AI Security encompasses the measures, practices, and technologies used to protect artificial intelligence systems from adversarial attacks, data breaches, unauthorized access, and other security threats. It covers both the security of AI systems themselves and the security implications of AI adoption within organizations.
Key threat categories include: Adversarial Attacks (manipulating AI inputs to cause incorrect outputs), Model Theft (extracting or replicating proprietary AI models), Data Poisoning (corrupting training data to compromise model behavior), Prompt Injection (manipulating AI through crafted inputs), Supply Chain Attacks (compromising AI tools, libraries, or models), and Privacy Attacks (extracting training data or personal information from models).
AI security also addresses organizational risks: Shadow AI usage by employees, data leakage through AI interactions, compliance violations from unmanaged AI, and intellectual property exposure through AI tools.
Security measures include: input validation and sanitization for AI systems, model security testing and red teaming, access controls and authentication for AI platforms, monitoring and logging of AI interactions, DLP controls for AI data flows, vendor security assessment for AI tools, and incident response procedures for AI-specific threats.
