The disconnect between where a secret is stored and the organisation’s ability to prove who or what can read it at runtime. When build tools or AI agents can access local credentials, provenance becomes a governance problem, not just a storage problem.
Expanded Definition
A credential provenance gap exists when an organisation can locate a secret but cannot reliably prove its runtime audience, effective readers, or the chain of access that led to exposure. In NHI governance, that gap matters because a stored credential is only part of the risk story; build systems, containers, automation jobs, and AI agents may inherit access through paths that are not obvious in inventory records.
Definitions vary across vendors on whether provenance means ownership history, access lineage, or runtime entitlements, but the practical meaning is consistent: if the organisation cannot answer who or what can read a secret at the moment of use, then control is incomplete. This is especially relevant for ephemeral environments and agentic workflows, where local files, mounted volumes, environment variables, and cached tokens can all become hidden read paths. The OWASP Non-Human Identity Top 10 frames these failures as part of secret handling and identity sprawl risk, while NIST SP 800-63 Digital Identity Guidelines reinforces the need for assurance around authenticators and their use.
The most common misapplication is treating secret storage location as proof of control, which occurs when teams assume a vault entry or encrypted file automatically describes every runtime reader.
Examples and Use Cases
Implementing provenance rigorously often introduces extra telemetry, access tracing, and operational friction, requiring organisations to weigh auditability against deployment speed and developer convenience.
- A CI pipeline injects a cloud API key into a job container, but the platform team cannot prove whether a post-build step, sidecar, or debug shell also read it. The issue is not only where the key lived, but who could consume it during execution, as seen in NHIMG coverage of the CI/CD pipeline exploitation case study.
- An AI coding agent can open local configuration files and prompt caches, creating a hidden read path for secrets that were never intended for model use. That pattern maps to the secret exposure concerns discussed in the Guide to the Secret Sprawl Challenge and is frequently addressed alongside OWASP Non-Human Identity Top 10 guidance.
- A mounted workload credential is rotated in a vault, but an old copy remains readable in a developer workstation cache. The organisation has storage control, yet no defensible provenance chain for runtime readers.
- A secrets manager reports that a token exists, but cannot show whether a build tool, agent, or operator extracted it through an inherited environment variable. This is where provenance becomes a governance question rather than a logging question.
Why It Matters in NHI Security
Credential provenance gaps create blind spots that attackers exploit through automation, especially when secrets are copied into pipelines, agents, and transient compute. NHIMG research on NHI practice maturity found that 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with human IAM efforts, and 23.7% still share secrets through insecure methods such as email or messaging applications. That combination makes provenance failure a common path to secret reuse, lateral movement, and unclear accountability.
For practitioners, the governance task is to establish read-path evidence, not just storage records. That means tracing how secrets flow through build systems, runtime environments, agent tools, and delegated automation, then tying those paths to policy and review. The LLMjacking: How Attackers Hijack AI Using Compromised NHIs research shows how quickly exposed credentials are targeted in the wild, which underscores why provenance must be operationally verifiable, not assumed. Organisations typically encounter this consequence only after a secret is abused in a pipeline or agent workflow, at which point credential provenance becomes operationally unavoidable to address.
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 address the attack and risk surface, while NIST SP 800-63, NIST CSF 2.0, NIST Zero Trust (SP 800-207) 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-02 | Addresses secret handling weaknesses that create unclear runtime access to non-human credentials. |
| NIST SP 800-63 | Defines assurance concepts that inform how strongly credential use can be trusted. | |
| NIST CSF 2.0 | PR.AC-1 | Identity and access control outcomes depend on knowing who or what can access a credential. |
| NIST Zero Trust (SP 800-207) | Zero trust requires continuous verification of access, not assumption based on storage location. | |
| NIST AI RMF | AI risk management requires controlling hidden access paths used by agents and tools. |
Map every secret to its runtime readers and remove any path that cannot be evidenced or reviewed.
