OWASP AI refers to the Open Worldwide Application Security Project's guidance, controls, and research focused on securing artificial intelligence systems. In practice, it helps teams identify and reduce risks in AI models, data, prompts, integrations, and agent behavior, especially where AI components interact with applications, identities, and sensitive information.
What OWASP AI Covers
OWASP AI is best understood as practitioner guidance for securing AI systems end to end. It focuses on the places where models, prompts, data, integrations, and orchestration create security exposure, especially when AI is embedded into real applications and workflows.
The term is also a reminder that “AI security” is not one control problem. A useful programme has to account for model behaviour, input handling, output handling, surrounding application logic, and the trust boundaries that connect AI components to users, systems, and sensitive information.
Why OWASP AI Matters in Security Programmes
OWASP AI matters because AI failures often emerge at the seams between the model and the rest of the stack. A model may be technically sound while the surrounding prompt flow, data access, or integration layer still allows unsafe actions, unintended disclosure, or abusive automation.
That makes OWASP AI useful as a shared language for application security, AI engineering, and governance teams. It helps them discuss concrete weaknesses such as prompt injection, unsafe tool use, insecure integrations, and weak handling of model outputs without collapsing every issue into a generic “AI risk” label. For a broader baseline, OWASP Top 10 remains the best-known starting point for mainstream web application risks.
OWASP’s broader security references are also useful when AI features depend on conventional application controls. OWASP ASVS helps teams anchor AI-enabled applications to testable requirements for authentication, session handling, access control, and validation.
Core Risk Areas in OWASP AI
Most OWASP AI concerns fall into a few recurring patterns. One is input manipulation, where prompts or surrounding data are crafted to steer the system into ignoring instructions or revealing information. Another is over-trust in model output, where downstream systems act on generated content as if it were authoritative.
A second pattern is excessive integration power. When an AI component can call tools, query internal systems, or trigger actions, the security posture depends on the authority granted to that component and the checks around each action. A third is data exposure, especially when training data, retrieval sources, logs, or conversation history contain information that should not be accessible to every user or workflow.
These issues are related, but not identical. Some are application security problems, some are architecture problems, and some are governance problems about who can approve, monitor, and change the AI system’s behaviour.
How Teams Use OWASP AI Practically
Teams usually use OWASP AI as a design and review lens rather than as a single checklist. It can inform secure architecture reviews, threat modelling, red teaming, secure development standards, and release gates for AI features that touch sensitive data or operational systems.
The most effective programmes map OWASP AI concerns to the controls already used in the organisation, so that model testing, prompt handling, logging, access checks, and incident response are not handled as separate silos. Where AI features are built into production software, OWASP ASVS Guide can help translate AI-adjacent concerns into testable application security requirements.
For teams that need a maturity view rather than a pure test checklist, OWASP SAMM provides a way to embed those AI security practices into the software delivery lifecycle.
Risk and Threat Considerations
OWASP AI risks are most serious when AI outputs or actions are treated as trusted automation. In those cases, an attacker does not need to break the model itself, only to manipulate the inputs, context, or tool chain enough to make the system disclose data, take an unsafe action, or amplify an existing application flaw.
Failure mechanism: Prompt injection, tool abuse, weak output validation, and over-privileged integrations can convert a language model from a decision aid into an attack surface that forwards malicious instructions into downstream systems.
Impact: The result can be data leakage, fraudulent actions, privilege misuse, corrupted decisions, or loss of trust in the AI feature and the systems connected to it.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP ASVS, OWASP SAMM and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP ASVS | V6 — Authentication | AI-enabled apps still rely on authentication for users and admin flows |
| V8 — Authorization | AI systems often expose actions and data that need access checks | |
| V16 — Security Logging and Error Handling | AI prompts and tool calls need traceable logs for review and incident response | |
| Recommendation — Verify authentication strength for AI-facing user and admin paths. Enforce authorization checks on every AI-triggered action and data access. Log AI inputs, outputs, and tool actions for security review and detection. | ||
| OWASP SAMM | GOVERN — Governance | OWASP AI use benefits from lifecycle governance and accountable ownership |
| Recommendation — Embed AI security ownership and release gates into the software lifecycle. | ||
| NIST AI RMF | AI Risk Management Framework | OWASP AI concerns map to AI governance, mapping, measurement, and management |
| Recommendation — Use AI RMF functions to structure AI risk identification and control decisions. | ||
Practitioner Guidance
Why practitioners should care: OWASP AI is most useful when it is treated as an operational control lens, not a branding label. The term should drive concrete review of model boundaries, prompt handling, data access, tool permissions, and the reliability of any downstream automation that consumes AI output.
What to watch for: If an AI feature can read sensitive context, call external tools, or trigger business actions, the security review should focus on who can influence its inputs and what it is allowed to do once influenced. That is usually where the highest-value control decisions sit.
Related resources from NHI Mgmt Group
- Should organisations use OWASP, CSA, NIST, or MITRE for AI security?
- What is the difference between MITRE ATLAS and control frameworks like NIST AI RMF or OWASP guidance?
- How should security teams implement the OWASP Top 10 for LLMs in an AI governance programme?
- How do NIST AI RMF and OWASP guidance fit into enterprise AI security?
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Reviewed and updated by the NHIMG editorial team on October 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org