Digitalization of Industrial Risk Analysis Using a Deterministic AI-Augmented Engine: A Field Engineer's Approach

Abstract—Industrial physical-security and site-survey risk analysesstill rely heavily on manual procedures: spreadsheets, personalinterpretation of ISO/IEC-aligned practice (notably ISO 31000 /ISO/IEC 27005 frames and IEC/EN 62676-4 for CCTV), and narrativereports that are slow, expensive, and difficult to audit. Two equallyexperienced engineers may produce inconsistent recommendationsfrom the same facts, and economic justification (CAPEX, OPEX, ROI)often drifts from dimensioned bill-of-materials evidence. Thistechnical note documents how a field electronics engineer with morethan forty years of experience—age seventy-two at the time of thework, without formal programming or AI education—specified andsteered a Python-based prototype, RAP/RAE (Risk Analysis Platform /Risk Analysis Engine). The engine digitizes an end-to-endrisk-to-investment workflow as sequential modules M0–M8,producing auditable artefacts (BOM, CAPEX/OPEX, scenarioranking, and HOLD / INVEST / CONDITIONAL dictamina). Thedesign principle is: AI does not decide the risk. The engine decides. AItranslates and explains. LLM assistance is confined to edges; riskscoring, control recommendation, technical dimensioning, andeconomic gates remain deterministic. The contribution shows that asenior domain expert can digitize a complex engineering procedure bycommissioning AI with precise specifications, while deterministicgovernance keeps AI from substituting for engineering judgment.