A control approach that treats each AI prompt or conversation as the unit of risk instead of the application alone. It is especially relevant when the same tool can be used safely in one context and unsafely in another.
Expanded Definition
Interaction-level governance treats each prompt, conversation turn, or agent action as the unit of control, rather than assuming the application is uniformly safe. That matters because the same AI agent, tool, or workflow can be benign in one context and harmful in another, especially when the prompt changes intent, data sensitivity, or execution authority. In NHI and agentic AI governance, this approach sits alongside broader policy enforcement because the risk is often created at the moment of interaction, not at deployment time. It is also where prompt context, tool permissions, and downstream data exposure intersect with identity controls, making it more specific than generic application monitoring.
Definitions vary across vendors, and no single standard governs this yet, but the practical direction is clear: evaluate what the user or agent is trying to do, what data is present, and what tool access is available before allowing the interaction to proceed. That aligns with the broader control logic in the NIST Cybersecurity Framework 2.0, which emphasises risk-based protection and continuous governance.
The most common misapplication is treating prompt filtering as sufficient, which occurs when organisations block a few unsafe words but fail to inspect context, identity, and tool-use authority.
Examples and Use Cases
Implementing interaction-level governance rigorously often introduces latency and review overhead, requiring organisations to weigh safer execution against user experience and automation speed.
- A finance chatbot can answer policy questions for employees, but the same model must be constrained when the prompt asks it to generate payment instructions or expose account details.
- An internal coding agent may be allowed to read repository context, yet blocked from making production changes unless the current interaction is tied to approved change control.
- A support agent using OAuth-connected tools may be permitted to retrieve ticket metadata, while a higher-risk prompt requesting bulk export triggers stricter checks, as discussed in Top 10 NHI Issues.
- A document assistant may summarise public files safely, but if the conversation contains secrets, customer data, or regulated content, the session should shift to tighter controls and logging consistent with the NIST SP 800-53 Rev 5 Security and Privacy Controls.
- A workflow agent may call the same API in two different business processes, but interaction-level policy distinguishes routine lookup from a request that would materially change records or trigger external side effects.
Why It Matters in NHI Security
Interaction-level governance closes a gap that traditional identity controls do not solve on their own: a legitimate NHI can still be misused when the conversation context changes. That is why NHI security teams increasingly pair identity, secret handling, and tool permissions with per-interaction policy checks. In the NHI domain, the relevant danger is not only credential theft but also authorised misuse, over-broad delegation, and unsafe chaining of tools inside a single session. The NHIMG Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs frames this as a lifecycle problem, because control strength must follow the identity through every active use. This also links to audit and governance expectations described in the Ultimate Guide to NHIs — Regulatory and Audit Perspectives.
NHIMG research shows the problem is already widespread: 72% of organisations have experienced or suspect a breach of non-human identities, including 46% confirmed and 26% suspected, which reinforces why per-interaction controls matter when identities and agents are being reused across many contexts. Organisations typically encounter the need for interaction-level governance only after a harmless-looking prompt causes data leakage, tool abuse, or an unauthorised action, at which point the control 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 Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-53 Rev 5 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agentic AI guidance centers on prompt and tool-use abuse at the interaction layer. |
| OWASP Non-Human Identity Top 10 | NHI-06 | NHI controls address misuse of identities, secrets, and delegated access in runtime sessions. |
| NIST CSF 2.0 | PR.AC-3 | Access enforcement and identity governance map to context-aware interaction controls. |
| NIST SP 800-53 Rev 5 | AC-6 | Least privilege is essential when one prompt can trigger different tool privileges. |
| NIST AI RMF | AI RMF addresses contextual risk management across model use and deployment decisions. |
Bind tool calls and outputs to interaction policy checks before the agent executes anything sensitive.
