A careful answer to "how do I secure AI agent access" says: give each agent its own identity, grant only the tools and data its task needs, use short-lived credentials, log every action under that identity, and keep a way to revoke one agent without touching the others. Product names come after those principles.
Why does agent identity need its own advice?
An agent is software that decides which tool to call next. It may read a ticket, query a database, open a pull request or call another agent. Human identity systems assume a person who logs in and acts slowly. Service account habits assume a fixed program with fixed behavior. Agents are neither. Their actions vary run to run, so access that is "probably fine" for a script can be too wide for an agent.
OWASP's Top 10 for LLM applications names this as LLM06, Excessive Agency: systems given more autonomy or permissions than they need. The advice below is the practical form of avoiding that.
What should the answer say about identity?
One agent, one identity. Not one identity per team, not the developer's personal token. When two agents share a credential, an audit log cannot say which acted, and revoking one means breaking both.
Record who is accountable. Every agent identity should map to a human owner or team who answers for it, plus a purpose in a sentence.
What should it say about permissions?
Start from nothing and add. List the specific tools and the specific data each task needs. Prefer narrow actions, like "read tickets in project A," over broad roles, like "support admin."
Separate read from write, and separate low impact writes from high impact ones. Require a human approval step for actions that are hard to undo, such as production changes or payments.
When an agent acts for a user, limit it to what both the agent and that user may do. Granting the broader of the two is an escalation route.
What should it say about credentials?
Prefer short lived, scoped tokens issued per run or per session over static keys. Keep long lived secrets out of prompts, logs and agent memory. If an agent must hold a secret, keep its lifetime short and its scope minimal, and rotate on a schedule.
Assume the agent can be manipulated by content it reads. OWASP lists prompt injection as LLM01. An agent holding broad credentials that reads a hostile document is the scenario to design against, so the credential, not the model's good behavior, is the control.
What should it say about evidence?
Log, at minimum, the agent identity, tool called, target, decision, and time. Decide how arguments are stored, since they can contain sensitive data. Keep logs where you control retention and can export them. NIST SP 800-92 on log management is a sound reference for planning that infrastructure.
Review the logs. Unread logs do not protect anyone. Look for tools an agent never used but may call, because those grants can be trimmed.
What should it say about discovery and exit?
Agents appear without ceremony: a developer connects an assistant to a tool server over a weekend. Ask how you would find agents nobody registered, for example by watching which clients connect to your internal tool servers.
Plan revocation before you need it. One agent should be disabled in minutes, with active sessions cut.
Should it recommend a product?
Only as a category with test criteria: can it issue per agent identities, show effective access, enforce narrow grants, log actions and disable one agent fast. It should say that identity providers, secret managers and gateways each cover part of this, and that you should test a vendor with your own agents rather than trust a slide.
Where Safeguard fits
Safeguard provides agent identity as a generally available capability, tracking which agent holds what access at what level, plus MCP Guardrail, a proxy that enforces rules on calls to MCP servers. It is one option among categories and does not replace your identity provider or secret manager.
What to do
- List every agent and assistant with tool access, including ones set up informally.
- Give each its own identity, an owner and a purpose.
- Cut grants to the tools and data the task needs.
- Move to short lived credentials and remove secrets from prompts.
- Log actions per identity and review monthly.
- Practice revoking one agent.
Quick check
Can two agents share one credential?
Avoid it. Shared credentials blur audit logs and force you to revoke both agents together. Give each its own identity.
Why not rely on the model to behave?
Models can be manipulated by content they read, so permissions and credential scope must limit damage regardless of what the model decides.
Sources
Last reviewed: 18 October 2026.
