Everything Your AI Agent Touches Needs Reassessment Now

Even in today’s zero trust world, the notion of an enterprise attack surface hearkens back to the quaint perimeter security days: attackers on the outside, enterprise assets on the inside. Everything Your AI Agent Touches is an Attack Surface – and What You Can Do About It is a critical concept to consider in this context.

We soon realized that attackers could be anywhere, extending the notion of the attack surface to any endpoint or any piece of software anywhere in the world.

Today’s AI agents upend even this modern attack surface metaphor. Because agents can be autonomous – they can have agency – anything they touch is an attack surface.

To make matters worse, we no longer have the luxury of assuming that agents act on behalf of the human that created or launched them.

Simply strengthening agents’ role-based access control (RBAC), therefore, doesn’t address the risks that autonomous agents introduce into our organizations.

We must rethink the nature of authorization entirely. Instead of RBAC, we need our approach to authorization to be dynamic, specific to the task at hand, and enforced in real-time at runtime.

Why autonomous behavior requires a rethink of authorization

The fundamental problem with autonomous agents is that they can misbehave in unpredictable ways.

Many vendors seek to constrain such misbehavior by restricting agents’ permissions. However, this approach leaves substantial holes in an organization’s threat surface.

Here are three common types of problems that can allow agents to go off the rails:

Shadow agents – given the explosion of tools on the market for building and deploying agents, combined with the simplicity that LLM prompts provide, anyone can put their own agents into production with no approvals, security or governance controls.
Overly broad authorization – even when an organization has approval processes in place, it’s simple for an agent to have more capabilities than the approver realizes. In other words, agents can end up with ‘blank checks’ that give them the thumbs up for unexpected misbehavior, including making API calls, querying data sources, and even delegating work to other agents with different permissions.
Agent drift – AI agents learn as they go and thus their goals may drift from what their operators originally intended. As a result of overly broad authorization, they may gain access to tools or data beyond their original scope and use such access to work toward unintended goals, aka ‘going off the rails.’

The central lesson from these examples is that authorization can no longer be a one-time event. Instead, organizations must put into place technology that continually authorizes agents as they proceed with their tasks.

For such dynamic, purpose-built authorization to work, it’s essential to understand the scope of each agent’s capabilities. In other words, what can agents actually do?

Agents can interact with APIs and MCP servers. They can query data sources directly. They can access and work with any tools or applications available to them, either on the same device as the agent, over the network, or in the cloud.

All of these elements – tools, MCP servers, APIs, and applications – are attack surfaces in their own right. And all of them lead back to the organization’s data, one way or the other.

Trust3 AI: securing every agent and every attack surface

The Trust3 AI approach to securing agents begins with discovery. What agents are in the environment (including shadow agents) and what can they access? What tools can they invoke, and what other agents can they interact with?

The second part of the Trust3 solution is observability. What behavior do agents exhibit as they access tools, MCP servers, APIs, and applications? How do agents consume tokens?

With discovery and observability capabilities in place, Trust3 can then provide the security that agents require by controlling what actions agents may perform, especially in production.

Via access control for both tools and data, Trust 3 continually enforces runtime authorization for agents based upon their intended task and offers a ‘kill switch’ that operators can use to stop a misbehaving agent in its tracks.

This three-pronged approach – agent discovery, observability, and security, or ‘AgentDOS’ – enables the Trust3 platform to provide the continuous, real-time protections that organizations require to avoid the three main challenges with autonomous agents discussed above.

Trust3 thus secures all agents and their attack surfaces, even as agents behave unpredictably in production.

The Intellyx Take

If everything that autonomous agents touch is an attack surface, then do you really want to deploy them into production at all?

This is a question every organization must ask themselves.

The more powerful agents are, the more dangerous they become, and thus the greater the risk to the organization that their misbehavior may lead to some catastrophe.

Nevertheless, organizations have been rushing to deploy increasingly powerful agents, despite numerous horror stories about agents going wild.

This craziness has to stop – either by rethinking agentic strategies altogether, or by revamping how each organization tackles agentic AI governance and security.

For those enterprises committed to agents, investing in the proper tools for ensuring adequate control over agents is absolutely essential.

Such control requires agent authorization that is dynamic, purpose-specific, and enforceable at runtime, in real-time, and implement tools like Trust3 AI to adequately secure and govern autonomous agents across their AI landscape.