AI Penetration Testing involves simulating hacker-style attacks to evaluate the security of AI-powered systems—such as machine learning models, large language models (LLMs), applications, and chatbots—to uncover and fix vulnerabilities. Security teams replicate real-world attack techniques to identify weaknesses that could result in unauthorized access, data breaches, or system disruptions. This practice is essential because traditional security tools cannot address AI-specific threats like prompt injection (malicious inputs) or data poisoning (corrupted training data). As AI increasingly handles sensitive information and influences critical business decisions, even minor vulnerabilities can lead to major consequences. Regular AI pentesting enables organizations to safeguard data, maintain reliable AI behavior, and meet emerging compliance standards such as ISO/IEC 42001, which outlines best practices for secure AI system management.