Can AI Be Trusted in Industrial Automation? The Case for Security Assurance and Validation

As artificial intelligence moves from advisory analysis to operational decision-making in industrial environments, a fundamental shift is occurring. Traditional industrial automation relies on deterministic logic where predefined inputs consistently produce repeatable outputs. In contrast, AI introduces non-deterministic, probabilistic behavior that depends on complex data pipelines, model states, and changing operating contexts.

Our white paper, "Can AI Be Trusted in Industrial Automation? The Case for Security Assurance and Validation," explores why conventional testing is no longer sufficient and outlines a rigorous engineering discipline for establishing operational trust.

Key Takeaways from the Paper

The Extended Industrial AI Attack Surface: AI broadens vulnerabilities far beyond traditional OT assets (like PLCs and DCSs) into data layers, model repositories, inference platforms, supplier update paths, and operator decision pathways.

• Why Traditional Testing Falls Short: While standard OT cybersecurity testing asks can the system be compromised?, AI security validation must ask can the system be trusted under uncertainty? This requires accounting for data corruption, model drift, adversarial inputs, and automation bias.
• The Industrial AI Trust Validation Framework: Palindrome proposes a four-part assurance cycle:
  1. Architecture & Control Verification: Aligning trust boundaries and deployment pathways with ISA/IEC 62443 standards.
  2. Operational Behavior Validation: Testing accuracy, consistency, and explainability under representative conditions.
  3. Adversarial Testing: Exposing models to manipulated data, abnormal states, and edge cases.
  4. Deployment Assurance & Monitoring: Tracking drift, configuration changes, and ongoing supplier dependencies.
• Risk-Based Human Oversight: Managing autonomy as a staged transfer of operational authority—scaling human review, override capability, and fallback mechanisms directly to the operational consequence of an incorrect AI output.

Download the White Paper

Ready to dive deeper into the technical architecture, frameworks, and lifecycle strategies for securing industrial intelligence?

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