AI Agent Security: How to Govern the Next Wave of Non-Human Identities

AI agents are leading the second generation of non-human identities and bringing with them a troubling imbalance: they are entering production faster than AI agent security practices can mature.

Operations lacking visibility, clear ownership, or controls are among the alarming risks posed by these autonomous agents, which are capable of integrating with and managing critical systems, data, and applications. These are risks for which conventional safeguards are no longer sufficient.

The figures confirm this: half of all organizations admit to not having implemented governance frameworks capable of ensuring AI agent security, according to a Keyfactor report; and only 28% say they could stop the actions of an out-of-control AI agent before it causes damage.

The security of AI agents cannot be addressed by adding more layers to an already fragmented environment. It requires a converged IAM architecture capable of unifying identity, access, governance, and detection to make AI agents—as a new generation of non-human identities—visible, traceable, and governable. Because the risk is no longer hypothetical: an agent with excessive permissions could access confidential data, perform actions out of context, or interact with critical systems without sufficient oversight. At that point, the key lies not only in restricting the agent, but also in governing its identity.

What Sets AI Agent Security Apart (and Makes It Dangerous)

AI agent security poses clear challenges to the cybersecurity practices used to date. Their dynamism and ability to chain together operations without human supervision mean that controls that used to work (based on more or less defined and predictable interactions and paths) no longer function.

The problem is that AI agent security presents an entirely new landscape. These are systems designed to make decisions independently and instantly, which greatly expand the attack surface and whose conclusions and actions are often opaque and difficult to predict. Added to this are vulnerabilities specific to AI agent security that hackers are already exploiting to their advantage, such as prompt injection or the manipulation of tools and APIs, among many others.

 

How to Govern an AI Agent?

Securing an AI agent involves building an architecture that strikes a balance between autonomy and risk mitigation. To this end, AI agent security must be built into a layered system that includes the following:

• Limits on the agent’s operations, with architectures that confine its actions to a specific, controllable environment. At the same time, mechanisms must be incorporated to prevent uncontrolled access to tools by the agent.
• Continuous visibility across the entire decision-making chain, preventing opacity while facilitating regulatory compliance.
• Identity security, governing permissions and access through IAM tools designed for the dynamic and complex environment involved in AI agent security.

 

Identity as a Safeguard: The Control Needed to Track Every Move of AI Agents

Identity management is crucial for effectively tracking AI agents and curbing suspicious activities before they occur. Thus, a well-designed AI agent security strategy involves integrating these identities into the IAM strategy, ensuring they are visible, governable, and traceable.

Many of the issues surrounding AI agent security stem from identity problems: agents with excessive permissions, poorly managed credentials, a lack of transparency in decision-making and execution chains… Conversely, a well-designed AI agent security strategy based on identity involves:

• Authentication processes for AI agents that withstand attempts at credential theft or impersonation.
• Granular access (only to the necessary resource at the right time), based on context and with authentication for each request.
• Complete, real-time visibility into the AI agent’s actions.

And this is where two key tools come into play: IAM tools that enable a unified identity architecture by design; and Identity Threat Detection and Response (ITDR) solutions, capable of detecting and responding to identity-based attacks.

AI agents are already operating on your network, and their access is just as critical as that of any employee. At Soffid, we unify the management of non-human identities through a single native engine.

See how it works within your own architecture. Make it easy on yourself: let’s talk