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Secure AI applications by extending your existing secure software development process to cover models, training data, pipelines, APIs, and development environments. NIST’s Secure Software Development Framework (SSDF) remains the baseline; its AI-focused SP 800-218A profile adds recommendations for AI model development rather than replacing that baseline. For application threat modeling, OWASP’s 2025 LLM Top 10 offers a practical set of risk categories to consider alongside conventional software threats.
What changes when an application includes AI?
The usual secure-development work still matters: protect code, dependencies, credentials, infrastructure, and user data. AI adds assets and lifecycle steps that may not be captured by a conventional application inventory or threat model, including models, model weights, training datasets, configuration parameters, reward models, and the pipelines and APIs that move or expose them.
NIST SP 800-218A augments the practices and tasks in SSDF 1.1 with recommendations and considerations specific to AI model development. It is intended to complement the baseline, and it is relevant to AI model producers, AI system producers, and organizations acquiring AI systems. See the NIST SP 800-218A publication record.
Which NIST guidance should teams use?
NIST lists SSDF SP 800-218 version 1.1 as final, dated February 3, 2022, and SP 800-218A as final, dated July 26, 2024. The NIST publication listing’s December 17, 2025 entry identifies SP 800-218 Rev. 1, version 1.2 as a draft, not a final replacement. Draft status can change, so consult the NIST SSDF publications listing for the status shown there when you adopt or update your program.
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In practical terms, use SSDF 1.1 for the secure-development baseline and SP 800-218A for AI model development considerations. NIST also provides broader secure product development guidance for teams establishing product-security practices.
Which AI application risks belong in a threat model?
OWASP’s 2025 LLM Top 10 includes the categories below. Treat them as prompts for deployment-specific analysis, not as a claim that every risk is equally likely or exploitable in every system. The attacker’s access, the data and tools available to the model, and the consequences of an output all affect the risk.
| Risk category | What to examine | Threat-model question |
|---|---|---|
| Prompt injection, including direct and indirect forms | Instructions supplied by users or encountered in content the application processes | Could untrusted instructions alter model behavior or affect an action the application takes? |
| Sensitive information disclosure | Information available to the model or application and information returned in responses | Could a response expose sensitive information to someone who should not receive it? |
| Supply-chain risk | Model-related assets and the components, services, and dependencies used to build or operate the system | Which external or internally produced components could affect the system’s security? |
| Data or model poisoning | Training data and model-related assets across their development lifecycle | Who can influence these assets, and how would the team identify an unauthorized or suspicious change? |
These categories are useful starting points, not a complete application threat model. Map each one to the specific assets, access paths, and potential consequences in your product; then decide whether the relevant response is preventive, detective, or evaluative. That distinction is a practical way to organize controls, not a taxonomy attributed to NIST or OWASP.
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1. Identify the assets and their owners
Build an inventory that includes more than the application’s source code. Record models, model APIs, weights, configuration parameters, training datasets, pipelines, development environments, and dependencies. Note which team owns each asset, where it is stored or executed, and which systems or people can access it. Include reward models where the system uses them.
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This inventory gives threat modeling a concrete scope: a team cannot assess access or monitor changes to a model-related asset it does not know exists. NIST’s AI profile discusses monitoring development environments and model-related resources including APIs, weights, configuration parameters, and training datasets.
2. Protect assets with least privilege
Apply least privilege to code and model elements, not just to application accounts. Limit access to models, datasets, pipelines, and related resources to the people and services that need it for a defined task. Review permissions when ownership or responsibilities change, and consider whether build, deployment, and development roles need separate access.
NIST’s SP 800-218A recommendations extend least-privilege protection to AI-related elements, including pipelines and reward models. The profile does not make least privilege a guarantee against every AI risk; it is one control within a broader secure-development process. The SP 800-218A publication provides the detailed recommendations.
3. Monitor development environments and changes
Monitor the components of development environments that host models and related resources. Configure alerts for activity that crosses a defined risk threshold or warrants investigation, and make sure the appropriate team can respond to those alerts. Monitoring should cover meaningful changes and access events for the assets identified in the inventory, rather than treating the model as the only object worth watching.
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Detection does not prevent every unauthorized change, and an alert is useful only if it reaches a team able to investigate it. Set the threshold and response path to fit the environment and the impact of the affected asset; NIST’s profile recommends continuous monitoring and alerts for activity that meets a risk threshold or merits investigation.
4. Assess behavior against the application’s threat model
Evaluate the application’s behavior against the risks that actually apply to its design. For example, examine whether untrusted input can influence model behavior in ways that affect downstream application actions, and whether responses could disclose information that the requesting user should not see. Consider the system’s data, model, integrations, and permissions together: the relevant risk depends on how those parts are connected.
Keep this assessment tied to the threat model and lifecycle. A control that limits access may reduce exposure but does not establish that model behavior is safe in every context; monitoring may reveal suspicious activity but does not prevent it. Use preventive, detective, and evaluative measures as complementary parts of the security process, and revisit the assessment when assets, integrations, or system behavior change.
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Who should use the AI-focused profile?
- AI model producers: Apply the AI-specific development recommendations to the work of creating and maintaining models and their related assets.
- AI system producers: Consider how models are integrated into applications, APIs, pipelines, and development environments, and how those connections affect application risk.
- Acquirers: Use the profile’s scope to frame questions about the development and protection of the AI models and related resources in systems they acquire.
The profile’s relevance across these roles makes it useful beyond teams that train their own models. An organization that integrates or acquires a model still needs to understand the assets and access paths its application depends on.
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