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LLM AI Cybersecurity & Governance Checklist

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The document outlines the OWASP Top 10 for LLM Applications Cybersecurity and Governance Checklist, aimed at leaders in various fields to protect against risks associated with insecure AI implementations. It emphasizes the importance of leveraging AI for corporate success while also safeguarding against potential threats. The checklist serves as a tool for developing a comprehensive strategy to defend and protect organizations as they navigate the complexities of Large Language Models (LLMs).

Key points covered include the need for responsible and trustworthy artificial intelligence, the challenges posed by LLMs, and the importance of incorporating AI security and governance into existing practices. The checklist contributors are acknowledged, and the document is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Additionally, the document discusses the significance of model cards and risk cards in enhancing transparency, accountability, and ethical deployment of LLMs. Model cards provide standardized documentation on AI systems’ design and constraints, while risk cards address potential negative consequences like biases and security vulnerabilities. These cards play a crucial role in ensuring ethical standards and legal compliance in AI research and deployment.

Furthermore, the document highlights the need for collaboration between IT, security, and legal teams to address legal implications of AI and emphasizes the importance of following best practices, integrating AI security with existing organizational practices, and evaluating policies and protocols to align AI technologies safely and ethically with business processes.

In conclusion, the checklist aims to assist organizations in enhancing their defensive techniques, addressing new threats arising from LLM usage, and fostering continuous improvement through a structured and effective strategy. It underscores the evolving landscape of AI technologies and the necessity for proactive measures to mitigate risks and ensure responsible AI deployment.


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