AI governance
NIST sets ground rules for AI agent identity.
NIST published ten design tenets for managing the identities of AI agents, drawn from public comments on its agent identity and authorization project.
What it is
A starting point for how agents should be identified and authorized. Each agent gets its own identity, separate from the people who use it. Access is granted when a task starts and expires, instead of living in long-lived keys. The system that decides what an agent is allowed to do sits outside the AI model, and agent activity is monitored for unusual behavior.
Why it matters for risk
An AI model can be talked into things. Content hidden in an email or a document can change what an agent tries to do, which is known as prompt injection. If the agent runs on a person's account or a shared key, it can do anything that account can, and the logs can't tell you whether a person or the agent acted. Separating "what the agent wants" from "what it is allowed" is what keeps a manipulated agent contained.
Potential impact
- Data leaves through a helper. An agent with broad mailbox or file access is tricked into summarizing and sending sensitive data outside the company.
- Payment fraud without a person in the loop. An accounts payable agent reads a supplier email with hidden instructions and changes the bank details on an invoice. The money is gone before anyone looks.
- No clean way to stop it. If agents share credentials with people or with each other, cutting off one agent means disrupting the business.
- No answer for the auditor. Without a separate identity and logs, you can't show who did what, which matters for incident response, insurance claims and regulators.
Questions to ask your team
- How many AI agents and connectors are running in our environment, and who owns each one?
- Does each agent have its own identity, or is it using a person's account or a shared key?
- If one agent misbehaved today, how quickly could we shut off just that agent?