Why This Matters
Since the public release of modern AI solutions, AI has rapidly expanded across industries, for critical infrastructure operators, utilities, transport, water treatment, manufacturing plants, and more. AI promises greater efficiency, more intelligent decision-making, cost savings, and improved service delivery.
However, embedding AI into Operational Technology (OT) systems that directly control physical processes also brings significant risks. If not managed appropriately, AI integration can threaten system reliability, safety, security, and the availability of essential public services.
This guide, jointly authored by international cybersecurity agencies (including Cybersecurity and Infrastructure Security Agency (CISA), ACSC, and other global partners), lays out a framework of four core principles. These help critical-infrastructure owners and operators harness the benefits of AI while managing risk.
Core Principles for Secure AI-OT Integration
Understand AI’s Risks & Lifecycle
Recognize AI-Specific Risks in OT Settings
- Cybersecurity threats: AI models, training data, and deployment software can be tampered with, leading to erroneous outputs or even bypassing safety or security controls. Traditional cyber risks (e.g., unauthorized access, data breaches) remain relevant.
- Data quality issues: AI performance depends heavily on high-quality, normalized data. In distributed OT environments, collecting consistent sensor data can be difficult; centralizing such data can also raise security/privacy risks.
- Model drift: As operating conditions change over time (e.g., process parameters, environment), AI models may become less accurate, leading to degraded performance or safety hazards.
- Lack of explainability: Many AI models are opaque (“black box”), making it hard to audit or understand their decisions. That complicates troubleshooting, compliance, and safety oversight.
- Operator overload & automation reliance: Over-reliance on AI can erode human operator skills; false alarms or incorrect AI outputs may lead to downtime or safety incidents.
Because of these risks, organizations must treat AI integration seriously, not as a simple add-on.
Embed Security From Design to Decommission
AI systems must follow a secure lifecycle from secure design, through procurement or development, deployment, and ongoing operation/maintenance.
Operators should evaluate whether to procure a vendor-provided AI system, build in-house, or customize an existing solution, selecting only those that meet stringent security, safety, and operational requirements.
Invest in Personnel Awareness & Training
Human operators must be trained in AI fundamentals, threat modeling, and potential AI failure modes. This helps ensure they can interpret AI outputs correctly and respond appropriately when AI fails or behaves unexpectedly. Standard operating procedures (SOPs) should clearly define roles, responsibilities, and fallback processes.
Operators should favour explainable AI (XAI) for transparency; outputs are auditable and understandable.
Assess Whether AI Is the Right Fit for Your OT Use Case
Evaluate the Business Case Rigorously
AI is powerful, but that doesn’t mean it’s always the best solution. Operators should weigh its complexity, cost, security implications, performance, and long-term maintenance needs. Sometimes, traditional automation or simpler tools may suffice.
Protect Sensitive OT Data First
OT environments produce a mix of data: long-lived engineering data (network diagrams, equipment configs, logic flows, safety schematics) and ephemeral sensor data (temperature, pressure, flow, runtime diagnostics). Both types are sensitive: the former holds critical infrastructure design/intellectual property, the latter reveals operational patterns. Treat both as high-value and secure accordingly.
Ensure Vendor Transparency & Respect Real-time Constraints
If using vendor-supplied AI or third-party integration, demand transparency: complete documentation, clear security obligations, and clarity on whether AI makes outbound connections or modifies engineering workflows. Also assess whether the AI can meet strict OT requirements, such as low latency or deterministic response times. Real-time control contexts may not suit AI-driven solutions.
Where possible, integrate AI first in test environments, and ensure that any direct control over OT remains under operator oversight (i.e., “human-in-the-loop”). Provide robust fallback to traditional automation/manual control.
Build Strong Governance & Assurance Mechanisms
Set Up Governance, Roles & Accountability
Before deployment, senior leadership (e.g., CEO, CISO), OT/IT experts, and vendor partners all must commit to a clear governance framework. Define who is responsible for design, procurement, deployment, monitoring, maintenance, and incident response.
Regular audits, compliance checks, and performance reviews should be scheduled. Stakeholders must know their roles and accountability boundaries.
