Industrial AI's First Real Risk Is Access, Not Accuracy
FERC's latest CIP audits show AI tools entering power-sector OT faster than asset registration and access controls can track them.
FERC's latest CIP audits show AI tools entering power-sector OT faster than asset registration and access controls can track them.
Automotive, aviation, energy, and healthcare are taking four distinct regulatory postures on agentic AI, and compliance leaders cannot assume one will predict another.
MathWorks and FERC are solving the same AI assurance problem from opposite ends, and neither one answers the AI governance question regulators are building next.
AI systems are shifting from consuming grid power to making real-time dispatch decisions, and that reclassification changes who is accountable when something goes wrong.
AI-driven data center demand is pushing utilities toward AI-managed storage and dispatch, quietly expanding critical infrastructure governance exposure.
Manufacturers integrating AI into OT environments face a single architectural choice that determines their entire safety and audit posture.
Manufacturers buying AI-driven OT security platforms are skipping the asset visibility problem that determines whether the AI works at all.
Vendors are certifying AI tooling for safety-critical systems while FERC's own CIP audits are finding the AI access risks that certification is supposed to prevent.
FERC's 2026 CIP audit findings put the aviation-grade question of AI inside the control loop onto the power grid and the factory floor.
MathWorks' TÜV SÜD-certified ISO 26262 guidance covers a process, not an AI's output, and buyers across sectors need to know which framework any such credential actually answers.