Reality-Graded AI Control Failure Forecast Audit

A forensic-preventive audit for regulated teams shipping LLM features, focused not on taking custody of live incident logs but on forecasting which safeguards will fail next. The customer provides redacted artefacts, policy documents, system prompts, and a small set of synthetic or internally replayed scenarios. AE runs adversarial multi-model debate against the team’s stated controls, classifies likely failure modes using the six-pattern autopsy taxonomy, and—crucially—forces explicit forward predictions about which controls will break, under what conditions, and with what user-visible consequences. Those predictions are then reality-graded against subsequent internal test runs, replay exercises, or future incidents, creating an objective learning loop rather than a one-off retrospective. Output is a control register in AE’s structured constraint language with promotion, demotion, and kill rules, plus a ranked list of brittle assumptions and missing evidence. Delivery can be advisory-first and self-hosted or customer-run to avoid sensitive data transfer, making it more plausible for regulated buyers while using AE’s actual superpower: reality-graded forecasting tied to operational controls.

The best case is that AE occupies a valuable, under-served niche: a self-hosted, reality-graded AI control failure forecasting audit for regulated teams that need measurable assurance rather than generic governance paperwork.

Commander override: KILL. Commander kill: audit product shape rejected; no warm-contact base in target ICP