Most GRC teams treat AI security as a vendor management question. They ask whether their AI provider is secure. They do not ask whether the way their organization uses AI introduces risk the vendor cannot control.

The OWASP AI Top 10 closes that gap. It is a practitioner-level reference listing the ten most critical security risks for AI applications, organized to inform control design and risk assessment. This guide explains each of the ten risk categories, what they mean for GRC programs, and how to map them to your existing control library.

What the OWASP AI Top 10 Is

The OWASP AI Security Top 10 is published by the Open Web Application Security Project, the same organization behind the Web Application Top 10 that security programs have used for decades. It is not a compliance framework and does not result in certification. It is a risk inventory designed to give practitioners a shared vocabulary for AI-specific threats.

The OWASP AI Top 10 applies to organizations that build AI systems, deploy AI systems, or use AI tools in business processes. That means almost every organization is in scope for at least some of these risks.

GRC professionals use it the same way they use the OWASP Web Application Top 10: as a starting point for risk assessment and a guide for control gap analysis.

In This Post

• What the OWASP AI Top 10 Is
• The 10 Risk Categories
• How to Use the OWASP AI Top 10 in a GRC Program
• Frequently Asked Questions
• Conclusion

The 10 Risk Categories

LLM01: Prompt Injection

Prompt injection occurs when an attacker manipulates the input to an AI system to override its intended behavior, extract protected information, or cause it to take unauthorized actions. Direct prompt injection targets the AI system directly. Indirect prompt injection embeds malicious instructions in data the AI system retrieves and processes.

GRC implication: Input validation and output review controls are required. Define acceptable use boundaries for AI tools that process external content. Audit logging of AI interactions supports forensic investigation when injection attacks succeed.

LLM02: Insecure Output Handling

AI-generated content is used downstream in applications, workflows, and communications without adequate validation or sanitization. When the output contains malicious content, scripts, or false information and is not reviewed before use, it becomes an attack vector or a source of material harm.

GRC implication: Define mandatory human review requirements for AI-generated content used in high-stakes decisions. Document which AI outputs require validation before use and who is responsible for that review.

LLM03: Training Data Poisoning

An attacker corrupts the data used to train or fine-tune an AI model, causing it to produce outputs aligned with the attacker’s objectives rather than the organization’s. Poisoned training data can cause subtle, persistent bias or create backdoor vulnerabilities that are difficult to detect after deployment.

GRC implication: Data governance controls must extend to AI training pipelines. Document data provenance, validation procedures, and integrity checks for any model your organization trains or fine-tunes. Third-party models require supply chain assessment.

LLM04: Model Denial of Service

An attacker floods an AI system with resource-intensive inputs designed to degrade its availability or force unpredictable behavior. Unlike traditional DoS attacks, model DoS exploits the computational characteristics of AI inference rather than network bandwidth.

GRC implication: Rate limiting, resource quotas, and availability monitoring controls apply. AI systems supporting critical business processes require availability commitments with defined recovery procedures.

LLM05: Supply Chain Vulnerabilities

AI systems depend on third-party components: foundation models, fine-tuning pipelines, training datasets, embedding APIs, and plugin ecosystems. Compromises in any of these components can propagate into the organization’s AI environment without direct attack on internal systems.

GRC implication: Third-party risk management (TPRM) must extend to AI supply chain components. Assess foundation model providers, API dependencies, and dataset sources using the same vendor risk framework applied to other critical suppliers. This is one of the highest-priority OWASP AI Top 10 items for GRC teams.

LLM06: Sensitive Information Disclosure

AI models can surface training data, confidential inputs from prior sessions, or personally identifiable information in their outputs. This occurs when models are not adequately constrained from referencing memorized content or when session data is not properly isolated across users.

GRC implication: Data classification controls apply to AI inputs as well as outputs. Define what categories of information may not be submitted to AI systems. For shared AI environments, assess session isolation and data retention policies with the provider.

LLM07: Insecure Plugin Design

AI systems can be extended through plugins and tool integrations that execute code, access external APIs, or modify systems on the AI’s behalf. Insecure plugin design creates attack paths where an AI system can be manipulated into taking unauthorized actions through its extensions rather than its core model.

