Securing an AI agent is above all a matter of good knowledge of the risks, combined with healthy digital hygiene.
Whether designed natively, with SDKs or in a digital suite, AI agents pose new risks for IT. While they provide a definite gain in time and productivity for the best use cases, agentic systems extend the attack surface even further. By their very design and depending on their degree of autonomy, agents can cause real damage to the systems or databases with which they interact.
Autonomy becomes vulnerability
If it is the heart of efficiency and cost gains, autonomy is also the main cause of agent failure. By eliminating human feedback, agents adjust their decisions independently. This absence of human supervision creates a blind spot: the agent can optimize its actions according to its own metrics. Metrics that can gradually diverge from initial objectives, without any warning signals being raised. Worse still, the agent being controlled directly by an LLM, the latter can start to hallucinate. Autonomy then causes cascading errors, where each erroneous decision fuels the next without any external correction mechanism intervening.
In a multi-agent system, risk multiplies exponentially. “In a system where several agents collaborate, each will transmit their access rights to the next to enable them to accomplish their task,” recalls Françoise Soulie-Fogelman, pioneer of neural networks in France and scientific manager of the France AI Hub. A successive propagation which will create an escalation of privileges uncontrollable: the final agent finds himself omnipotent with all access rights.
Finally, the interconnection of agents with external systems API, MCP, databases or websites, drastically increases the attack surface. Each connection, each integration with a third-party tool is a new door that hackers can try to exploit. “When you have an agent who fetches information on the Internet, who interacts with your internal systems, who can execute code, you create so many potential attack vectors,” insists Françoise Soulie-Fogelman. Connections not only increase the risk of prompt injection or data poisoning, but also the risk of importing malicious scripts via unverified connectors.
Limit risks in 4 steps
Unfortunately, as in all information systems, safe and foolproof security solutions do not exist. If it is possible to use a verification agent which ensures the proper functioning of the system and limits deviations, the latter will also be fallible (especially as it adds another layer of complexity). But to drastically limit risks, it is possible to act on 4 pillars: risk identification, compartmentalization, logging and auditing, in addition to monitoring.
1.Map the risks
Françoise Soulie-Fogelman reminds us: it is the main lever to limit risks. “Before deploying an agent, it is absolutely necessary to map out what it will do: what systems it accesses, what data it manipulates, what actions it can undertake.” The mapping must be exhaustive and documented: list all the tools and APIs to which the agent connects, identify the sensitive data it processes, evaluate the potential consequences of each action it can execute. “It’s tedious but essential work, because you can’t protect what you don’t know,” explains the researcher.
2. Compartmentalize agents, restrict access
The principle of least privilege must, more than ever, apply to agents. Each agent must only have the rights and tools strictly necessary for their mission, without the possibility of expanding their scope of action. A restriction that must also apply to timed access: permissions must be revoked automatically once the agent’s task is completed. It is also possible to apply whitelists of authorized websites (those controlled by the company, for example).
3.Log all actions
Each decision, each request, each modification made by an agent must be traced and timestamped in detailed logs. This traceability not only makes it possible to trace the chain of events in the event of an incident, but it also responds (in part) to regulations. Depending on the level of criticality of the AI, the AI Act requires, in fact, “the automatic recording of events throughout the lifespan of the system”. Finally, logging also makes it possible to improve the general behavior of the agent by optimizing the system (or the model) based on log analysis.
4.Audit models, monitor drift in production
But mitigating risk doesn’t stop at deployment. Agents must be continuously monitored to detect any behavioral drift: unusual responses, abnormal execution times, access to unexpected resources. Real-time monitoring, coupled with defined alert thresholds, makes it possible to automatically interrupt an agent who deviates from their planned behavior.
Finally, if the agent is critical, using human validation is essential. “In all critical applications, autonomy will be the red flag,” warns Françoise Soulie-Fogelman. Before deploying an autonomous agent, ask yourself: what is the real impact if the agent makes a mistake or is compromised? If the answer involves serious consequences, always keep a human in the decision loop. The rule is simple: the higher the risk, the more non-negotiable human supervision becomes.
