They treat secrets as configuration details instead of privileged identities with lifecycles. If a token, API key, or certificate leaks into code, logs, or a repository, the response should include rotation, revocation, and scope review. Waiting for a later cleanup leaves the exposure window open.
Why This Matters for Security Teams
AI-built applications tend to multiply secrets quickly because developers wire together model providers, vector stores, internal APIs, and automation tooling in the same delivery cycle. That creates a blind spot: a token or certificate is often treated as disposable config rather than a credential with access scope, audit requirements, and revocation needs. Current guidance from the OWASP Non-Human Identity Top 10 is clear that machine credentials deserve the same rigor as human identities.
The practical risk is not only exposure in source control. Secrets are also commonly embedded in build logs, CI variables, model orchestration workflows, and agent tool calls, where they can be reused long after the original deployment changed. For AI-built apps, the blast radius can extend into inference services and downstream integrations if one credential is overprivileged or shared across environments. In practice, many security teams encounter secret sprawl only after an incident review, rather than through intentional lifecycle governance.
How It Works in Practice
Teams get better outcomes when they manage secrets as part of identity and access control, not as a developer convenience. That means every secret should have an owner, a purpose, a rotation path, and a clear revocation trigger. It also means separating build-time, runtime, and agent-time credentials so one compromise does not expose the whole application estate. NIST’s security control catalog is useful here because it anchors secret handling in access control, configuration management, and auditability rather than ad hoc practices.
In AI-built applications, the lifecycle has to cover more than storage. A secret may be used by a deployment pipeline, a retrieval layer, an LLM gateway, or an autonomous agent that calls external tools. Good practice is to minimise scope, issue separate credentials per workload, and instrument access so unusual use is visible in logs and alerting.
- Store secrets in a dedicated secrets manager, not in code or prompt templates.
- Issue distinct credentials per environment, service, and agent where possible.
- Rotate on a schedule and immediately after suspected exposure.
- Limit permissions to the minimum API actions required.
- Trace secret usage through CI/CD, runtime, and agent activity logs.
For AI delivery pipelines, OWASP guidance on application and machine identity, plus NIST’s control families, both point to the same operational reality: the safest secret is the one that is short-lived, narrowly scoped, and observable. These controls tend to break down when legacy integrations force shared credentials across production, staging, and developer environments because revocation then becomes disruptive and slow.
Common Variations and Edge Cases
Tighter secret controls often increase delivery overhead, requiring organisations to balance release speed against reduced exposure. That tradeoff is especially visible in AI-built apps that rely on rapid experimentation, external model APIs, or multiple service accounts. Best practice is evolving for agentic systems, but one point is stable: if an agent can act autonomously, its credentials should be treated as production privilege, not temporary lab access.
There are also cases where a secret is technically unavoidable, such as vendor API keys, signing certificates, or legacy service credentials that cannot yet be replaced with federated identity. In those environments, guidance suggests compensating controls: short expiration windows, strong monitoring, per-purpose credentials, and rapid rollback plans. If secrets are injected into prompts or retrieved dynamically, teams should also validate output paths so the model cannot echo or transform sensitive material into logs, traces, or user-visible responses.
The main edge case is shared infrastructure. In multi-tenant platforms, containerised workloads, or fast-moving agent workflows, one leaked credential can become a cross-service pivot point. That is where NIST AI Risk Management Framework thinking helps: treat secret exposure as a model and system risk, not just an application bug. Where agents can call tools directly, the OWASP Non-Human Identity Top 10 also reinforces the need for lifecycle ownership, scoped privilege, and revocation discipline.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Secrets are machine identities and need lifecycle governance. |
| NIST CSF 2.0 | PR.AC-1 | Secret misuse is fundamentally an access control and privilege issue. |
| NIST AI RMF | GOVERN | AI-built apps need governance over secret handling across the AI lifecycle. |
| OWASP Agentic AI Top 10 | LLM05 | Agent tool access can expose credentials through prompts and actions. |
| MITRE ATLAS | AML.T0001 | Prompt or workflow abuse can surface secrets through AI system interactions. |
Prevent agents from handling secrets unless scope, logging, and safeguards are explicit.