GRC implication: Plugin and integration approval processes are required. No AI extension should have access to systems or data beyond what the defined use case requires. Principle of least privilege applies to AI tool integrations.

LLM08: Excessive Agency

Agentic AI systems act autonomously on behalf of users or organizations. Excessive agency occurs when an AI system is granted more permissions, capabilities, or autonomy than its intended use requires. This exposes the organization to actions that are difficult or impossible to reverse.

GRC implication: Scope boundaries for every agentic AI system must be defined and documented before deployment. Who approved the scope? What can the system modify or initiate? What requires human review? These are governance questions that must have written answers.

LLM09: Overreliance

Organizations and individuals treat AI output as authoritative without adequate human verification. In high-stakes contexts including legal, medical, financial, and security decisions, overreliance on AI output without validation creates material harm and liability.

GRC implication: Define where AI-assisted decisions require human sign-off before action. Document the review requirement in the relevant process or policy. AI output is evidence, not a decision. The decision belongs to a named individual.

LLM10: Model Theft

Attackers extract model weights, architecture details, or sufficient output to reconstruct a proprietary AI system. This represents both intellectual property loss and a security risk if the extracted model is used to identify vulnerabilities or craft targeted attacks.

GRC implication: Treat AI models as intellectual property assets subject to access control and monitoring. API rate limiting, output monitoring, and access logging support detection. Contractual protections with AI providers should address model confidentiality.

How to Use the OWASP AI Top 10 in a GRC Program

The OWASP AI Top 10 is most useful as a risk assessment input and a control gap analysis tool. Here is the practical workflow:

1. Inventory your AI systems. You cannot assess risks you have not documented.

2. Map each system to relevant OWASP AI Top 10 categories. Not all ten apply to every system. A read-only AI assistant has different exposure than an agentic workflow tool with API access.

3. Identify existing controls. Which of the risks above do your current data governance, TPRM, access control, and monitoring controls already address?

4. Document gaps. Where controls do not exist or do not cover AI-specific failure modes, record the gap and assign ownership.

5. Build AI-specific controls where needed. The OWASP AI Top 10 gives you the risk categories. ISO 42001 Annex A provides the control framework. Your NIST CSF 2.0 program provides the governance structure.

Items 5 (Supply Chain), 8 (Excessive Agency), and 9 (Overreliance) are the highest governance priority for most organizations managing third-party AI tools. These are the risks most likely to be present and least likely to be covered by existing controls.

Frequently Asked Questions

What is the OWASP AI Top 10? The OWASP AI Top 10 is a practitioner risk reference published by the Open Web Application Security Project listing the ten most critical security risks for AI applications. It is used by GRC and security teams to inform risk assessments, control design, and governance decisions for AI systems.

Is the OWASP AI Top 10 a compliance framework? No. It is a risk inventory, not a compliance framework. It does not result in certification and is not referenced by specific regulations as a mandatory standard. GRC teams use it to supplement frameworks like ISO 42001 and NIST CSF 2.0.

How often is the OWASP AI Top 10 updated? OWASP updates the list as the threat landscape evolves. GRC programs should monitor OWASP publications and reassess their AI risk registers when significant updates are released.

Which OWASP AI Top 10 items are most relevant for non-technical GRC teams? Items 5 (Supply Chain Vulnerabilities), 8 (Excessive Agency), and 9 (Overreliance) are the most relevant for governance and compliance teams. They address third-party risk, scope control, and accountability, which are core GRC disciplines applied to AI-specific failure modes.

Conclusion

The OWASP AI Top 10 does not require an AI system to be sophisticated to be dangerous. Prompt injection, overreliance, and excessive agency are risks in the simplest AI deployments. Supply chain vulnerabilities exist in every organization using a foundation model API.

GRC programs that have not incorporated the OWASP AI Top 10 into their AI risk assessment methodology are working with an incomplete risk picture. The framework exists. The risk categories are defined. What remains is mapping them to what your organization is actually running.

Start with the five AI systems your organization relies on most. Map each to the ten risk categories. Identify which controls exist and which do not. That exercise produces the first version of your AI risk register.

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