LLM AI Cybersecurity & Governance Checklist

What’s Covered?

The OWASP Top 10 LLM AI Cybersecurity & Governance Checklist (v1.1, April 2024) is a practical guide for organizations planning to use large language models. It brings structure to a messy problem: deploying GenAI systems without opening the door to legal, security, or governance headaches. This isn’t just about best practices—it’s a full-spectrum blueprint across risk, regulation, security, and strategy.

The document is structured around three core ideas:

LLMs introduce a new attack surface—they aren’t deterministic, have porous boundaries, and generate new types of security, privacy, and legal risks.
Many traditional controls still apply, but new techniques like red teaming, model cards, and adversarial testing need to be added to the mix.
Business, legal, security, and engineering must co-own deployment strategy—and that means checklists help keep everyone aligned.

What’s included:

30+ detailed security and governance controls, including threat modeling, adversarial risks, training, privacy, procurement, regulatory assessments, and testing methods.
Clear mapping of risks: hallucinations, jailbreaking, model inversion, intellectual property leaks, and shadow AI.
AI Asset Inventory & Deployment Strategy: guidance to track models, suppliers, training data sources, and permissions.
New tools and standards: TEVV, RAG, Model Cards, and Risk Cards.
Integration guidance with OWASP resources like SAMM, ASVS, CycloneDX, and SCVS—and MITRE tools like ATT&CK, ATLAS, and Caldera.

If you’re building or deploying LLMs, this is one of the most operationally detailed documents you’ll find in the public domain.

💡 Why it matters?

There’s a false sense of security that AI tools are “just APIs”—but LLMs learn from what you give them, remember what they shouldn’t, and generate things you can’t predict. That turns even simple use cases into risk vectors. This checklist helps shift from being reactive to structured, cross-functional AI security planning. It’s the bridge between technical risk and organizational accountability.

What’s Missing?

This document is deeply detailed, but it’s not a framework in the governance sense. It’s a tactical playbook—but lacks a higher-level structure for prioritizing tasks based on organizational maturity or risk appetite. It also doesn’t explain how to apply this across different levels of AI adoption (e.g., pilot vs. enterprise-scale). Without that, smaller orgs may feel overwhelmed.

The legal section is strong but focuses more on U.S. and EU exposure; sector-specific obligations (finance, health, education) are not discussed. Finally, while model and risk cards are mentioned, there’s no reference to conformance assessment, as discussed in ISO 42001 or the EU AI Act.

Best For:

This checklist is made for security leads, tech execs, AI architects, and governance teams working with LLMs in real-world systems. Especially valuable for companies where AI use has scaled quickly without much foundational planning. If your AI deployment is ahead of your AI policies—start here.