AI Safety vs. AI Security: Demystifying the Distinction and Boundaries

AI Safety and AI Security are distinct but interconnected concepts: AI Safety focuses on ensuring AI systems behave as intended and don't cause harm through accidents or design flaws, while AI Security protects AI systems from intentional misuse and adversarial attacks. Understanding this distinction is essential for developing comprehensive risk management strategies.

Security breaches can cause safety failures and vice versa: The paper demonstrates that these two domains are interdependent—a security vulnerability that allows unauthorized access to an AI system could compromise its safety guarantees, and conversely, safety failures can create security vulnerabilities that malicious actors might exploit.

Clear definitions improve policy and research effectiveness: By providing rigorous definitions and delineating precise research boundaries between AI Safety and AI Security, the paper argues that policymakers and researchers can develop more targeted interventions and avoid duplicative or contradictory approaches to managing AI risks.

Cross-disciplinary collaboration is critical for trustworthy AI: The paper emphasizes that clarifying the distinction between these domains fosters better collaboration across different research communities, which is necessary for deploying AI systems that are both safe (accident-resistant) and secure (misuse-resistant) in critical applications like healthcare and autonomous vehicles.