AI Agent Security

There are two main things you need to address when securing AI agents: the infrastructure layer and the agent’s behavior at runtime. At the infrastructure layer, agents should run inside TEEs so the data they process and the models they use remain encrypted throughout execution. If an attacker compromises the infrastructure, they won’t be able to read what the agent is doing ot manipulate the model it’s working with. 

Agents also need strict access controls to limit which systems and data they can access, clear policies governing the actions they can autonomously take, and continuous monitoring to detect anomalies.  

Teams should implement a human-in-the-loop strategy to handle high-impact decisions when they arise. Fortanix’s Armet AI provides turnkey agentic AI orchestration with the protection of confidential computing, essentially combining infrastructure-level security with governance controls. 

To fully monitor AI agents in production, you need to be able to see both what they’re doing and the environment they’re running in. You should track agent actions, tool calls, data access patterns and outputs, then log and flag any deviations from expected behavior.  

Similarly, monitor your infrastructure to ensure the environments where agents work haven’t been tampered with, using cryptographic attestation to confirm the execution environment’s integrity. 

Maintaining control over your agents requires a combination of rate limiting and usage quotas, access controls that restrict the systems and data they can access, enforcing policies that block unauthorized actions, and immutable audit logs to record what each agent accessed and did (and, preferably, why).  

It has become a best practice to treat AI agents as first-class identities, just as you would any other team member, and use the same least-privilege and audit principles that apply to human users and applications.

AI agents are useful because they can execute multi-step workflows autonomously, and that applies to cybersecurity and threat intelligence. Today, they’re being deployed for automated penetration testing, vulnerability scanning, and analyst assistance that can launch an initial investigation without human involvement for routine cases. 

As is the case in several industries, agentic AI has significant implications for cybersecurity. An AI with broad access across your systems immediately becomes a high-value target. If an attacker compromises the agent or alters its model, they could theoretically gain control of the entire security operation.  

This is why running agentic security workloads inside trusted execution environments is vital: it ensures operations are protected against infrastructure-level access or manipulation, which is especially important when agents are executing sensitive actions such as key rotation or access revocation.

Governance is crucial for AI agents because they act autonomously, and their actions can have severe consequences. While traditional software operates on deterministic instructions, AI agents reason, plan, and execute multi-step actions, which makes their behavior harder to predict.  

Without proper governance, an agent could access more data than it should or behave in ways that create a liability for the wider organization.  

Standards, meanwhile, are meant to provide a common foundation for what responsible agentic AI should look like, covering areas such as access controls, logging, human oversight, and a security baseline.  

Organizations that establish governance frameworks before agentic AI scales across their operations put themselves in a better position for success. It’s much harder to retroactively apply governance standards to systems already in production.