What are the key AI threat model best practices?

Key AI threat modelling practices are to map the full system, including data sources, prompts, tools, models, outputs, operators, and dependencies. Identify likely abuse paths such as prompt injection, data poisoning, model theft, misuse of tools, and privacy leakage. Revisit the model whenever the architecture, data, or permissions change, and turn findings into control ownership and testing.

What “good” AI threat modelling has to cover

AI threat modelling works best when it treats the system as a full trust boundary, not just a model endpoint. That means mapping the application, data flows, prompts, tools, retrieval layers, operators, model providers, and downstream dependencies together, so you can see where trust is granted, where it can be abused, and where a change in one component changes the security posture of the whole system.

For agentic systems, the attack surface expands quickly because the model can take actions, not just produce text. NHIMG’s Agentic AI Security Guide is useful here because it frames inputs, memory, tools, orchestration, and identity as one connected threat model rather than separate concerns.

That broader map matters because many AI failures are composition failures. Prompt injection, indirect prompt injection, model misuse, tool abuse, and privacy leakage often become exploitable only when the model is allowed to call external systems, retrieve sensitive context, or act with excess permission.

How to identify the abuse paths that matter most

The most useful best practice is to enumerate abuse paths from the attacker’s point of view, then test which ones are realistic in your own architecture. Prompt injection is the obvious starting point, but it is not the only one. Data poisoning can alter training or retrieval outcomes, model theft can expose capabilities or weights, and tool misuse can turn an otherwise safe prompt into a privileged action.

Privacy leakage deserves equal attention because AI systems often combine user input, enterprise context, and external data sources in ways that are hard to reason about after the fact. If the model can surface memorised, retrieved, or indirectly inferred sensitive data, the issue is not just model quality, it is confidentiality and governance failure.

Where threat modelling needs a deeper adversarial lens, MITRE ATLAS remains one of the strongest references for AI attack techniques. The MITRE ATLAS adversarial AI threat matrix gives practitioners a shared language for prompt abuse, poisoning, evasion, extraction, and agentic attack patterns, while CSA MAESTRO agentic AI threat modeling framework is useful when the real problem is multi-agent orchestration, autonomy, and emergent behaviour.

When the model must be revisited, not just documented

AI threat models go stale faster than many traditional application threat models because the security properties shift when prompts, permissions, tools, retrieval sources, model providers, or deployment topology change. A model that looked controlled in staging can become materially different once it is connected to live data, broader tools, or a new agent workflow.

The practical rule is to treat architecture change as a threat-model trigger, not a paperwork trigger. If the system can now call a CRM, send email, create tickets, or query internal knowledge, reassess both the abuse paths and the blast radius of failure. The same applies when a model is retrained, a retrieval corpus changes, or an operator gains the ability to override guardrails.

Governance also matters because findings need an owner. NHIMG’s AI Governance Guide is a strong companion when you need to turn threat-model outputs into named controls, review cadences, and accountable decision-making rather than leaving them as a one-time workshop output.

Risk and Threat Considerations

AI threat models fail when teams stop at the model and ignore the surrounding system, especially the prompts, tools, retrieval paths, and operator privileges that make abuse practical. The biggest exposure is usually not a theoretical model weakness, but a chain where an attacker can influence inputs, shape context, or trigger actions through trusted integrations.

Failure mechanism: Adversaries exploit indirect prompt injection, poisoned data, excessive tool authority, weak segregation between environments, or leaked model and API credentials to move from manipulation to impact.

Impact: The result can be data exfiltration, unauthorised actions, model theft, corrupted outputs, privacy loss, or a wider compromise path if the AI system is allowed to act inside business workflows.

Practitioner Guidance

What to prioritise: Start with the highest-blast-radius paths, not the most visible ones. If the model can access production data, invoke tools, or influence external actions, those paths deserve review before low-impact prompt edge cases.

What to verify: Confirm that each threat-model finding has an owner, a test, and a rollback condition. If you cannot point to the control that reduces the abuse path, the threat model is still descriptive rather than operational.

Common mistake: Teams often over-focus on jailbreak-style prompts and under-focus on permission design. In practice, excessive tool scope, weak data segregation, and unreviewed connectors usually create the most durable risk.

Practitioner takeaway: The best AI threat modelling is iterative and system-level, because AI risk changes whenever the system’s data, permissions, tools, or operators change.

Related resources from NHI Mgmt Group

• Guide: Threat Modelling AI Agents
• What does AI model abuse reveal about the current NHI threat surface?
• What are effective practices for operationalizing NHI threat detection?
• What are the key model explainability best practices for fraud?
• What are the key watermarking best practices for AI content?