How to Conduct an AI Vulnerability Assessment: 5 Steps
AI vulnerability assessments identify and remediate risks across the AI lifecycle, using a five-step process to strengthen resilience and protect sensitive data.
Fergal Glynn
Agentic AI systems can perform a wide range of tasks, from booking vacations to responding to customer emails in just a few seconds. However, a lot can go wrong with AI agents. Agentic AI makes decisions and takes action independently, often at superhuman speed.
As enterprises experiment with autonomous copilots and AI agents that can execute tasks across cloud systems, finance apps, and CRMs, the margin for error has never been smaller. Without clear oversight, agentic AI can turn a single misstep into a cascading compliance or data-security incident.
From privacy violations and compliance breaches to security vulnerabilities and loss of control, these systems require a new level of oversight. Agentic AI strategies should always consider risk management. Learn why AI agent risk mitigation is so important, plus five strategies to reduce your risk.
Agentic AI is far more complex than standard AI chatbots, which require the user to take action on the AI’s suggestions. Since agentic solutions act on their own, risk management is a must for several reasons:
Every agentic AI system moves through stages where different risks emerge. Understanding where those risks appear helps teams apply the right controls early.
Recognizing this lifecycle is important, but understanding how to implement practical solutions throughout the enterprise is key. As agentic systems are built and moved into production, how do you help your organization keep them secured and compliant?
The best practices discussed below provide guidance for how to implement structured and repeatable controls to improve oversight and minimize risk as you scale agentic AI throughout the enterprise.
Risk management is particularly challenging with AI agents. Follow these AI agent risk management strategies to protect users and reduce your attack surface.
Building from a structured framework helps your organization scale AI responsibly without reinventing the wheel. Especially if you’re new to agentic AI, start with an established framework for AI risk management.
The NIST AI Risk Management Framework provides a solid foundation for reducing agentic risks. Together with ISO/IEC 23894 (AI risk management) and ISO/IEC 42001 (AI management systems), these frameworks help enterprises align their governance practices with global standards.
Overprivileged agents are one of the fastest ways to introduce vulnerabilities. Give your AI agents the minimum level of access required to perform their tasks. Combine RBAC with real-time monitoring through a Security Information and Event Management (SIEM) system to detect anomalies and unusual behavior from both human and AI users.
Even the most advanced agentic AI needs human oversight. A human-in-the-loop model ensures accountability by validating all critical decisions. Set clear escalation protocols: agents can draft responses or actions, but high-impact or external-facing tasks should always require human confirmation.
This approach aligns well with real-world solutions such as Mindgard’s human-AI collaboration model, which empowers security teams to combine human judgment with AI-driven analysis.
Set up audit trails to record every action your AI agent takes. These logs are invaluable for investigating incidents and proving compliance. If you use a vendor, ensure they also provide unchangeable audit logs to ensure end-to-end traceability.
Before deployment, conduct sandbox testing to identify vulnerabilities safely and securely. After launch, run continuous red teaming exercises to stress-test your AI agent’s decision-making loops and prompt chains.
While continual testing typically requires a significant amount of resources, the right partner can make a substantial difference. Mindgard’s Offensive Security solution provides continuous, adversarial testing to uncover and patch weaknesses before attackers can exploit them.
Agentic AI’s benefits come with tradeoffs, and companies need to address these risks proactively. Autonomous systems require proper risk management to safeguard both users and the organization against data loss and misuse.
As agentic AI evolves, responsible innovation will depend on integrating governance directly into the development pipeline, not bolting it on afterward. To keep innovation moving safely, partner with an AI red teaming provider equipped for agentic systems.
Mindgard’s Offensive Security platform empowers security and compliance teams to monitor and test AI systems in real time. Learn how to innovate without sacrificing safety: Book a Mindgard demo today.
Unlike standard models that only generate outputs when prompted, agentic AI can take actions independently. It can send messages and execute workflows without a human user. This autonomy creates new layers of risk that require continuous monitoring.
Look for signs such as unusual data access or deviations from normal output patterns. Automated anomaly detection tools and immutable logs make it easier to spot and contain compromised agents before they cause more harm.
Continuous testing is a best practice. While security teams might test traditional software quarterly, AI agents evolve dynamically. Every model update or data source change can introduce new risks. Ongoing red teaming and validation cycles are key to maintaining safe performance.
