Summary
This white paper presents a framework for secure AI development, addressing the unique security challenges posed by AI systems. Traditional secure development lifecycles are inadequate for AI, which introduces risks such as prompt injection, jailbreaking, and data leakage. The document outlines the adaptation of the Shift-Left security principle to AI development, proposing six AI-specific phases that include data management, isolated model evaluation, system-level validation, and continuous monitoring. It emphasizes the necessity of integrating security from the outset of AI projects to mitigate risks effectively. The paper also discusses the evolving security landscape of AI, highlighting the importance of proactive security measures in response to new vulnerabilities that arise as AI technologies become more prevalent. By detailing a structured approach to secure AI solution delivery, the document aims to assist organizations in aligning their development practices with emerging regulatory requirements and enhancing their overall security posture against AI-related threats.
