What is Anomaly Detection?

What is Anomaly Detection?

Anomaly detection in AI agent security uses statistical and machine learning methods to identify behaviors that deviate from normal patterns. Unlike signature-based detection that looks for known attack patterns, anomaly detection can identify novel threats by recognizing that something unusual is happening—even if the specific technique hasn't been seen before. This includes unusual action sequences, abnormal timing patterns, atypical data access, unexpected tool usage, and other deviations from established behavioral baselines.

How Anomaly Detection Works

Anomaly detection systems first learn what normal behavior looks like by analyzing historical data and building statistical models. These models capture typical patterns: how often actions occur, what sequences are common, what resources are normally accessed, and what the usual timing patterns are. During operation, the system continuously compares current behavior against these models, calculating anomaly scores for each observation. When scores exceed thresholds, alerts are generated. Advanced systems use ensemble methods combining multiple detection approaches, and adapt their baselines over time to account for legitimate behavioral evolution.

Why Anomaly Detection Matters

Attackers constantly develop new techniques, and signature-based detection can only catch known attacks. Anomaly detection provides defense against zero-day attacks and novel threat techniques by recognizing that behavior is unusual, even without knowing the specific attack. For AI agents, this is particularly valuable because the attack surface is new and evolving—anomaly detection can catch attacks that security teams haven't anticipated yet. It also helps identify bugs, misconfigurations, and other non-malicious issues that cause unexpected behavior.

Examples of Anomaly Detection

Key Takeaways

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