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Conference Proceedings

International Mine Health & Safety Conference Proceeding 2026

Conference Proceedings

International Mine Health & Safety Conference Proceeding 2026

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Closing the critical control gap – accelerating performance standards and verification quality for principal hazards using generative AI

Critical control management (CCM) is intended to focus operational attention on the few controls that prevent or mitigate material unwanted events. In mining, this approach is closely linked with principal hazard management plans (PHMPs), bow ties, trigger action response plans, standards, inspections and frontline supervision. The International Council on Mining and Metals (ICMM) describes CCM as a cycle of identifying, defining, implementing, verifying and improving controls (ICMM, 2015). The practical challenge is that many operations hold credible hazard documentation but struggle to translate barrier intent into observable, testable and repeatable verification tasks. When performance standards are ambiguous, verification can become a count of completed forms rather than a test of whether the control would function under credible operating conditions. This creates false assurance, weakens learning and delays detection of control degradation. Queensland coal mining requirements reinforce the importance of structured risk management for principal hazards and a safety and health management system that is implemented, maintained and reviewed (Queensland Government, 2017). At the same time, ISO 45001:2018 emphasises risk-based planning, leadership, worker participation, performance evaluation and continual improvement for occupational health and safety management systems (ISO, 2018). These expectations are consistent with safety barrier literature, which distinguishes barriers by function and highlights the need to understand performance, degradation and dependence between barriers (Sklet, 2006). A gap remains between these principles and the day-to-day quality of field verification.
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  • Closing the critical control gap – accelerating performance standards and verification quality for principal hazards using generative AI
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  • Published: 2026
  • Pages: 4
  • PDF Size: 0.122 Mb.
  • Unique ID: P-05514-T8W0Y4

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