AI moved from the office. It now runs on the plant floor
Manufacturing, energy, utilities, transport, chemicals, supply chain. Industrial AI sits where critical infrastructure, operational technology, and machine learning converge. The risk surface covers safety, continuity, and the public interest in the operations themselves.
How AI is changing industrial operations
The dominant approach inside industrial companies has been to treat AI as an extension of operational technology. The same OT security frameworks, the same safety protocols, the same engineering review processes that governed traditional automation are extended to AI-driven systems. The prevailing assumption has been that industrial discipline is rigorous enough to absorb AI risk without a new framework. That assumption is narrower than most organizations have mapped.
OT security frameworks were built for systems that behave consistently after deployment. AI models retrain, drift, and respond differently as operating conditions shift. Engineering review processes validate that a control loop performs as specified. Evaluating whether a predictive maintenance model will continue to perform under different conditions requires a different kind of oversight.
The EU AI Act adds an explicit layer: critical infrastructure operators have specific high-risk obligations when AI participates in safety-relevant systems. Sectoral regulations in energy, transport, and chemicals add further requirements. None of these frameworks alone addresses the full picture.
What risks does this create
The risks are structural, and in industrial AI, governance failures have operational and safety consequences that go beyond compliance.
OT-IT-AI boundary gaps
Traditional separation between operational technology security, information security, and emerging AI governance creates blind spots where accountability is unclear. A predictive maintenance model that influences when equipment is taken offline sits across all three domains simultaneously. Governance that covers all three domains together closes the blind spots that separate frameworks leave open.
Safety-relevant AI without independent assurance
Self-attestation that a predictive maintenance model is safe, that an autonomous logistics system is reliable, or that an energy optimization AI behaves as expected is not sufficient when public safety or critical service continuity is at stake. Engineering sign-off is not independent conformity assessment. Regulators, insurers, and boards are increasingly drawing that distinction.
Critical infrastructure obligations that most operators have not yet mapped
The EU AI Act classifies AI in critical infrastructure management as high-risk under Annex III. Energy operators, transport networks, water management, and chemicals companies are deploying AI in functions that trigger these obligations. Most have not yet conducted the inventory to know which of their systems qualifies.
Supply chain AI as an emerging exposure
AI-driven demand forecasting, autonomous warehousing, and dynamic logistics create risk surfaces that traditional supply chain risk management does not address. The EU AI Act does not yet establish explicit accountability for AI failures in supply chain partners, but contractual and reputational exposure is real, and procurement frameworks are not designed to evaluate it. Industrial companies that govern their own AI but not their critical suppliers are managing only part of the risk.
The question that has changed
The question regulators and boards are asking has shifted: from whether AI systems perform within engineering tolerances, to whether AI deployed across operations, safety, and supply chain is governed continuously, in line with critical infrastructure obligations and sectoral regulations.
How these risks can be mitigated
The mitigation path runs through a governance framework that integrates AI management with the existing operational, safety, and IT infrastructure.
Unified governance perimeter
ISO/IEC 42001 integrates with ISO/IEC 27001 and OT frameworks to create a single governance boundary that covers IT, OT, and AI deployments. The alternative, separate frameworks for each domain, creates the blind spots that generate the risk.
Independent assurance for safety-relevant systems
Any AI system that participates in a decision with physical safety implications needs independent assessment beyond internal engineering review. That includes predictive maintenance models, autonomous logistics, energy optimization, and process control AI.
Supply chain AI verification
Critical supply chain partners deploying AI in functions your operations depend on require the same governance scrutiny as internal AI. Extending AI risk assessment to the supply chain is a procurement question that most frameworks have not yet caught up with.
For most industrial operators, the certification process surfaces AI deployments that were invisible to the compliance and risk functions.
How we help
ISO/IEC 42001 Certification for Industrial Operators
ANAB-accredited certification scoped to industrial realities. Integration with existing ISO/IEC 27001 ISMS, with ISO 9001 quality systems where applicable, and with OT security frameworks.
EU AI Act Assessment for Critical Infrastructure
Inventory of AI deployments across operations, classification by AI Act risk tier, identification of critical infrastructure obligations and sectoral regulatory overlaps (energy, transport, chemicals).
Operational AI Risk Assessment
Independent assessment of AI in safety-relevant systems: predictive maintenance, energy optimization, autonomous operations, process control. Documented findings for boards, regulators, and operational leadership.
Supply Chain AI Verification
Assessment of AI deployments in critical supply chain partners. Closes the gap between procurement frameworks and operational AI risk.
Zertia Academy — Industrials Track
Training for OT security, operational excellence, safety engineering, and supply chain leadership. Builds shared language between operational discipline and AI governance.
What regulators are asking and what certification answers
| Regulatory obligation | What it requires | How Zertia addresses it |
|---|---|---|
| EU AI Act — Annex III (High-risk: critical infrastructure) | AI used in the management and operation of critical infrastructure (energy, water, transport) is classified as high-risk. Requires risk management system, logs, human oversight, and registration. Applies to operators as deployers, not just AI developers. | EU AI Act Assessment for Critical Infrastructure: full inventory and Annex III classification of operational AI deployments, with prioritized remediation path for high-risk systems. |
| NIS2 Directive (Network and Information Security) | Essential entities in energy, transport, and other critical sectors must implement cybersecurity risk management measures. NIS2 covers cybersecurity incidents, including those arising from compromised or manipulated AI systems. A cyberattack that exploits an AI system in OT triggers NIS2 incident reporting. An AI model failure that is not security-related does not, though operational and regulatory consequences may still follow under sectoral law. | ISO/IEC 42001 certification integrated with ISO/IEC 27001 ISMS. Single governance framework covering information security and AI management. |
| ISO 9001 integration (Quality management) | Industrial operators with existing ISO 9001 certification face integration questions when deploying AI in quality-relevant processes. AI-assisted quality control, process optimization, and defect detection create overlapping obligations. | ISO/IEC 42001 implementation scoped to integrate with existing ISO 9001 and ISO/IEC 27001 certifications. Reduces audit burden and produces a coherent multi-standard management system. |
| EU AI Act — Art. 26 (Deployer obligations) | Document AI systems, implement risk management, maintain human oversight, and keep logs for all AI used in operations. Applies to industrial operators as deployers, regardless of whether the AI was built internally or procured from vendors. | Covered in ISO/IEC 42001 certification scope. Audit covers both internally developed and third-party AI deployed in operational contexts. |
From inquiry to certification
If you're earlier in the process
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FreeA structured assessment for manufacturing, energy, utilities, and transport operators. Covers AI inventory, classification, and integration with operational and regulatory frameworks.
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Paid · Fixed feeDiagnostic of your industrial AI deployments against ISO/IEC 42001, AI Act obligations, and integration with OT and IT security frameworks.
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ISO/IEC 42001 Certification
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Zertia is a conformity assessment body accredited by ANAB for ISO/IEC 42001
Our certification activities under ISO/IEC 42001 are conducted under accreditation by ANAB (ANSI National Accreditation Board), a globally recognized accreditation body.
ANAB accreditation confirms that our audit and certification processes meet applicable ISO standards and international requirements for competence, impartiality, and independence. Our methodologies, auditor qualifications, and decision processes are externally assessed against rigorous technical criteria.
For certified organizations, this provides internationally recognized certificates and enhanced credibility with regulators, clients, and investors.
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