AI Security & Red Teaming for Energy & Oil/Gas
Energy companies are critical infrastructure, and their AI deployments inherit that threat model. State-aligned actors probe the sector continuously, NERC CIP exists because the grid is a target, and pipeline operators carry TSA security directives born from an actual ransomware shutdown. As AI enters this environment, from operational copilots to trading models to field assistants, it opens paths traditional security programs do not cover. AI security red teaming for energy and oil and gas attacks those paths deliberately: injection into assistants with access to operational documents, manipulation of models informing integrity and trading decisions, and data extraction that would matter to a nation-state. We find the exposure before someone with worse intentions does.
AI Security & Red Teaming, built for energy & oil/gas
We scope the AI attack surface with your OT security context in mind, mapping which systems sit near CIP boundaries, operational data, or market-sensitive information.
We run adversarial campaigns against copilots, RAG systems, and decision-support models: injection, exfiltration, poisoning scenarios, and manipulation of model outputs.
We test the seams, especially where AI systems consume documents and data that cross from OT-adjacent sources, since that is where surprising paths appear.
Findings map to your existing security frameworks, including CIP evidence and TSA directive posture, with retest included after remediation.
Where it pays off in energy & oil/gas
Operational copilot testing
Adversarial assessment of assistants with access to procedures, P&IDs, and integrity data, where leakage has security significance.
Trading AI assessment
Testing whether market-facing models and assistants can be manipulated or induced to leak position and strategy information.
Decision-support manipulation
Probing whether crafted inputs can skew models that inform integrity, maintenance, or dispatch recommendations.
Data boundary validation
Verifying that AI systems cannot be used as an indirect path to information that should stay inside OT or CIP boundaries.
Operators typically discover that AI systems expose more operational information than their architecture reviews assumed, close the critical paths within weeks, and add adversarial AI testing to their security evidence for regulators and boards.
Energy & Oil/Gas AI, answered
No. We test AI systems on the IT side, including their data boundaries with OT sources, under rules of engagement your OT security team approves. The point is verifying that AI cannot become an indirect path toward operational systems, without ever creating that risk ourselves.
Because deployment is outpacing security review. Copilots and RAG systems with access to operational documents are going live across the sector, in an industry that adversaries already target. Testing them adversarially now costs weeks; discovering the exposure through an incident costs far more.
We frame findings against your existing obligations: whether an AI system creates an unassessed access path, whether information protected under CIP could leak through an assistant, and how your directive-driven security plans should account for AI. The output strengthens those programs rather than duplicating them.
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