Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

‍ The distance between what OpenTelemetry was built for and what AI governance is asking of it shows up in a single number. Distributed tracing descends from Dapper, the 2010 Google paper that gave the industry the vocabulary of traces and spans. Dapper sampled one trace in 1,024. That is ample for finding a latency regression, because a regression recurs and the next sample catches it.

A software bill of materials works because software changes through a build. Someone bumps a dependency, the pipeline runs, the manifest updates and a scanner compares the new list against known vulnerabilities. Every part of that loop assumes a rebuild is the thing that changes behavior. ‍ AI systems break that assumption at the point it matters most. Editing a system prompt changes what a model does, swaps no dependency, triggers no build and produces no new manifest.

Human oversight is the only control in an AI program that can stop working while producing exactly the same evidence as when it worked. A failed encryption control throws errors. A monitoring pipeline that breaks stops delivering alerts. A review step that has become a formality still generates approvals, timestamps and sign-offs, and the compliance file looks identical. ‍ The asymmetry makes the design question secondary to the measurement one.

A cyber loss model produces one distribution. How that distribution gets drawn changes what a reader can see in it, and the conventional projection hides the part most decisions depend on. ‍ The two views below contain identical data. One of them is close to unreadable for anything except the extreme tail, and the difference is worth understanding before the next time somebody asks what the number means. ‍

A maturity score answers where a program has been. It reports the state of documented process at the moment somebody assessed it, on a cadence measured in quarters or years, using a scale that describes organization rather than outcome. Every property that makes it useful for planning makes it useless as an early warning. ‍ The interesting question is what a leading indicator would look like instead, and the answer requires separating two problems that get treated as one.

Two teams inventory the same organization and return different numbers. One counts forty-one AI assets, the other counts one hundred and twelve. Neither is wrong, because they counted different things, and nobody had decided what a row represents. ‍ Guidance on building an AI inventory covers which fields a row should carry and skips what a row is. That question determines the count, the risk scores, the regulatory classification and whether two inventories can ever be reconciled.

Gartner predicted in mid-2025 that more than forty percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Treat the figure as a forward-looking estimate rather than a measurement, since canceled projects tend to be quietly renamed, absorbed or left to lapse rather than formally closed. ‍

Quantifying cyber risk in an enterprise IT environment starts from frequency. Incidents of a given type happen at some rate, that rate is observable across enough organizations to be estimated, and severity follows from what was affected. ‍ Operational technology inverts both halves. Frequency data barely exists, and the consequences are already documented in detail by people who have never thought about cyber. Working with that inversion rather than against it is what makes the modeling tractable.

An AI governance framework tells a financial institution to inventory its systems, assess risk and document decisions. Consumer protection law tells it something different and considerably harder, which is that a model unable to produce specific reasons for a credit denial cannot lawfully be used to make one. ‍ That distinction is what separates AI governance in financial services from AI governance generally.