AI Agents & Cybersecurity: The Governance Guide

As a Manager and Information Systems Officer, Guillaume Pommier assists his clients in defining, deploying, and securing their technical infrastructures, as well as in managing digital transformation projects.

Key Takeaways:

• A New Risk Paradigm: The autonomy of AI agents transforms business risks (such as bias and hallucinations) into direct cybersecurity vulnerabilities, making governance essential.
• “Security by Design” as a Prerequisite: Cybersecurity can no longer be an afterthought. It must be integrated from the agent’s conception, following the principle: “security first, autonomy second.”
• Governance is Collaborative: The security of AI agents is not solely the CISO’s concern. It requires close and structured collaboration between the DPO, the CISO, the Chief Data Officer, and the business owners of the agent.
• AI to Secure AI: A future perspective is to use specialized “sentinel agents” to continuously monitor the compliance and security of other agents, turning a risk into a strengthening opportunity.

The advent of agentic AI marks a new era in automation and decision-making. These systems, capable of operating with increasing autonomy, raise complex questions regarding cybersecurity and governance. Business risks, like hallucinations and biases, directly translate into cyber vulnerabilities. For data and AI experts, it is imperative to orchestrate agent security by integrating legal requirements and sustainable management.

AI Governance: The Pillar of Trust in the Agentic AI Ecosystem

The governance of AI systems, and more specifically AI agents, encompasses the processes, policies, and mechanisms that ensure ethical, responsible, and secure operation. This means controlling the risks inherent in their autonomy, particularly those related to cybersecurity. A poorly secured agent is an open door for manipulation or data breaches. The illusion of “risk-free” automation is a trap to avoid: the growing autonomy of agents demands a reinforced framework of trust.

Is yours already in place? How do you assess its robustness against the challenges of agentic AI?

Integrating Cybersecurity from the Design of an AI Agent

The security by design approach is crucial. It is no longer about thinking of cybersecurity after an incident, but about integrating it at every stage of the lifecycle. The logic is clear: security first, autonomy second.

• Threat Analysis and Risk Assessment: Agents access confidential data and make decisions. A comprehensive mapping of potential vulnerabilities (e.g., training data poisoning, guardrail bypassing) is paramount.
  • Have you integrated a mapping of agents and their associated risks into your governance plan?
• Designing Resilient Architectures: This includes the principle of least privilege, network segmentation, and robust authentication mechanisms. It is crucial to question whether an agent needs to access entire inventories or modify prices in real-time.
  • Are your architectures ready to host autonomous agents?
• Data Security: Data quality and integrity are fundamental. Encryption, pseudonymization, and access control techniques are essential to prevent breaches. The case of the “Tea” dating app in the United States, where private messages were shared, is a significant example of the consequences of inadequate cybersecurity. France’s data protection authority (CNIL) emphasizes that data protection and cybersecurity are inseparable.
  • Are you effectively protecting the data your agents handle?
• Supervision and Control Mechanisms: It is crucial to monitor the agent’s behavior, detect anomalies, and have emergency shutdown mechanisms. Guardrails (syntactic, semantic, behavioral, dynamic, and managerial escalation) must be a foundational part of the design.

AI Agents: What is the Legal Framework?

The regulation of agentic AI relies on fundamental texts.

• The GDPR remains the benchmark for any personal data.
• The European AI Act will categorize agents according to their risk level and impose cybersecurity requirements for “high-risk” systems.
• The NIS 2 Directive strengthens cybersecurity obligations.
• The CNIL continues to publish specific recommendations.

Is your legal department fully engaged on these topics?

Orchestration, Documentation, and Maintenance: Keys to Sustainable Security

An agent’s security is not a static state but a dynamic process.

• Security Orchestration: Establish clear governance with defined roles and responsibilities. Security policies specific to agents and integration into DevOps/MLOps processes are essential for secure deployments.
  • Do you have security policies specific to your AI agents?
• Exhaustive Documentation: Every aspect of security must be documented: risk analyses, technical measures, testing procedures. This documentation is crucial for regulatory compliance and auditability.
  • Is your documentation up-to-date for every deployed agent?
• Continuous Maintenance and Monitoring: Threats evolve. The security of agents must be reassessed through audits, penetration tests, and active technological and regulatory watch.
  • How do you ensure the continuous maintenance and improvement of your agents’ security?

Governance Models: Structuring the Cybersecurity Approach for Agentic AI

There is no one-size-fits-all model. Each organization must adapt its operations to its structure and DNA. The key is to foster communication among key stakeholders: the DPO (regulatory), the CISO (security), the Chief Data Officer (data), and the AI agent owner (operational). Through their respective lenses, these four actors can ensure cross-functional compliance and full control over the agents.

Can Agentic AI Strengthen Your Cybersecurity?

Agentic AI offers immense potential. The challenge is to build agents that are not only high-performing but also intrinsically secure.

What if agentic AI became a major asset for cybersecurity? Specialized agents could be developed to continuously assess the security of other agents, ensuring they comply with group guidelines and regulations. These “monitoring agents” could detect abnormal behaviors, data processing drifts, or attempts to bypass guardrails in real-time. This self-monitoring capability could transform risk management and ensure a resolutely secure agentic AI.

This is the condition under which we can build the trust necessary to fully exploit the promises of agentic AI. Are you ready to structure your approach to master these new risks and consider AI as a solution to its own security challenges?