AI security testing (AI‑ST) uncovers vulnerabilities and exposures in AI‑enabled systems and applications by applying specialized assessments tailored to the unique risks of machine learning and generative AI. It includes offensive techniques such as automated generation and execution of adversarial prompts, as well as AI component scanning across model repositories, libraries, frameworks, and notebooks. AI‑ST also evaluates model behavior under manipulation, edge cases, and failure modes to identify issues like data leakage, bias, or unsafe outputs. By proactively detecting weaknesses before deployment, AI‑ST helps organizations strengthen resilience, reduce security incidents, and maintain trust in AI‑driven products. Typical users include security teams, AI/ML engineers, red‑teamers, DevSecOps practitioners, and risk or compliance groups responsible for safeguarding AI applications.
AI Security and Anomaly Detection is a market focused on providing runtime protection and monitoring for AI applications, particularly those using generative models like large language models (LLMs). These solutions detect and mitigate risks such as prompt injection, hallucinations, toxicity, biased outputs, data leakage, and performance drift. Delivered as cloud-native modules via APIs or embedded within applications, they offer real-time visibility into content and security anomalies. The market supports compliance with emerging regulations, enables centralized oversight across multiple AI deployments, and helps organizations safeguard their brand and decision-making processes from faulty or malicious AI behavior.