The release of the joint CISA-led guidance on integrating Artificial Intelligence (AI) into Operational Technology (OT) marks a pivotal moment in cyber governance. This is more than a technical advisory; it is a clear articulation of federal expectation: the security of critical infrastructure now depends on rigorous, verifiable supply chain oversight, especially when introducing AI.

While AI promises essential efficiencies in OT—such as predictive maintenance and system optimization—the guidance stresses that integration introduces significant, non-negotiable risks. For security and risk leaders, this translates into a shift in accountability. The question is no longer if you use AI, but how you prove the security maturity of the vendors and AI systems connected to your most critical physical processes. This is the Leadership Imperative for a new era of cyber resilience.

The Four Pillars of AI/OT Governance

The guidance outlines four core principles for vendor risk management:

1. Understand AI and its Lifecycle: Leaders must possess a nuanced understanding of the unique risks associated with the secure AI development lifecycle. This requires mandating proof that your AI solution providers adhere to security-by-design principles throughout development, procurement, and deployment.
2. Strategic Use in the OT Domain: Before deployment, organizations must rigorously assess the business case for AI, actively manage data security risks, and explicitly define the vendor’s role and data access privileges. This necessitates demanding transparency on software components, such as a Software Bill of Materials (SBOM), and clear policies on OT data usage by external parties.
3. Establish Governance and Assurance: Compliance can no longer be a reactive exercise. Governance must be established upfront, integrating AI assurance mechanisms into existing security frameworks. This means continuous testing and demonstrable evidence that vendors meet stringent regulatory and performance standards.
4. Embed Failsafe Practices and Oversight: The ultimate responsibility for functional safety remains with humans. You must implement robust monitoring, “human-in-the-loop” controls, and clearly defined failsafe procedures. Critically, your Incident Response (IR) plans must be immediately updated to account for AI model drift, failures, or malicious compromise.

For context, this guidance specifically addresses risks from advanced Machine Learning (ML) and Large Language Models (LLMs) and the AI Agents they power. These newer techniques involve complex security considerations compared to traditional statistical modeling.

These AI applications are not confined to the IT network; they penetrate the deepest layers of Operational Technology (OT). While optimization tools may reside in the business network, predictive ML models for local control and monitoring are operating at the Local Controllers (Level 1) and Local Supervisory (Level 2) levels of the Purdue Model. When a vendor’s security hygiene is compromised, the failure can directly impact these critical layers, creating immediate functional safety and availability risks.

From Static Assessment to Continuous Validation

The underlying thread across all four principles is the undeniable move away from static, point-in-time assurance. Traditional annual questionnaires provide a security snapshot that is instantly obsolete against constantly evolving threats. The integrity of an AI system depends entirely on the ongoing hygiene of the vendor who built, hosts, or maintains it. The failure to continuously monitor this external attack surface exposes critical infrastructure to risks like security bypasses, cascading compromise, and loss of productivity.

To meet the high-stakes requirements of the new guidance, security leaders must implement a process that integrates technical validation with vendor engagement. This continuous cycle of detection and response minimizes the critical time-to-remediate (TTR) when vulnerabilities appear. It is a proactive defense that validates vendor claims against real-time data, assessing factors like Application Security, DNS Health, and Patching Cadence—the very weak points that cyber adversaries exploit. This approach transforms risk awareness into immediate, measurable risk action, an outcome the modern security mandate demands.

Validating AI Vendor Hygiene

To achieve the level of demonstrable security and compliance demanded by the new guidance, leaders must adopt technical validation capabilities. These capabilities must provide continuous, external oversight of AI partners:

• Continuous Vendor Monitoring: You must move beyond self-attestation to continuously track the actual security posture of your AI vendors, ensuring their external-facing assets are healthy. This means leveraging external security ratings and intelligence to provide objective metrics on their cyber hygiene.
• Technical Control Validation: Focus on continuously assessing vendors across fundamental security risk factors. This includes verifying strong performance in key areas like Application Security (to prevent injection and data leakage), DNS Health (to block phishing attempts), and Patching Cadence (to eliminate known exploit vectors). These security signals directly address the weaknesses the guidance highlights.
• Proactive Remediation Engagement: When risks are identified, simply alerting the vendor is not enough. You need integrated workflows and communications tools to engage with your AI partners, prioritize the most critical vulnerabilities, and accelerate the time it takes to resolve security issues. This rapid, collaborative remediation helps ensure ongoing compliance and minimizes exposure.

The Call to Action for Critical Infrastructure

The CISA guidance provides strategic direction for critical infrastructure organizations. Secure AI adoption in OT requires visibility and accountability across vendor ecosystems. Neglecting AI vendor security posture creates direct exposure in physical operations.

Organizations should build programs of continuous assessment and verifiable remediation.

To help your organization transition from static risk identification to active risk management, explore SecurityScorecard solutions for Continuous Monitoring and Vendor Collaboration.