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.
Gartner defines data loss prevention (DLP) as a technical control designed to prevent data loss in order to comply with personal data regulations, prevent unintended disclosure, minimize insider risk and ensure that sensitive data is not overly accessible. DLP controls are typically applied to reduce the data risk for two states of unstructured data: data at rest and data in motion. Depending on the state of the data, DLP applies detective, preventive or corrective controls, including alerting, quarantining, blocking, redaction or access restriction.