Should organisations treat AI as an application or as an identity?

Treat it as an identity when the AI can access data, invoke tools, or participate in workflows that affect business systems. That framing makes least privilege, just-in-time access, and lifecycle governance relevant. If you keep treating it only as an application, you will miss the access and delegation behaviours that actually create risk.

Why This Matters for Security Teams

The choice is not semantic. If AI can call APIs, move data, or trigger workflows, it behaves like a delegated actor with its own access path, not a passive application. Treating it only as software leaves gaps in authorization, offboarding, and auditability. That is why NHI governance exists: modern enterprises already manage far more machine identities than human ones, and NHIs outnumber human identities by 25x to 50x according to the Ultimate Guide to NHIs.

Security teams often miss the point when they review AI through an application-security lens alone. An application is deployed, patched, and monitored; an identity is granted, constrained, rotated, and revoked. For autonomous or tool-using AI, the risk is not just code defect or prompt abuse, but misuse of delegated authority. The NIST Cybersecurity Framework 2.0 is helpful here because it pushes teams toward governance, access management, and lifecycle accountability rather than narrow software controls.

In practice, many security teams encounter AI-driven access sprawl only after an agent has already touched production systems or exposed secrets, rather than through intentional identity design.

How It Works in Practice

The practical answer is to treat AI as an identity when it has execution authority, tool access, or delegated business impact. That means assigning a workload identity to the AI, then governing it with the same discipline used for service accounts, API keys, and privileged automation. Current guidance suggests this identity should be short-lived, traceable, and bound to a specific task or context rather than left as a permanent credential holder.

In mature implementations, the AI does not rely on one long-lived secret. Instead, it receives just-in-time credentials, scoped to a single request or workflow, then revoked automatically when the task ends. That reduces the blast radius if the agent is misled, compromised, or over-permissioned. The 52 NHI Breaches Analysis shows why this matters: machine identities are frequently involved in real compromise chains, especially where credentials persist longer than the business need.

Operationally, the better pattern is:

• Use workload identity as the primary binding for the agent, not a shared human credential.
• Issue ephemeral tokens per task, with narrow scopes and strict time-to-live limits.
• Evaluate policy at runtime using context such as request intent, data sensitivity, and tool target.
• Log every delegated action so the identity trail shows what the agent was allowed to do and what it actually did.

This aligns with emerging agentic AI guidance from OWASP Top 10 for LLM Applications and the identity-first direction of SPIFFE, which both reinforce that cryptographic proof of workload identity matters more than static secrets. These controls tend to break down in flat legacy environments because shared service accounts, hard-coded tokens, and coarse RBAC make task-level delegation impossible.

Common Variations and Edge Cases

Tighter identity control often increases operational overhead, requiring organisations to balance security against integration complexity and developer friction. That tradeoff is real, especially when AI is embedded in legacy workflows, vendor platforms, or multi-agent pipelines where the same model may perform multiple roles.

There is no universal standard for this yet. Some teams will keep a monolithic application boundary for low-risk AI that only generates text and never touches systems. Others will split the design, treating the model as an application component but the tool-using runtime as an identity-bearing actor. Best practice is evolving, but the distinction usually comes down to whether the AI can cause side effects.

Edge cases also matter. A model that only drafts email may not need identity treatment. A model that approves purchase orders, rotates secrets, or accesses customer records almost certainly does. The Top 10 NHI Issues and Ultimate Guide to NHIs are useful references for deciding where identity governance must replace simple application monitoring. In practice, the hardest failures appear when teams trust an AI because it is “internal” and then allow it to inherit privileges that no human reviewer would approve.

Related resources from NHI Mgmt Group

• When should organisations treat an AI agent as a privileged system?
• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?
• What is the difference between human identity governance and AI agent governance?