Organisations should place AI systems inside the non-human identity inventory and assign each one a clear owner, scope, and offboarding path. If an AI feature can authenticate, call tools, or hold tokens, it needs lifecycle governance. Without that, hidden access paths can outlive visibility and accountability.
Why This Matters for Security Teams
AI systems that can authenticate, call APIs, or hold tokens are not just applications. They are non-human identities with a lifecycle, a scope, and an attack surface. The governance gap appears when teams treat those credentials as implementation details instead of access rights. That is how credentials drift into code, logs, notebooks, and agent toolchains. NHIMG’s research on the Secret Sprawl Challenge shows how quickly hidden credentials become an operational liability, while the OWASP Non-Human Identity Top 10 frames the core failure modes practitioners keep missing.
What matters most is that autonomous or semi-autonomous systems do not behave like human users. They can chain tools, request new access at runtime, and retain secrets long after the original project owner has moved on. Current guidance suggests security teams should manage these systems as first-class NHIs, with ownership, inventory, least privilege, and explicit offboarding. In practice, many security teams encounter credential abuse only after an AI workflow has already been used to expand access or exfiltrate data, rather than through intentional design.
How It Works in Practice
Governance starts with identity, not with the model. If an AI feature can sign in, exchange tokens, or operate tools, it should have a named owner, a documented purpose, and a defined trust boundary. The cleanest control pattern is to issue short-lived credentials per task, then revoke them when the task ends. That reduces the blast radius of token theft and limits the damage from unexpected agent behaviour. The operational model is described well in NHIMG’s Ultimate Guide to NHIs, static vs dynamic secrets.
For teams building agentic systems, static IAM roles are often too blunt. An agent may need calendar access in one workflow, ticketing access in another, and database read access only for a narrow action. Best practice is evolving toward runtime authorisation, where policy evaluates the request context, task intent, data sensitivity, and destination service before granting access. That is why standards bodies now emphasize modern identity and policy controls in the NIST Cybersecurity Framework 2.0 and the NIST SP 800-63 Digital Identity Guidelines.
- Register every credentialed AI system in the NHI inventory.
- Prefer workload identity over shared secrets where possible.
- Issue ephemeral tokens with narrow scope and short TTLs.
- Evaluate access at request time with policy-as-code.
- Revoke credentials automatically when the task, session, or pipeline ends.
Where possible, use cryptographic workload identity as the root of trust for agents, then layer JIT credentials on top for specific actions. These controls tend to break down in legacy environments with shared service accounts, hard-coded secrets, and long-running batch jobs because ownership and revocation cannot be enforced cleanly.
Common Variations and Edge Cases
Tighter credential controls often increase integration overhead, requiring organisations to balance speed of delivery against the cost of more frequent token issuance and policy maintenance. That tradeoff is real, especially in hybrid estates where tooling maturity differs across cloud, SaaS, and on-prem systems. There is no universal standard for this yet, but current guidance suggests that dynamic secrets and workload identity should be the default direction of travel.
One common edge case is an AI assistant embedded inside an existing business application. Teams may assume the parent application’s controls are sufficient, but the agent may still possess separate tokens for search, storage, or external APIs. Another is human-in-the-loop systems, where manual approval does not remove the need for lifecycle controls because the credentials themselves may remain active beyond the approved action. NHIMG’s Top 10 NHI Issues and the Secret Sprawl Challenge both reinforce that visibility fails when secrets are distributed across pipelines, repositories, and runtime tooling.
For organisations adopting agentic ai, the key exception is not whether the system is “intelligent” but whether it can act independently. If it can, then credential governance must follow the same discipline used for privileged workloads, with stronger revocation discipline and clearer accountability than many teams currently apply.
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 | A2 | Agentic systems need runtime access controls and bounded tool use. |
| CSA MAESTRO | TRUST-2 | MAESTRO addresses trust boundaries and identity for autonomous agents. |
| NIST AI RMF | AI RMF governance applies to accountable, controlled AI system lifecycles. |
Assign owned, scoped identities to agents and revoke access automatically after use.
Related resources from NHI Mgmt Group
- How should security teams govern API keys used for generative AI access?
- How do organisations reduce the dwell time of exposed credentials at scale?
- How should organisations stop auto-sync from turning desktops into repositories of credentials?
- How should organisations govern AI systems that can make consequential decisions?
