Mining & Extractives - Overview
Extreme bifurcation: majors deploy AI across operations, juniors have near-zero adoption. 22% efficiency gains and 20% cost reductions are proven -- but concentrated in ten companies and two vendors. MSHA regulates mine safety, not AI. When a predictive maintenance model fails underground, the governance gap becomes a safety gap.
Extreme bifurcation defines this industry. Rio Tinto and BHP deploy AI across autonomous haul trucks, predictive maintenance, and ore grade optimization. Junior miners have near-zero adoption. The 22% efficiency gains and 20% cost reductions are proven but concentrated in fewer than ten companies and two dominant vendors. MSHA regulates mine safety with some of the most aggressive enforcement authority in federal government -- but has no AI governance framework. When a predictive maintenance model fails to flag a mechanical failure underground, or an autonomous haul system makes a routing decision that results in a safety incident, MSHA's investigation will apply the same evidentiary standard it applies to human decisions. The model's decision chain becomes the investigation record. For the few companies deploying at scale, the governance gap is a safety gap.
This industry includes 2 segments in the Ontic governance matrix, spanning risk categories from Category 1 — Assistive through Category 4 — Safety-Critical. AI adoption index: 3/5.
Mining & Extractives - Regulatory Landscape
The mining & extractives sector is subject to 10 regulatory frameworks and standards across its segments:
- BLM/USFS permitting
- EPA NEPA/CERCLA/Clean Air Act
- EU Critical Raw Materials Act
- ICMM principles
- MSHA (30 CFR)
- NEPA
- SEC (if public)
- SEC ESG disclosure
- State environmental regulations
- State mining commissions
The specific frameworks that apply depend on the segment and scale of deployment. Cross-industry frameworks (GDPR, ISO 27001, EU AI Act) may apply in addition to sector-specific regulation.
Mining & Extractives - Mining -- Junior / Exploration
Risk Category: Category 1 — Assistive Scale: SMB Applicable Frameworks: MSHA (30 CFR), BLM/USFS permitting, State mining commissions, NEPA, SEC (if public), State environmental regulations
MSHA does not distinguish between a geologist's judgment and the model that informed it.
The Governance Challenge
Junior mining and exploration companies use AI for exploration report drafting, permit applications, and investor communications. MSHA (30 CFR) regulates mine safety with aggressive enforcement authority — but has no AI governance framework. BLM/USFS permitting and NEPA environmental review apply to AI-generated environmental documentation. SEC requirements apply to AI-generated investor communications for public companies. Most juniors have near-zero AI governance despite using AI for critical reporting.
Regulatory Application
MSHA (30 CFR) applies to safety-related AI outputs. BLM/USFS permitting requirements apply to AI-generated environmental documentation. NEPA review applies to AI-assisted environmental impact assessments. SEC reporting requirements apply to AI-generated investor communications. State mining commissions add jurisdiction-specific requirements.
AI Deployment Environments
- Studio: Exploration report drafting | Permit application assist | Investor communication templates
- Refinery: Environmental impact documentation governance | Safety plan compliance templates
Typical deployment path: Studio → Studio → Refinery
Evidence
- MSHA enforcement authority is among the most aggressive in federal government
- Junior mining has near-zero AI governance despite growing tool adoption
- SEC disclosure requirements increasingly scrutinize AI-generated content
Mining & Extractives - Mining -- Major / Integrated Operator
Risk Category: Category 4 — Safety-Critical Scale: Enterprise Applicable Frameworks: MSHA (30 CFR), EPA NEPA/CERCLA/Clean Air Act, SEC ESG disclosure, ICMM principles, State mining commissions, EU Critical Raw Materials Act
MSHA investigates the decision chain, not the algorithm. Autonomous haul trucks generate decisions at scale.
The Governance Challenge
Mining majors deploy AI across autonomous haulage systems, safety analysis, environmental monitoring, safety incident reporting, environmental compliance narratives, and community impact documentation. MSHA (30 CFR) has some of the most aggressive enforcement authority in federal government — but no AI governance framework. EPA NEPA/CERCLA/Clean Air Act applies to AI-assisted environmental operations. SEC ESG disclosure requirements apply to AI-generated sustainability reporting. When an autonomous haul system makes a routing decision that results in a safety incident, MSHA's investigation applies the same evidentiary standard it applies to human decisions.
Regulatory Application
MSHA (30 CFR) governs mine safety with aggressive enforcement authority including AI-assisted operations. EPA NEPA/CERCLA/Clean Air Act applies to AI-assisted environmental decisions. SEC ESG disclosure requirements apply to AI-generated sustainability and safety reporting. ICMM principles apply to major operators. State mining commissions add jurisdiction-specific requirements. EU Critical Raw Materials Act adds compliance for European operations.
AI Deployment Environments
- Studio: Autonomous haulage system summaries | Safety analysis drafting | Environmental monitoring assist
- Refinery: Safety incident reporting governance | Environmental compliance narratives | Community impact documentation
- Clean Room: MSHA investigation evidence packages | EPA enforcement response files | Safety-critical system governance
Typical deployment path: Clean Room → clean_room (primary) | refinery for environmental and community documentation
Evidence
- 22% efficiency gains and 20% cost reductions proven in mining AI
- MSHA enforcement authority is among the most aggressive in federal government
- Autonomous haul systems generate thousands of AI decisions per shift
- SEC ESG disclosure requirements increasingly scrutinize AI safety claims