How can organisations tell whether their context model is good enough for agentic AI?

Why This Matters for Security Teams

A context model is only useful if it helps an agent make defensible decisions about identity, access, tool use, and data handling. For agentic ai, that means the model must carry enough structure to explain entitlement boundaries, dependency chains, and compensating controls, not just summarise documents. Guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both point to the same operational issue: if context is incomplete or stale, the system may still produce fluent answers while making unsafe inferences.

This matters because agentic systems do not fail like static applications. They fail by taking actions that are technically authorised but operationally wrong, such as invoking the wrong tool, reusing an overbroad token, or treating an old approval as current. NHIMG’s AI Agents: The New Attack Surface report found that 80% of organisations reported AI agents performing actions beyond their intended scope, which shows how quickly weak context becomes a governance problem. In practice, many security teams discover poor context only after an agent has already crossed an access boundary, rather than through intentional testing of the model’s decision quality.

How It Works in Practice

Organisations should test the context model as a control surface, not as a knowledge base. A strong model links identity, permissions, data classification, business purpose, and runtime constraints into a chain the agent can query before acting. That means the model should answer questions such as: who owns this identity, which permissions are standing versus temporary, which tools are allowed, what data is in scope, and what breaks if access is removed. This is where OWASP NHI Top 10 style thinking becomes practical, because the issue is not only prompt quality but the integrity of the identity and context feeding the agent.

• Test for provenance: can the system show where the context came from and when it was last validated?
• Test for authorisation: does it know whether a token, role, or service account is still valid for the requested action?
• Test for scope: can it distinguish read-only context from context that permits change, deletion, or external calls?
• Test for fallback: if a dependency is missing, does it stop, or does it infer a substitute and continue?

Current guidance suggests the model is good enough only when those answers are consistent across similar prompts, tools, and sessions. Security teams should also align the evaluation with adversarial testing patterns from the MITRE ATLAS adversarial AI threat matrix, because prompt injection, context poisoning, and tool misuse often look like ordinary model drift until they trigger a real action. These controls tend to break down when context is assembled from loosely governed data sources and short-lived identities are not tracked with the same rigour as human access.

Common Variations and Edge Cases

Tighter context controls often increase latency, integration effort, and operational overhead, requiring organisations to balance richer decision support against agent responsiveness. That tradeoff becomes more visible in environments with many ephemeral identities, fast-changing entitlements, or multiple business owners feeding the same model. There is no universal standard for this yet, so the practical test is whether the model can stay correct under change, not just under ideal conditions.

Edge cases often appear when context is incomplete but still plausible. A model may appear “good enough” in a single workflow while failing across tenants, subsidiaries, or data domains where access rules differ. It may also struggle when compensating controls live outside the context store, such as in PAM, ticketing, or approval systems that the agent cannot query. The current best practice is to make those dependencies explicit and to require a refusal or escalation when confidence is low.

This is also where NHIMG’s LLMjacking: How Attackers Hijack AI Using Compromised NHIs becomes relevant, because compromised non-human identities can make bad context look trustworthy to the agent. A context model is not good enough if it cannot distinguish approved operational state from attacker-controlled state, especially where stolen keys, stale tokens, or inherited permissions are involved.

Related resources from NHI Mgmt Group

• How can organisations tell whether their AI security model is actually working?
• How can teams tell whether AI governance is mature enough for agentic workflows?
• How can teams tell whether their AI connectivity model is mature enough?
• How should organisations decide whether existing identity controls are enough for agentic AI?