Security teams should require certified definitions, approved metrics and policy metadata to be delivered together at runtime. The AI should not be allowed to invent business meaning from raw tables or prompts. Governance should cover lineage, ownership, freshness and allowed use, so meaning and permission stay aligned when the system acts.
Why This Matters for Security Teams
When business meaning is resolved at runtime, the risk is not just weak access control. It is semantic drift, where an AI system interprets a metric, customer segment, or policy rule differently from the business owner who approved it. That creates a governance gap between data access and decision authority, especially when an agent can chain tools, query multiple sources, and act without a human in the loop. Current guidance suggests treating meaning as part of the protected workload context, not as an optional prompt detail. The NIST Cybersecurity Framework 2.0 reinforces that governance, risk, and access control must be coordinated, while NHIMG research on the Top 10 NHI Issues shows how identity and permission failures commonly compound under real-world operational pressure. If runtime meaning is not certified, the system can become technically available and operationally unsafe at the same time. In practice, many security teams discover this only after an AI-generated action has already used the wrong business definition to make a live decision.
How It Works in Practice
The practical control is to bind business definitions, policy metadata, and workload identity into the same runtime transaction. That means the AI does not receive a table or prompt alone. It receives a certified definition package that states what the metric means, who owns it, when it was last approved, which use cases are allowed, and what policy applies to the current request. This is consistent with NHI governance principles in NHIMG’s Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs, where identity, lifecycle, and use constraints must stay aligned.
At runtime, security teams should require:
- Signed or otherwise verified metadata for lineage, ownership, freshness, and approved use.
- Policy evaluation at request time, not just at deployment time, so access can change with context.
- Short-lived credentials or tokens for the AI workload, rather than static secrets that outlive the task.
- Separate controls for data retrieval and semantic authorization, because access to a dataset does not imply permission to interpret it any way the model wants.
For implementation, teams can map the workload to SPIFFE style workload identity or similar cryptographic identity primitives, then enforce policy with a real-time engine such as Open Policy Agent. The objective is to make meaning conditional on context, not assumed from text. NHIMG’s Regulatory and Audit Perspectives also matters here because auditors will ask who approved the definition, how often it is refreshed, and whether the AI could act outside the approved purpose. These controls tend to break down when definitions are stored in ad hoc spreadsheets, copied into prompts, or changed by business users without a corresponding policy refresh because the AI then acts on stale or ungoverned meaning.
Common Variations and Edge Cases
Tighter runtime governance often increases operational overhead, requiring organisations to balance decision safety against release speed and data-team flexibility. That tradeoff becomes more visible when definitions change frequently, such as in pricing, fraud, or customer segmentation workflows. Best practice is evolving here, and there is no universal standard for how much semantic metadata must travel with every model call.
In lower-risk environments, teams may allow cached definitions with a short freshness window, but only if the cache is tied to ownership and revocation. In high-impact use cases, the safer pattern is to block execution when lineage, approval status, or policy metadata is missing. This is especially important when AI systems are allowed to combine multiple sources, because one approved definition can be silently contaminated by another source that has a different business meaning. NHIMG’s Ultimate Guide to NHIs — What are Non-Human Identities is useful for framing why the workload itself must be governed as an identity-bearing actor, not just as software. If the runtime cannot prove which approved meaning it used, the safer answer is to stop the action rather than let the model infer business truth from raw data alone.
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 and CSA MAESTRO address the attack and risk surface, while 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 | Runtime definitions need freshness and rotation discipline for governed AI access. |
| CSA MAESTRO | GOV-2 | Covers governance of agentic decisions using approved context and policy. |
| NIST AI RMF | GOVERN | Requires accountable, traceable governance for AI systems using runtime meaning. |
Ensure semantic metadata and credentials are refreshed, revoked, and revalidated before each privileged AI action.
Related resources from NHI Mgmt Group
- How should security teams govern metadata for AI systems that retrieve and act on data?
- How should security teams govern API keys used for generative AI access?
- How should security teams govern AI agents that rely on shared runtime credentials?
- How should security teams prepare AI systems for the first audit?
