What should organisations do when AI-generated applications handle tokens or privileged access?

Treat those applications as part of the identity security surface. Review token scope, service-account use, database roles, and any code path that can create or consume credentials. If the app touches identity, IAM and PAM teams should validate the control design before release.

Why This Matters for Security Teams

When AI-generated applications can request, store, refresh, or pass along tokens, the application is no longer just code. It becomes an identity-controlled workload with potential access paths into production systems, customer data, and administrative functions. That shifts the question from software quality to trust boundaries, because a flaw in token handling can create privilege misuse even when the application itself appears well contained. The OWASP Non-Human Identity Top 10 is a useful reference point for understanding why machine identities need explicit governance.

Security teams often underestimate how quickly an AI-generated application inherits high-risk behaviour through copied patterns: hard-coded secrets, overbroad scopes, shared service accounts, and automatic credential refresh logic. In an agentic or code-generation context, these issues are not edge cases. They are common failure modes when development speed outruns review discipline. The identity risk is especially sharp where the application can call internal APIs, query databases, or trigger administrative workflows without a human in the loop.

In practice, many security teams encounter token misuse only after an access review, incident response, or breach investigation reveals that the application had far more privilege than anyone intended.

How It Works in Practice

The safest operational model is to treat every AI-generated application that handles credentials as a non-human identity with a defined lifecycle. That means assigning an owner, defining the business purpose, documenting the token issuer, and limiting each token to the minimum scope needed for a single workflow. If the application needs to act on behalf of users, the delegation model should be explicit rather than inferred from code. If it needs direct machine-to-machine access, the service account should be isolated, monitored, and reviewed like any other privileged workload identity.

Control design should cover the full path from secret creation to revocation. That includes how credentials are issued, where they are stored, whether they are rotated automatically, and what telemetry is available when the application uses them. NIST control baselines remain relevant here, especially access control and auditability requirements in NIST SP 800-53 Rev 5 Security and Privacy Controls. For organisations already using formal security management systems, ISO/IEC 27001:2022 Information Security Management provides a governance structure for ownership, review, and exception handling.

• Map each token or service account to a named application purpose and owner.
• Restrict scopes to the smallest practical set of APIs, tables, or actions.
• Separate human admin access from machine access and avoid shared credentials.
• Log token issuance, token use, privilege elevation, and secret rotation events.
• Test failure paths, including expired tokens, revoked secrets, and permission denials.

Where AI-generated code also creates new credentials or modifies access rules, the review scope should expand to include IAM and PAM controls before deployment. These controls tend to break down when the application is deployed into loosely governed multi-cloud environments because identity sprawl, inconsistent logging, and unmanaged secret stores make privilege boundaries hard to enforce.

Common Variations and Edge Cases

Tighter token controls often increase release overhead, requiring organisations to balance developer speed against assurance and revocation discipline. Best practice is evolving for agentic and AI-generated systems, especially where a model writes code that can create identities or call privileged workflows; there is no universal standard for this yet, so policy should be explicit about what the application may and may not do.

One common edge case is a low-risk application that later gains new integrations. A token that was acceptable for read-only reporting can become dangerous once the same application can write records, trigger jobs, or impersonate a user. Another is ephemeral AI-generated tooling used in testing or automation. Temporary does not mean harmless if the tool can reach production secrets or persist credentials in logs, prompts, or cached artefacts.

For teams building agentic workflows, the important distinction is between delegated action and autonomous privilege. If an application can decide when to use a token, that decision point deserves the same scrutiny as a human approval step. The governance model should also reflect the OWASP Non-Human Identity Top 10 guidance on machine identity sprawl and secret lifecycle weaknesses. Organisations that use broad platform roles, shared secrets, or undocumented break-glass access will see the same issue surface repeatedly across different AI-generated applications rather than in just one system.

Related resources from NHI Mgmt Group

• How should organisations handle privileged access when workloads and AI systems are part of the model?
• How should security teams handle risks from AI browser extensions?
• How should security teams govern API keys used for generative AI access?
• What is the difference between protecting applications and protecting access?