AI agent security: the attack surface growing faster than the controls

More and more teams are adding AI agents to their operations: to serve customers, to write code, to make automated decisions inside a business process. Few of those teams have a security control designed specifically for these systems. The question that becomes unavoidable is a simple one: who is reviewing the security of the agents that are already in production?

Why does an AI system need its own approach?

The security tools we already know —static code analysis, network scanning, penetration testing— were designed for software that follows fixed rules. An AI agent makes different decisions depending on the context it operates in, so the same flaw can show up in different ways each time.

This opens the door to an audit approach built specifically for this type of system, rather than an adaptation of tools that existed before AI agents reached production.

What does it mean to scan an AI system?

In practice, scanning an AI system means reviewing how it responds to unexpected inputs, how exposed the data it handles is, and whether its decisions can be audited after they have been made. Xcapit's AiSec does this work automatically and backs the results against the main security standards, including ISO 27001.

The part that changes a security team's process the most is that fixing what is found can also be automated, instead of producing a report that someone has to resolve manually weeks later.

Who needs this control today?

Any team that already has AI agents in production, or is about to add them, benefits from this type of control. The sooner it is integrated into the development process, the less work is needed later to fix what was overlooked along the way.

Does your team today have a security control designed specifically for the AI agents already in production?