Model Monitoring

Once an AI model is deployed, performance doesn’t remain static. Real-world conditions shift—user behavior evolves, data inputs change, and unexpected edge cases emerge. Without robust monitoring, these changes can silently degrade accuracy, introduce bias, or create risk.

Model monitoring encompasses:

• Performance tracking: Observing key metrics such as accuracy, precision, recall, latency, and throughput
• Drift detection: Identifying shifts in input data (data drift) or changes in model behavior (concept drift)
• Error and anomaly alerts: Triggering warnings when outputs become unreliable or inconsistent
• Behavior logging: Capturing prompts, responses, and decision logic for auditability
• Governance and compliance: Ensuring continued alignment with regulatory frameworks or internal policies

Monitoring is especially critical for large language models (LLMs) and generative AI systems, which can behave unpredictably or degrade due to dynamic context.

Without proper visibility, organizations risk delivering poor results, introducing unintended bias, or failing compliance audits.

How PointGuard AI Helps
PointGuard delivers continuous model monitoring across hosted and open-source environments. It captures runtime behavior, detects drift and anomalies, and provides explainable risk scoring. Events are logged and visualized for engineering, GRC, and security teams—making it easier to detect issues and enforce policies proactively.
Learn more: https://www.pointguardai.com/ai-runtime-defense

References:

Comet.ml: Introduction to Model Monitoring

Evidently AI: Model monitoring for ML in production