AI Safety for Hardware Observation, Device Command Processing, and External Effect Correlation

Hardware evidence, command preparation, DMA visibility, device consumption, local completion and external effect are distinct events, yet monitoring systems frequently treat a measurement of one as evidence of all. This paper defines evidence-qualified observation-to-effect consistency for processor memory, DMA ownership, descriptor occupancy,firmware epochs, device consumption, hardware telemetry and downstream request transformation. The method reconstructs the exact relationship between a source operation and a later hardware or external effect using three ingredients: generation-qualified identity, measurement coverage, and source dependency. It returns one of three verdicts— CONSISTENT, INCONSISTENT or UNRESOLVED — computed as a Strong-Kleene-style conjunction over mandatory predicates, so that missing telemetry is never treated as a negative observation. These verdicts are observational conclusions, not grants of execution authority or guarantees of delivery. The paper presents a seven-phase,forty-two-stage common workflow, twelve inventive steps, six specialised embodiments, mathematical predicates, failure cases and forty implementation checkpoints, and reports a reproducible synthetic laboratory in Python, Go and JavaScript (48 shared fixtures; 154 test executions, all passing). Expected end-to-end latency is modelled analytically (1.40 ms in an illustrative budget); measured laboratory timings (about 1.0 to 4.5 microseconds per call) cover only in-memory evaluators. All laboratory results are self-authored, not peer reviewed, and not independently or field validated. Keywords: AI safety; hardware observation; operation-to-effect consistency; DMA ownership; descriptor occupancy; firmware epoch; device reset; packet correspondence; hardware telemetry; observation coverage; IOMMU translation; three-valued evidence; completion record; external effect verification; proxy request transformation; source dependency graph; generation-qualified identity; synthetic laboratory reproducibility.