Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The standard answer to fragmented AI compliance is a responsibility matrix mapped across the lifecycle. Procurement accountable at intake, legal responsible for regulatory vetting, engineering accountable at implementation, security accountable for monitoring. Every stage has an owner and every function knows its part. ‍ Read that arrangement carefully and the problem is visible inside the solution.

A governance program looks complete until somebody asks it to prove something on a deadline it did not set. A supervisor sends an information request. An underwriter asks for control coverage before binding. A prospect's security team asks how a specific control operated last quarter, and the deal waits on the answer. ‍ Most programs can describe what they do accurately and cannot evidence it inside the window. The difference is not a documentation problem.

Arguments for quantifying cyber risk are abundant and mostly sound. What gets published far less often is a plain account of what a modeled figure does not tell you, which is unfortunate, because stating the limits is more persuasive to a skeptical audience than another argument for the method. ‍ We build these models. What follows is what they cannot do, written plainly, followed by what remains useful once those limits are accepted. ‍

Cross-mapping tables for AI evidence in life sciences already exist and are broadly right. Data integrity practice lines up against data governance requirements, software lifecycle logs against technical documentation and logging, human review checks against human oversight duties, post-market surveillance against post-market monitoring. Build one repository, present it two ways. ‍ All of that is sound and it starts one step too late.

Monitoring an agent tells you what that agent did. Every useful question about a multi-agent deployment concerns what happened between agents, and those are properties of the connections rather than of the participants. A per-agent view records nodes and the problems live on the edges. ‍ The shortfall is not a tooling problem waiting on a product.

An annual quantification gets produced in March and quoted as fact in November. Everyone involved knows the figure has aged and nobody knows by how much, so it keeps being presented with the same confidence it had on the day it was signed off. ‍ The usual framing is that a number decays gradually and needs refreshing more often. The framing is half right and it misleads on the part that matters, because most of the decay does not happen gradually at all. ‍