Start by building a register that links each agent to its owner, the identities it uses, the systems it can reach, and the data it can touch. Do not treat model deployment as proof of governance. Discovery must also capture hidden tokens, inherited service accounts, and any workflow that lets an agent act outside direct human review.
Why This Matters for Security Teams
Inventorying AI agents is not a paperwork exercise. Before production access is granted, security teams need to know which autonomous systems exist, who owns them, what identities they use, and how far their execution authority extends. That matters because agents do not behave like fixed applications: they can chain tools, request new permissions, and act on goals in ways that are hard to predict from deployment records alone.
Current guidance suggests treating agents as active NIST AI Risk Management Framework subjects rather than passive software assets. NHIMG research on OWASP NHI Top 10 shows why: organisations often miss the identity layer, the tool layer, and the data layer until an agent has already exceeded its intended scope. That is the real audit problem. If a register only lists model name and environment, it can still leave hidden tokens, inherited service accounts, and delegated workflows completely ungoverned.
In practice, many security teams discover this only after an agent has already touched production systems rather than through intentional inventory design.
How It Works in Practice
A useful agent inventory should be built around behaviour and authority, not just deployment metadata. Start with a record for each agent that captures owner, business purpose, runtime location, workload identity, secrets it can retrieve, systems it can call, and data classes it can reach. Then map the agent’s action path: what it can do on its own, what requires approval, and what happens when it invokes tools, APIs, or other agents.
For autonomous systems, static RBAC is usually too blunt. An agent may need different access for different tasks, so many teams are moving toward intent-based authorisation and policy evaluation at request time. That can mean policy-as-code controls, short-lived JIT credentials, and ephemeral secrets that expire when the task ends. The point is not to trust the model less than the human, but to ensure the agent never has more standing privilege than its current objective requires. This is consistent with the direction of OWASP Agentic AI Top 10 and the CSA MAESTRO agentic AI threat modeling framework.
- Record the agent’s owner, operator, and approving authority.
- List all workload identities, service accounts, and delegated credentials it can use.
- Inventory tools, APIs, MCP connectors, queues, and downstream agents.
- Tag data it can read, write, export, or summarise.
- Define which actions are JIT, which are denied, and which require human review.
NHIMG reporting on the AI LLM hijack breach reinforces the urgency: exposed credentials can be abused almost immediately, which means a weak inventory is also a weak response path. These controls tend to break down in multi-agent pipelines with inherited privileges because one agent’s approved action becomes another agent’s hidden trust path.
Common Variations and Edge Cases
Tighter inventory controls often increase operational overhead, so teams need to balance visibility against delivery speed. That tradeoff is real, especially where agents are spun up for short-lived projects, embedded in developer tooling, or allowed to call external SaaS systems. There is no universal standard for this yet, but best practice is evolving toward lifecycle-linked inventory: agent created, credentials issued, scope approved, activity monitored, and access revoked.
Edge cases matter. An agent that only drafts content may still be high risk if it can read source repositories or customer records. An agent that appears harmless in a test environment may inherit broad permissions in production through a shared service account. Multi-tenant platforms create another blind spot because one workspace’s agent can accidentally inherit another workspace’s trust boundary. For that reason, the register should be refreshed whenever an agent’s model, tools, prompts, connectors, or permission scopes change.
For deeper context on identity sprawl and hidden credentials, see Ultimate Guide to NHIs — Key Challenges and Risks and the OWASP Non-Human Identity Top 10. The practical test is simple: if the team cannot say what an agent can reach without searching logs and tribal knowledge, production access is premature.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agent inventories must capture tool access and autonomous behaviour risks. |
| CSA MAESTRO | M1 | MAESTRO focuses on modelling agent threats, identities, and trust paths. |
| NIST AI RMF | GOVERN | AI RMF governance requires accountability and lifecycle oversight for AI systems. |
Catalogue every agent's tools, data reach, and approval boundaries before production access.
Related resources from NHI Mgmt Group
- How should security teams limit the risk from AI agents that have access to production systems?
- How should security teams govern AI agents that use OAuth access?
- How should security teams govern AI agents that can access enterprise systems?
- How should security teams inventory AI agents across SaaS, cloud, and low-code platforms?
