AI Governance Reference

Introduction Measuring the behavior of AI systems is a necessary but insufficient step toward effective governance. Organizations also need structured ways to transform observations into decisions and actions. Without clear frameworks, even high-quality signals risk remaining unused or being interpreted inconsistently. This article examines how organizations can move from behavioral measurement to operational governance. It […]

Introduction AI systems in production are not static. Model updates, provider-side changes, modifications to prompts or retrieval sources, shifts in input distribution, routing changes, or adaptive system components can cause observed behavior to evolve over time. Detecting these changes requires more than isolated evaluations or short-term observations. Longitudinal monitoring provides the temporal perspective necessary to

Introduction Effective AI governance in production requires both the ability to detect short-term variations and the capacity to understand longer-term behavioral patterns. Two complementary approaches have emerged to address these needs: weekly barometers and monthly cartographies. While both methods aim to improve the observability and governance of AI systems, they serve different purposes and operate

Introduction In AI governance, stability is often perceived as a positive indicator. However, a model can appear stable while still presenting hidden risks or variations. This phenomenon, known as deceptive stability, can lead to a false sense of security and inadequate risk management. This article defines the concept, explains how to detect it, and discusses

Introduction As AI systems are increasingly used in production environments, the ability to reliably measure their behavior becomes essential. However, measurements that cannot be repeated or verified offer limited value for governance. This is why reproducible AI measurement campaigns are a foundational requirement for any serious approach to AI governance and metrology. This article explores

Introduction As AI systems operate in real-world production environments, the ability to detect and interpret risk signals during or immediately after generation becomes essential. These signals, often referred to as runtime risk signals, provide valuable information about the behavior of AI models while they are actively processing requests. This article explains what runtime risk signals

Introduction Two of the most important behavioral dimensions in AI governance are stability and factual consistency. They are related but distinct, and confusing them leads to incomplete risk assessment. This article clarifies both concepts and explains their role in production environments. Defining Stability Stability refers primarily to the consistency of observable generative behavior across repeated

Introduction One of the most critical distinctions in AI governance is the separation between signal and verdict. Confusing the two leads to over-reliance on tools, blurred responsibility, and increased operational risk. This article defines the concepts, explains why the distinction matters, and shows how to apply it in practice. Defining Signal and Verdict A signal

Introduction Traditional AI observability focuses on system-level metrics such as latency, token usage, error rates, and infrastructure health. While valuable, these approaches provide limited insight into the actual behavior of generative models during or after content generation. As AI systems are deployed in high-stakes production environments, a more precise layer of measurement is required. This

Introduction As generative AI systems move from experimentation to production environments, organizations face a growing challenge: how to reliably monitor, understand, and govern their behavior at scale. Traditional benchmarks provide static snapshots of performance, while classical observability tools focus primarily on infrastructure metrics such as latency, throughput, and cost. Neither approach fully addresses the dynamic,