Why AI Agents Need Their Own Identity Rules, Not Just Employee Logins

As businesses increasingly use AI agents to automate tasks and interact with company systems, a new report explains why standard identity management tools are not built to handle them safely. Unlike human employees, AI agents can chain together tasks and choose their own tools on the fly, meaning they can end up doing things far beyond what was originally approved.

The core problem is what researchers call an 'intent to execution gap'. IAM platforms are good at defining who should have access to what, but they cannot see what an agent actually does with that access inside an application. This creates a blind spot researchers describe as 'identity dark matter': agents, credentials and access paths that never show up in central identity records. Without visibility into this hidden layer, a security policy only shows intent, not real world proof that the agent behaved correctly.

The report points to a known industry risk category called 'excessive agency', where an AI agent is given more permissions or autonomy than it needs and ends up acting outside its intended task. Importantly, having risky permissions configured does not automatically mean a business has been harmed; the real risk depends on what the agent can reach and what it actually does at runtime.