OWASP: LLM Applications Cybersecurity and Governance Checklist

Summary of https://genai.owasp.org/resource/llm-applications-cybersecurity-and-governance-checklist-english Provides guidance on securing and governing Large Language Models (LLMs) in various organizational contexts. It emphasizes understanding AI risks, establishing comprehensive policies, and incorporating security measures into existing practices. The document aims to assist leaders across multiple sectors in navigating the challenges and opportunities presented by LLMs while safeguarding against potential threats. The checklist helps organizations formulate strategies, improve accuracy, and reduce oversights in their AI adoption journey. It also includes references to external resources like OWASP and MITRE to facilitate a robust cybersecurity plan. Finally, the document highlights the importance of continuous monitoring, testing, and validation of AI systems throughout their lifecycle. Here are five key takeaways regarding LLM AI Security and Governance: AI and LLMs present both opportunities and risks. Organizations face the threat of not using LLM capabilities, such as competitive disadvantage and innovation stagnation, but must also consider the risks of using them. A checklist approach improves strategy and reduces oversights. The OWASP Top 10 for LLM Applications Cybersecurity and Governance Checklist helps leaders understand LLM risks and benefits, focusing on critical areas for defense and protection. This list can help organizations improve defensive techniques and address new threats. AI security and privacy training is essential for all employees. Training should cover the potential consequences of building, buying, or utilizing LLMs, and should be specialized for certain positions. Incorporate LLM security into existing security practices. Integrate the management of AI systems with existing organizational practices, ensuring AI/ML systems follow established privacy, governance, and security practices. Fundamental security principles and an understanding of secure software review, architecture, data governance, and third-party assessments remain crucial. Adopt continuous testing, evaluation, verification, and validation (TEVV). Establish a continuous TEVV process throughout the AI model lifecycle, providing regular executive metrics and updates on AI model functionality, security, reliability, and robustness. Model cards and risk cards increase transparency, accountability, and ethical deployment of LLMs.