Enterprises should threat model AI systems as if they are part of the trust boundary, not outside it. The key question is whether the model can influence authorization, data access, or downstream actions. Security teams should keep identity, permission checks, and policy decisions outside the model, then validate data flows, prompts, and tool access against realistic abuse paths.
How to Frame the Trust Boundary for Model-Driven Actions
Enterprises should treat a model that can act for a user, service, or another model as part of the trust boundary, not as a passive interface. The right framing is not “what did the model intend?” but “what authority did the system expose?” That shifts the analysis toward authorization, data access, delegation, and tool use, which are the points where harm becomes real.
A practical threat model should separate what the model can observe, what it can request, and what it can actually cause to happen. That distinction matters because many failures come from confusing helpful reasoning with safe authority. The model may generate a request, but the enterprise still needs a deterministic control plane that decides whether the request is allowed.
When the system supports on-behalf-of behavior, the enterprise should model the delegated path explicitly, including where consent, impersonation, token exchange, or agent registration occurs. Agentic AI Identity Guide is useful here because it maps how agents acquire and retire authority, which is the same lifecycle pressure point that determines whether an acting system is safely bounded or over-empowered.
What Failure Looks Like in Practice
The main failure mode is not simply prompt injection or a bad output. It is authority leakage, where the model can influence permissions, trigger actions, or reach data that should have remained behind policy enforcement. That can happen through over-broad tool scopes, weak approval boundaries, shared tokens, or a design that lets the model become the de facto decision-maker.
Enterprises should also model the data path as an attack surface. If the model can see sensitive context, retrieve additional records, or pass content into downstream systems, then the threat model must include unintended disclosure, misuse of context, and escalation through chained actions. The right question is whether a compromised or misled model could convert a single bad input into a broader business action.
For that reason, validation should focus on realistic abuse paths, not only happy-path workflows. Threat Modelling AI Agents is directly relevant because it centers trust boundaries, identity maps, and attack paths for agentic systems. If the model can touch tools, the enterprise should test what happens when the model is tricked into selecting the wrong tool, using the wrong context, or repeating an unsafe action at scale.
Controls That Belong Outside the Model
The safest pattern is to keep identity, permission checks, and policy enforcement outside the model. The model can assist with decision support, but the control plane should enforce who the user is, what the system may access, and which action is allowed at runtime. That separation reduces the chance that language generation becomes an implicit authorization engine.
Enterprises should design for narrow, auditable delegation. Tool access should be scoped, token use should be constrained, and any on-behalf-of flow should be explicit about whose authority is being exercised. RFC 8693: OAuth 2.0 Token Exchange matters because it gives a concrete delegation pattern for exchanging authority without collapsing user and agent identity into one ambiguous credential.
For systems that rely on agentic workflows, threat modeling should also include adversarial manipulation of prompts, memory, and tools. MITRE ATLAS adversarial AI threat matrix helps teams map those techniques to concrete test cases, while CSA MAESTRO agentic AI threat modeling framework is useful for structuring the model, multi-agent, and orchestration layers that often fail when authority is too diffuse.
Risk and Threat Considerations
When an AI system can act on behalf of users or other models, the risk is that trust is granted too broadly and the resulting action path becomes hard to bound, detect, or reverse. A compromised prompt, poisoned context, or abused delegation flow can turn an otherwise limited system into a mechanism for unauthorized access, data exposure, or unwanted downstream action.
Failure mechanism: The enterprise lets the model influence access or execution decisions directly, or it allows tool scopes and delegated tokens to be reused beyond the original intent. Once the model’s output becomes a control signal, an attacker only needs to steer the model, not defeat every downstream control.
Impact: That can produce privilege escalation, sensitive data disclosure, fraudulent transactions, lateral movement through connected tools, or actions that appear legitimate because they were executed through an authorized workflow.
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 MITRE ATLAS address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Models acting for users create delegated-authority and privilege-abuse risk. |
| Recommendation — Enforce external authorization checks before any agent action that changes access or data. | ||
| MITRE ATLAS | Adversarial AI threat techniques | Agentic threat modeling must cover prompt, memory, and tool abuse paths. |
| Recommendation — Map agent attack paths to ATLAS techniques and test the corresponding abuse cases. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Delegated AI actions should be constrained to minimum necessary permissions. |
| Recommendation — Restrict agent tool and data permissions to the minimum required for each task. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | Treat model-driven actions as untrusted and verify every request at the boundary. |
| Recommendation — Verify each agent request before granting access or executing an action. | ||
Practitioner Guidance
What to verify: Confirm that the model can never independently grant itself access, widen its tool scope, or bypass a policy decision. If the answer depends on the model’s reasoning to decide who gets access, the design is already too close to the trust boundary.
Decision rule: If a tool or token can trigger production impact, require an external control check that is deterministic, logged, and separable from the model. Treat “the model recommended it” as advisory only, never as authorization.
Practitioner takeaway: Threat modeling should assume the model can be deceived, but the enterprise still owns the authority boundary, so the safest architecture is one where the model suggests and the control plane decides.
