AI anomaly detection refers to the use of statistical methods, machine learning, or behavioral baselines to identify deviations from expected patterns within AI systems. These anomalies can indicate a wide range of issues—from model drift and data quality problems to active security threats like adversarial attacks or misuse.
Anomaly detection plays a crucial role in AI governance and runtime protection. It helps organizations:
Anomalies may emerge in different contexts:
Traditional monitoring tools often fall short in detecting these signals, because AI systems behave probabilistically rather than deterministically. Effective AI anomaly detection must incorporate contextual awareness and adapt to dynamic usage patterns.
Key techniques include:
How PointGuard AI Addresses This:
PointGuard AI continuously monitors AI systems for anomalies at the input, output, and behavioral levels. These alerts power notify stakeholders, and trigger automated remediation workflow to investigate resolve AI incidents before they cause harm.
Resources:
IIOT World: AI Anomaly Detection
IBM: What is anomaly detection?
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