AI Observability is the practice of instrumenting AI systems and tools to gain comprehensive visibility into how they are being used, what data flows through them, how they perform, and whether they operate within defined governance boundaries.
AI Observability encompasses several dimensions: usage observability (who is using which AI tools, how often, and for what purposes), data observability (what data is being sent to and received from AI services), performance observability (latency, accuracy, and reliability of AI outputs), cost observability (spending across AI services and APIs), compliance observability (adherence to policies and regulations), and security observability (detection of anomalous or risky AI interactions).
Enterprise AI observability platforms typically provide: centralized dashboards showing AI usage across the organization, real-time alerting for policy violations or security incidents, audit logs for compliance reporting, data flow mapping showing how information moves through AI systems, cost tracking and optimization recommendations, and integration with existing SIEM and GRC tools.
Effective AI observability is foundational to AI governance — you cannot govern what you cannot see. Organizations implementing AI governance programs should prioritize observability as a first step.
