. This means learning how to audit AI decisions, refine policies, and interpret AI-driven analytics. Teams should also understand that agents are not here to take their jobs, but to help them investigate issues and remediate issues faster, so the attack surface can be reduced and burnout can be decreased.

5. Monitor and audit agents

Set up continuous logging, anomaly detection, and periodic penetration testing specifically targeting the agent ecosystem. Simulate misbehavior and attacks to validate guardrails in safe environments.

About Mondoo Agentic Vulnerability Management

Mondoo delivers smart automation with the right level of human control. Mondoo's AI agents continuously monitor your entire IT environment for vulnerabilities, score and prioritize them, decide on remediation steps, semi-autonomously patch issues, and verify success - all with the level of human oversight that you feel comfortable with.

It’s important to understand that AI is only as good as the data it uses. If agents are given insufficient data and insights, decision-making will be hampered and error-prone. Since its foundation, Mondoo has continued to augment and perfect its unified data architecture that collects detailed insights on all cloud and on-prem assets and their configurations, relationships, installed software, exposures, contextual risks, and business-criticality. This comprehensive data is the foundation for Mondoo’s AI agents, allowing them to quickly make accurate decisions on which vulnerabilities pose real risk to the organization.

Beyond detection, prioritization, and guided remediation, Mondoo also offers (semi-) autonomous Agentic vulnerability remediation. With this feature, Mondoo’s remediation agent can automatically generate a pull request in the Mondoo security pipeline to remediate a vulnerability using Ansible. A platform engineer can then review the code and approve it with one click. If Mondoo has fixed a vulnerability that then reoccurs, Mondoo can automatically apply the remediation without requiring any human interaction.

Conclusion

Agentic vulnerability management represents a fundamental shift in how organizations approach cybersecurity risk. By pairing the adaptability and persistence of Agentic AI with well-defined governance, businesses can move from reactive, manual vulnerability handling to proactive, self-correcting protection.

The stakes are high: attackers are faster, infrastructures are more complex, and compliance demands are growing stricter. In this environment, Agentic systems offer a critical force multiplier, reducing exposure windows, scaling effortlessly, and providing the precision needed to focus human attention where it matters most.

The future of vulnerability management will be defined by the balance between autonomy and oversight. The biggest danger isn’t AI itself, but blind trust without governance. Organizations that balance autonomy with accountability will gain the advantages of speed and scale, while minimizing the risks of overreach.

Ready to see what Agentic vulnerability management looks like? Schedule a Mondoo demo.

Agentic Vulnerability Management FAQs

Is Agentic AI secure?

As with all systems, when deploying AI it’s important to use a secure and transparent architecture, enable thorough logging, and monitor events. By restricting agent actions to only what is absolutely necessary for completing assigned tasks, risks can be kept to a minimum. Further guardrails, such as allowing users to interrupt or shut down Agentic AI systems when necessary, and conducting regular audits on the agents and their actions can also build confidence and trust.

What makes Mondoo Agentic vulnerability management different?

Even though vendors may describe seemingly identical processes and technologies, under the hood, there are important differences between each system. There are several factors that make Mondoo stand out from other solutions: (1) Quality of data: Width and depth of Mondoo’s insights on the IT infrastructure, (2) Transparency: Use of Policy as Code and open source technologies such as Ansible and Terraform, (3) Rollback: Remediation pipeline includes versioning and rollback, (4) Guardrails: Granular exceptions, scoping, and human control levels.

What guardrails can be set in place for Agentic AI?

By requiring human approval before tasks are executed, scoping use cases, setting exceptions, and offering rollback options, you can ensure that agents operate safely within set guardrails. These guardrails can be loosened once confidence and trust is built up.

How can I start using Agentic vulnerability management?

It’s best to start with a specific, straightforward use case in a low risk or test environment and then gradually expand. First, agents should require human approval for every task. Then start slowly allowing more autonomy.

Is Agentic vulnerability management already available?

Although some vendors may be touting Agentic vulnerability management without actually having it, Mondoo offers real agentic AI that’s already delivering. Contact us to see a live demo of Mondoo’s Agentic AI in action.

‍

‍