AI, Cybersecurity And Operational Resilience In Mining

Digital transformation is creating exponential value in the mining industry.

From Artificial Intelligence ("AI")‑enabled exploration and digital twins in processing plants to autonomous fleets and predictive maintenance, mining companies can run increasingly complex, connected systems at scale. The results are visible: safer operations, higher recoveries and improved capital discipline.

The risk is equally clear. A cyber incident can travel at machine speed across interconnected infrastructure, with consequences for safety, production and enterprise value. This elevates the importance of robust governance.

AI is reshaping the threat landscape

IBM estimates the global average cost of a cyber breach to approximately USD4.4 million in 2025.

AI has transformed both offence and defense in cybersecurity.

The early rush to deploy ChatGPT resulted in speed-to-market outpacing governance. This resulted in sensitive IP being leaked into the model and hallucinated cases being relied upon in court by humans.

Agentic AI does not just generates outputs, it acts. AI-agent social platforms show how agents can post and interact with each other and coordinate across systems. This has already resulted in a reported exposure of 1.5 million API keys and tens of thousands of emails. This validates concerns about the risks associated with vibe-coding (AI-assisted software development) and rapid AI adoption without proper governance.

Agents embedded in everyday workflows can significantly improve efficiency. Yet, the same autonomy introduces new risks. If a cybercriminal interferes with the information these agents receive, or with the systems and data they can access, an agent could rapidly carry out harmful actions faster than a human can, Agents can faithfully follow malicious instructions hidden in content and then cover up its tracks. Agents can expand a once-off breach into a coordinated multi-breach attack, while adding complexity to post-incident forensics.

Even a traditional business email compromise attaching a “poisoned” document can lead to data leakage or malicious actions. These attacks can drive poor decisions in models intended to manage real-world assets, introducing safety and operational risks. For example, misrouting autonomous haul trucks or degrading safety interlocks.

As critical minerals grow in strategic importance to the global energy transition, cyber-attacks are increasingly linked to state-backed groups. These actors plan carefully and operate over longer periods, ranging from espionage to funding illicit activities, adding to an already busy cyber-crime environment.

Supply chains threaten cyber-resilient organisations

Modern mining operations rely heavily on complex supply chains, including equipment manufacturers, software vendors and integration partners. Attackers increasingly exploit this dependency by targeting the weakest security controls in the chain.

Mergers and acquisitions pose particular risk due to shared systems, temporary access arrangements and inconsistent security practices. Cyber risk should therefore be treated like environmental or financial due diligence that is measured, factored into deal value and addressed before a transaction closes. This requires clear contractual rights around monitoring, cooperation and incident notification that are tested in advance rather than during a crisis.

Effective outcomes depend on aligning technological ambition with operational reality and regulatory expectations through collaboration between operators, original equipment manufacturers ("OEM"s), data providers, law‑enforcement and regulators.

Models and AI agents should be treated like assets: inventoried, version-controlled, access-restricted and subject to the same change controls applied to critical operationa technology ("OT") assets. They influence operational decisions and risk.

Regulatory realities require evidential readiness

Cross‑border mining operations straddle multiple privacy, cyber security and sector-specific laws. Disclosure triggers vary and timelines are tight. After a cyber incident, defensibility (what can be proven quickly to regulators, courts, counterparties and communities) matters as much as technical recovery. 

In a double‑extortion ransomware incident, the ability to credibly establish what was accessed, what was exfiltrated and whose data is implicated will shape negotiations, regulatory posture and litigation exposure.

That makes logs, not narratives, the strategic asset.

Build forensic and litigation readiness into architecture, including immutable logging, role‑based access for humans and agents, data minimisation and anonymisation. Enterprise data mapping should include evidence of what is held, where it resides, who has access and the lawful basis for processing.

Knowing what was taken - fast: rapid content intelligence

When extortion groups dump or threaten to publish terabytes of mixed data, the decisive question is: what, precisely, was taken and what is the exposure risk?

A privileged, forensically sound methodology can turn messy breached data sets into classified content by business harm and legal sensitivity, for instance, personnel information, customer and supplier information, negotiation materials, board papers, intellectual property such as block models, drill logs and processing parameters and safety or OT‑adjacent documentation.

Critically, the process should move beyond labelling files to quantifying and mapping exposure so executives can act quickly on reputational and legal risk.

Organisations that maintain current data maps and retention schedules before an incident can answer “what was taken” and “whose data” materially faster. Counsel can move from raw data to a prioritised risk picture quickly enough to brief boards with specificity and sequence external communications before the narrative hardens.

This enables legal, cyber, operations and communications teams to work from the same, quantified picture of harm.

Incident response that reflects operational reality

Incident response plans must be AI‑aware and OT‑literate. A staged, achievable path forward for operators:

• Mandate and rehearse AI‑aware incident response that spans IT and OT, with clear decision rights on pausing automation and validating data integrity.
• Harden identity and session management across cloud and vendor ecosystems; regularly review controls during M&A and major projects.
• Uplift board reporting to include AI agent governance and site‑relevant resilience metrics.
• Invest in forensic readiness and surge review capability that can produce regulator‑ready outputs at speed without sacrificing quality.
• Train high‑risk functions against deepfakes and AI‑enabled social engineering using realistic simulations.

Boards need site‑relevant oversight and AI governance mechanisms

Boards need site-relevant oversight and information and AI governance mechanisms.. Directors should understand how technologies are governed, which actions require human approval, how quickly AI agents can be isolated, the scope of red‑teaming and safety testing, and whether evidential logging captures AI tool use across IT and OT environments.

Cyber readiness has shifted from a discretionary IT function to a mandatory, board-level, and legally auditable requirement. Auditable decision‑making pre and post-incident are critical factors in mitigating regulatory and reputational damage.

“Together we are better”: collaboration that changes outcomes

When operators prioritise security, regulators articulate consistent expectations around notification and evidence and when law‑enforcement engages early in high‑impact incidents, outcomes improve.

Inside the enterprise, legal, cyber, operations and communications teams must train together. Externally, industry platforms can accelerate uplift by promoting minimum standards for agent governance, logging and incident cooperation.

Applied carefully under human supervision, AI can also accelerate the post-incident plan, closing the gap between detection and defensible response.

Resilience is becoming a competitive capability in mining operations. Companies that can demonstrate how AI agents act, how models are governed, and how information governance and post-response meet cross‑border standards will modernise with confidence while protecting people, production and reputation.

Cybersecurity, done well, is not a brake on innovation. It is the discipline that lets you adopt AI at speed without losing control of your risk, your data or your licence to operate.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.