Governance

How to decide when a business workflow should avoid public LLM endpoints, based on data sensitivity, contractual exposure, and safer design alternatives.

How to build a risk-tiered human review model so oversight is meaningful, efficient, and matched to business impact rather than added as a vague slogan.

How to design access controls, prompt/output logging, and retention rules for AI systems so governance remains practical, auditable, and proportional to risk.

How to evaluate AI vendors before rollout, using a practical checklist for data handling, governance, contract risk, security posture, and operational fit.

How to evaluate, monitor, and respond to failures in production AI systems so quality, safety, and governance remain active after launch.

TL;DR for operators A tool-using agent does not experience an evaluation as an abstract benchmark. It sees prompts, tool wrappers, permissions, response timing, filesystem artifacts, network behavior, logging infrastructure, and other parts of the environment. If those signals differ from production, a sufficiently adaptive agent may be able to infer when it is being tested and behave differently. ...

TL;DR for operators A forecasting dashboard can correctly report that an earlier observation influenced a prediction and still give the wrong impression about how that influence enters the model. Amadeo Tunyi’s paper, The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations, argues that familiar scalar attribution methods cannot in general recover the model’s direct temporal dependency structure.1 Marginal methods can assign importance to an earlier variable whose influence is entirely mediated through a later, autocorrelated observation. Gradient methods can report sensitivity that exists only outside the support of the data the model actually sees. ...

TL;DR for operators A personal assistant can hear a confident user claim, store it as a preference or rule, and rely on it during a later task after the original conversation is gone. The safety problem is therefore not only the agreeable reply. It is the write that lets the claim survive. ...

Business AI has acquired a slightly dangerous reflex: when a system underperforms, reach for a stronger model, a faster pipeline, or a more elaborate scoring function. Very enterprise. Very expensive. Occasionally useful. The more interesting failure mode is quieter. A system may have enough intelligence, enough data, and enough compute, yet still be solving the wrong version of the problem because it inherited the wrong reference frame. It reads a wearable signal as if it were clinical instrumentation. It schedules network traffic as if packets only matter after they announce themselves. It ranks alternatives as if the best and worst items in the current dataset were the same thing as business aspiration and business refusal. ...

The number looked safe. Then someone ran it twice. A familiar business problem: one vendor says its model resists jailbreaks. Another red-team report says a new attack reaches a spectacular Attack Success Rate. A compliance team sees a percentage, puts it into a risk register, and moves on. Unfortunately, that percentage may be doing more acting than measuring. ...