EviGuard: Machine-Verifiable Evidence Grounding for LLM-Based Industrial Incident Reasoning

Large language models (LLMs) can turn a flood of cross-layer industrial logs into a fluent incident narrative, but a narrative that cites only real, resolvable events can still be wrong in every relation that matters: the login came from a different workstation, the write command occurred after the physical change it supposedly caused, the action fell inside a planned maintenance window, and the controller does not even actuate the affected process. A cited event is not necessarily supporting evidence. When such a narrative drives automated response, the error propagates into isolating the wrong controller or revoking a legitimate operator. We present EviGuard, a system that decides when an LLM’s understanding is trustworthy enough to act on. EviGuard stores auditable cross-layer evidence in a provenance graph, lets the LLM propose only hypotheses, compiles each hypothesis into atomic machine-checkable claims in an Incident Claim Language, and has an ensemble of deterministic verifiers label every claim supported, contradicted, or unknown against the graph—honoring interval time, event-time policy and credential versions, network reachability, and physical control dependencies. A response gate forbids any high-impact action whose critical preconditions are not all supported. On EviCPS-Bench (42 hardware-in-the-loop attack chains, 9600 claim-level labels, $\kappa =0.87$), EviGuard cuts the unsupported-claim rate from 12.6% to 1.7%, raises relation-edge $F_1$ from 0.64 to 0.89, holds prompt-injection success to 0.4%, and executes zero unverified high-impact actions across 3200 response decisions, at a median end-to-end latency of 0.44 s.