AI Governance and Risk Management

This application area focuses on systematically identifying, monitoring, and managing the risks created by AI systems deployed across mining operations—such as in exploration, production optimization, safety monitoring, and maintenance. It includes centralized platforms that track model performance, drift, and anomalous behavior, as well as frameworks that inventory all AI components, map their dependencies, and assess security, compliance, and ESG exposure. It matters because mining companies are rapidly scaling AI in safety‑critical, highly regulated environments with stringent ESG expectations. Without structured governance and risk management, they face hidden operational vulnerabilities, regulatory non‑compliance, reputational damage, and safety incidents triggered or amplified by poorly monitored models. By turning ad‑hoc oversight into a repeatable, auditable process, this application helps mining firms safely capture AI’s productivity and safety benefits while maintaining trust with regulators, investors, and communities.

The Problem

You’re scaling AI in safety‑critical mines with no single view of the risks you’re taking.

Organizations face these key challenges:

1

No central inventory of AI models, data pipelines, and vendors used across sites and functions

2

Model drift or anomalous behavior is only discovered after a safety, production, or quality incident

3

Risk, IT, and operations teams rely on spreadsheets and email to track AI issues and approvals

4

Regulators and auditors ask for evidence of AI controls that you can’t produce quickly or consistently

5

Third-party AI tools are adopted by sites without standardized security, ESG, or compliance review

Impact When Solved

Fewer AI-driven safety and operational incidentsFaster, safer AI deployment and change managementStronger regulatory, security, and ESG posture

The Shift

Before AI~85% Manual

Human Does

  • Manually track AI models and tools in spreadsheets or slide decks
  • Perform periodic, sample‑based model reviews and validation checks
  • Compile evidence for audits and regulatory inquiries by chasing teams for documentation
  • Assess security, safety, and ESG risks through workshops and manual questionnaires

Automation

  • Basic system monitoring via generic IT tools (uptime, CPU, network)
  • Ad hoc scripting to pull model performance metrics from individual systems
With AI~75% Automated

Human Does

  • Define risk appetite, policies, and escalation thresholds for AI systems
  • Review and act on high‑risk alerts, exceptions, and recommended mitigations
  • Engage with regulators, auditors, and stakeholders using system‑generated evidence

AI Handles

  • Continuously monitor model performance, drift, and anomalous behavior across all AI systems
  • Maintain an AI bill of materials and map dependencies between models, data, and infrastructure
  • Automatically run standardized risk, security, and ESG checks on AI systems and flag issues
  • Generate audit‑ready reports and evidence trails for compliance and governance

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

AI Inventory and Policy Dashboard

Typical Timeline:Days

A lightweight governance starter that centralizes a catalog of AI systems used across mining operations and overlays basic policy checks. It relies on manual and semi-automated discovery plus simple rules to flag missing documentation, owners, or approvals. This gives leadership a first unified view of where AI is deployed and obvious governance gaps without deep integration into OT systems.

Architecture

Rendering architecture...

Key Challenges

  • Getting accurate and complete AI inventories from distributed teams and vendors
  • Aligning stakeholders on a minimal but useful governance schema
  • Avoiding over-engineering when the goal is visibility, not full automation
  • Ensuring data quality without making data entry too burdensome
  • Driving adoption among busy operations and engineering teams

Vendors at This Level

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