Process safety in the era of AI: Assurance, deployment, and measurable risk reduction

Process safety is a fundamental industrial discipline focused on preventing fires, explosions, and the release of hazardous substances. The advent of artificial intelligence (AI) has augmented safety management proficiencies by facilitating the early identification of leakage precursors, alleviating alarm saturation, enhancing incident diagnostics, and bolstering risk-informed maintenance of essential assets. Nonetheless, the implementation of AI in safety-critical contexts remains constrained by issues related to data integrity and provenance, model generalization, system resilience, and cybersecurity vulnerabilities. This review amalgamates recent AI methodologies pertinent to process safety and critically assesses the impediments to their dependable industrial application. Four principal application sectors are scrutinized: (i) Natural Language Processing (NLP) for the analysis of incident and near-miss reports, (ii) multivariate anomaly detection for precursor identification, (iii) adaptive alarm management for dynamic prioritization, and (iv) computer vision for real-time surveillance of hazardous zones and personal protective equipment (PPE) compliance. A structured assurance framework is proposed that encompasses data governance, fault-based validation, model interpretability, compatibility with existing safety systems, and alignment with cybersecurity protocols. A phased deployment roadmap from shadow operation to advisory and controlled execution is delineated, underpinned by performance indicators such as Δ-risk reduction, response latency, false-alarm expense, barrier reliability, and model learning rate. The review further underscores alignment with the United Nations Sustainable Development Goals, particularly SDG 9 and SDG 12, by advocating for safer, more resilient, and sustainable industrial operations.