Integrate AI Risk into Existing Cybersecurity Frameworks
Rather than building a separate “AI security,” embed AI risk assessment and controls into the organization’s existing cybersecurity program. That means standard controls, encryption, access control, intrusion detection, logging, plus AI-specific threat modeling.
Security teams should extend their monitoring and incident response to cover AI-specific threats (e.g., adversarial attacks, prompt injection, data poisoning, model tampering). It may be helpful to leverage frameworks such as MITRE ATT&CK and its AI-adapted variants (e.g, ATLAS) when modeling AI threat vectors.
Test Thoroughly Before Production
Deploy AI first in non-production or test environments. Use virtualized controllers or simulation-based tests when possible, especially if the AI might influence physical processes.
Avoid using production data in test environments. Only after rigorous testing should moving into production be considered; even then, deploy incrementally, with fallback thresholds defined.
Continuously monitor model performance; set safety thresholds and criteria for failing back to non-AI control if AI outputs degrade or drift.
Embed Safety, Transparency & Failsafe Practices
Monitor AI Systems: Maintain Human Oversight
Every AI component should be inventoried; inputs and outputs must be logged and monitored. Establish a “known good” baseline or safe-state thresholds for behaviour; use anomaly detection or behavioural analytics to detect drift, misuse, or faults.
Prefer “push-based” or brokered architectures: for example, export OT data to a staging zone or buffer for AI processing, rather than giving AI persistent inbound access to OT networks. This limits the AI’s exposure as an attack vector.
Where critical decisions or actions are taken, maintain a human-in-the-loop, especially for control actions affecting safety, production, or real-world operations.
Design Fallbacks & Incident Response for AI Failures
AI adds new failure modes to OT systems. Update your functional safety procedures and incident response plan accordingly. Define how the system should behave if the AI fails, or if its outputs degrade below acceptable thresholds.
Ensure that fallback mechanisms allow reversion to traditional automation or manual control. Also, treat AI-related incidents as part of the broader cybersecurity incident management process.
What This Means for Critical Infrastructure Operators
- AI can unlock real value for OT predictive maintenance, anomaly detection, operational optimization, better decision support, and efficiency gains.
- But AI must not be treated as a plug-and-play tool. Without proper safeguards, it can introduce new vulnerabilities, reduce system resilience, create safety hazards, and complicate compliance/regulation.
- The four principles: understand AI, assess use cases, build governance, embed safety & security, and provide a realistic framework.
- Implementation should be iterative: careful testing, incremental deployment, continuous monitoring, human oversight, and readiness to revert to safe fallback states.
- Ultimately, human operators remain responsible for safety. AI augmentations can not replace human judgment.
Practical Steps: Top Recommendations for Deployment
- Conduct a complete risk assessment before even choosing an AI solution, consider data sensitivity, real-time constraints, and vendor transparency.
- Build or procure AI systems that follow a secure-by-design lifecycle, and ensure documentation and vendor SLAs cover AI-specific security responsibilities.
- Train your OT staff on AI fundamentals, threat models, and fallback procedures; create SOPs for AI-linked operations.
- Start with pilot or test deployments using simulation or virtualized OT infrastructure; avoid production use until testing is satisfactory.
- Implement logging, monitoring, and anomaly detection, and integrate AI-related logs into your standard cybersecurity monitoring and incident-response workflows.
- Maintain human-in-the-loop for decisions affecting safety or control.
- Prepare incident response plans that cover AI failures or adversarial attacks; ensure fallback to manual/traditional control is defined and tested.
Conclusion: Building a Secure, AI-Ready Future for Operational Technology
Successfully integrating artificial intelligence into operational technology systems requires far more than deploying a model and hoping for efficiency gains. It demands a strategic approach grounded in risk awareness, rigorous testing, strong governance, and continuous monitoring. Critical infrastructure environments operate under unique constraints: real-time requirements, safety implications, and interconnected physical processes, making secure AI deployment not just a technical decision but a public-safety responsibility.
By following the four core principles, which are understanding AI risks, validating use cases, establishing governance, and embedding safety, organizations can confidently adopt AI while safeguarding reliability, resilience, and trust. When implemented thoughtfully, AI becomes a powerful ally in improving operational performance, enhancing decision-making, and strengthening infrastructure security. The future of OT is undeniably intelligent, but it must also be secure, and this framework provides a clear path toward achieving both.