Why do AI risk controls need to include identity and access management?

Because many AI failures are caused by overbroad access, weak secret handling, and poor runtime visibility rather than model logic alone. IAM controls determine what the system can retrieve, call, or influence. Without those boundaries, risk management becomes advisory only, and the organisation has no practical way to constrain harm when the AI behaves unexpectedly.

Why This Matters for Security Teams

AI risk controls fail when they stop at model behavior and ignore what the system can actually access. An AI system with broad API rights, long-lived secrets, or unchecked tool permissions can cause harm even if the model itself is not compromised. That is why identity and access management belongs inside AI risk management, not beside it. NIST’s NIST AI Risk Management Framework treats governance, mapping, and control as operational duties, while NHIMG research shows how often non-human identity weakness becomes the real failure path in enterprise environments.

For practitioners, the key issue is scope: identity defines what the AI can retrieve, call, modify, or delegate. If that scope is too broad, the organisation is trusting runtime judgment that no model can reliably provide under every prompt, tool chain, or edge case. This is especially important where secrets, service accounts, and machine-to-machine access are reused across systems, because one compromised token can convert a model error into an enterprise incident. The 2024 ESG Report: Managing Non-Human Identities reports that 72% of organisations have experienced or suspect they have experienced a breach of non-human identities. In practice, many security teams encounter the access problem only after the AI has already called the wrong tool, exposed data, or chained a privilege path that nobody had explicitly reviewed.

How It Works in Practice

Effective AI IAM starts by treating the AI agent, workflow, or service as a distinct workload identity rather than as a human user with an assigned role. That means binding actions to cryptographic identity, then constraining each action with least privilege, short-lived credentials, and runtime policy checks. Current guidance suggests using workload identity mechanisms such as SPIFFE/SPIRE or OIDC-issued tokens to prove what the system is, while issuing just-in-time secrets only for the specific task being executed.

In operational terms, the control stack usually includes:

• workload identity for the agent or service, so access is anchored in proof of execution context rather than static accounts
• ephemeral tokens and secrets with tight TTLs, so compromised credentials expire before they can be reused broadly
• policy-as-code evaluation at request time, so the AI’s intended action is checked against context, risk, and sensitivity
• tool-level authorization, so access to retrieval, file systems, ticketing, code, and cloud APIs is separated instead of bundled
• continuous logging and revocation, so abnormal chains of calls can be cut off quickly

NHIMG’s Top 10 NHI Issues and Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs both reinforce the same operational reality: lifecycle management matters as much as provisioning. Secrets must be rotated, revoked, and reissued as the task changes, not left in place because the workload is “trusted.” The OWASP Non-Human Identity Top 10 aligns with this by emphasizing overprivileged machine identities, weak secret hygiene, and missing visibility as recurring control failures. These controls tend to break down when agents are allowed to chain tools across loosely governed systems because the trust boundary becomes the workflow itself, not any single API.

Common Variations and Edge Cases

Tighter IAM often increases operational overhead, requiring organisations to balance stronger containment against delivery speed and integration complexity. That tradeoff becomes sharper in AI systems that span multiple services, vendors, or business units, because every added boundary can create friction for developers and operators. Best practice is evolving, but there is no universal standard for how granular agent permissions should be across long-running, multi-step, autonomous workflows.

Two common edge cases deserve attention. First, human-in-the-loop approval does not replace IAM if the AI can already stage actions, collect data, or prepare downstream calls before approval. Second, long-lived service accounts are especially risky when agents operate unpredictably, because static roles assume stable behaviour that autonomous systems do not reliably exhibit. In those environments, runtime authorisation and short-lived identity are more defensible than pre-approved standing access.

NHIMG’s Ultimate Guide to NHIs — Key Challenges and Risks and the 2024 ESG Report: Managing Non-Human Identities are useful reference points when deciding how much privilege a system really needs versus how much convenience it merely wants. Where teams manage mixed estates of scripts, bots, agents, and legacy integrations, identity design usually becomes inconsistent fastest at the seams between platforms and ownership domains.

Related resources from NHI Mgmt Group

• Why do AI agents create new risk in non-human identity management?
• Why do local AI agents complicate identity and access management?
• Why do AI agents increase non-human identity risk in existing IAM programmes?
• When does just-in-time access reduce risk for agentic AI, and when does it fall short?