CSC News Table of Contents
AI security guidance from a coalition of cyber authorities sets baseline controls for critical infrastructure and operational technology.
The playbook addresses real risks tied to AI use in industrial systems and essential services. It prioritizes governance, secure deployment, continuous monitoring, and human oversight to protect safety and resilience.
The document targets owners and operators of OT, ICS, and SCADA environments, plus their suppliers and integrators.
It aligns sector protections and translates existing frameworks to AI-in-OT scenarios.
AI Security Guidance: What You Need to Know
- AI security guidance defines governance, isolation, testing, monitoring, and fail-safe controls for safe AI deployment in OT.
Who Is Behind the New Guidance and Why It Matters
A coalition of national cybersecurity agencies and international partners issued the AI security guidance to standardize protections across energy, water, transportation, healthcare, and manufacturing.
The guidance frames AI as both an enabler and a potential attack vector. It calls for sector-specific controls that address model abuse, data integrity, and automation safety in high-consequence environments.
The AI security guidance serves OT, ICS, and SCADA operators, along with software vendors, system integrators, and managed service providers that support critical infrastructure cybersecurity programs.
Trusted Tools to Implement This Guidance
Strengthen defenses and align with AI security guidance using these vetted solutions:
- Bitdefender: Advanced endpoint protection and behavioral analytics for critical systems.
- 1Password: Enterprise password and secrets management to reduce credential risk.
- IDrive: Encrypted backups for resilience against ransomware and data loss.
- Auvik: Network monitoring and visibility for OT and IT segmentation and uptime.
- Tenable One: Unified exposure management across assets, cloud, and OT.
- Tenable OT Security: ICS and SCADA specific visibility and risk reduction.
- EasyDMARC: Email authentication to stop phishing and spoofing threats.
- Passpack: Team password vault with access controls and audit trails.
Scope: Critical Infrastructure and OT Environments
The AI security guidance addresses how AI shapes critical infrastructure cybersecurity where automation controls physical processes. In OT, safety, integrity, and availability dominate risk decisions.
The document prescribes layered defenses, clear governance, and human-in-the-loop oversight so AI complements, not compromises, industrial safety and regulatory obligations.
For attack technique context, review this explainer on prompt injection risks in AI systems and current evaluation work in AI cyber threat benchmarks.
Top Risks Flagged by the Guidance
The AI security guidance highlights operational technology AI risks that can degrade safety and availability if left unmanaged:
- Model misuse and evasion: Adversaries can manipulate inputs or context to bypass controls or degrade output fidelity.
- Data integrity and provenance: Poisoned or low quality data can corrupt training, tuning, or operational decisions.
- Supply chain exposure: Third-party models, libraries, and services may introduce vulnerabilities or backdoors.
- Over automation: Excessive autonomy without safeguards can create unsafe states in physical processes.
- Shadow AI: Unvetted tools and undocumented integrations expand the attack surface.
- Insufficient segmentation: Weak isolation between IT, AI services, and OT networks increases blast radius.
Core Recommendations for Owners and Operators
To reduce operational technology AI risks, the AI security guidance maps practical steps to familiar frameworks:
- Governance and risk management: Assign AI risk ownership, define policies, and enforce change control linked to safety and business outcomes.
- Secure design and testing: Apply a secure development lifecycle, adversarial testing, red teaming, and safety validation before deployment.
- Segmentation and least privilege: Isolate AI services from controllers and enforce strong identity, secrets, and access management.
- Data protection and lineage: Validate sources, track provenance, and secure pipelines to prevent poisoning and leakage.
- Monitoring and incident response: Instrument robust logging, drift detection, and playbooks that include model rollback and safe fallback modes.
- Vendor assurance: Assess third party models and APIs, require security attestations, and enforce timely patch processes.
The AI security guidance encourages zero trust for identity, access, and network policy enforcement across OT and IT. For implementation steps, see this guide on zero trust architecture in industrial contexts.
What Vendors and Integrators Should Do
Suppliers should align product roadmaps with the AI security guidance. Priorities include secure by design defaults, transparent model and data handling, documented failure modes, and clear operational runbooks.
Vendors should extend threat modeling to AI-specific abuse cases and deliver validated configurations mapped to critical infrastructure cybersecurity standards.
For foundational references, see NIST’s AI Risk Management Framework and the UK NCSC’s Guidelines for Secure AI System Development. Both complement the international AI security guidance for high stakes environments.
Implementation Considerations in the Real World
Operationalizing the AI security guidance requires joint work across security, engineering, operations, and compliance.
- Start by inventorying AI components and dependencies, defining trust boundaries, and piloting controls in non-production environments.
- Build safe mode and human override procedures for high uncertainty or sensor failures.
- Maintain tested backups and training so teams can restore AI functions or control logic after incidents.
ENISA’s overview of AI cybersecurity challenges adds context for execution.
Implications for Critical Infrastructure Operators
Advantages
Adopting the AI security guidance can improve resilience, reduce downtime, and enhance safety. Clear governance limits shadow AI and improves audit readiness.
Strong segmentation contains incidents and stabilizes physical processes. Vendor assurance reduces supply chain risk.
Continuous monitoring and AI-aware incident response improve drift detection, enable rapid rollback to safe states, and support faster recovery after anomalies or cyberattacks.
Disadvantages
The AI security guidance also demands investment. Building governance and validation pipelines requires time, budget, and specialized skills. Adversarial and safety testing can delay deployments.
Segmenting legacy OT networks and modernizing identity may require significant redesign. Coordinating multiple vendors and MSSPs under common controls is complex without strong program management and executive sponsorship.
Deploy With Confidence: Tools That Map to the Guidance
Advance your critical infrastructure cybersecurity posture with these aligned solutions:
- Tenable OT Security: Deep visibility into ICS and SCADA assets and risks.
- Bitdefender: Endpoint and server protection with threat analytics.
- 1Password: Secure secrets management for engineers and operators.
- IDrive: Immutable backups to bolster recovery and continuity.
- Auvik: Automated network mapping and observability for OT and IT.
- EasyDMARC: Reduce phishing risk with DMARC, SPF, and DKIM controls.
- Passpack: Shared credential control with auditability.
- Tenable One: Prioritize exposures across hybrid environments.
Conclusion
The AI security guidance offers a practical roadmap to deploy AI in OT without trading away safety or compliance. It treats AI as a powerful technology that must be controlled through sound engineering and security.
By emphasizing governance, segmentation, data integrity, vendor assurance, and continuous monitoring, operators can manage operational technology AI risks while capturing efficiency gains.
Start with an inventory, pilot key controls, measure outcomes, and scale what works with human oversight at every step.
Questions Worth Answering
Who issued the guidance?
- A coalition of global cybersecurity agencies coordinated the AI security guidance to support safe AI adoption in OT and ICS environments.
What sectors are covered?
- The AI security guidance targets critical infrastructure cybersecurity across energy, water, transportation, healthcare, and manufacturing.
Why does AI change OT risk profiles?
- AI introduces complexity, supply chain exposure, and data integrity issues, while model manipulation or over automation can impact physical safety.
What controls are most important?
- Governance, segmentation, secure development, strong identity and secrets, data lineage, AI aware monitoring, incident response, and vendor assurance.
How do we start implementation?
- Inventory AI components, define trust boundaries, pilot controls, validate failovers, and train teams on AI specific incident playbooks.
Does this replace existing standards?
- No. The AI security guidance complements existing frameworks and safety regulations and adapts them to AI in OT scenarios.
Where can I learn more?
- See NIST’s AI RMF, the UK NCSC’s secure AI development guidance, and track sector advisories and ICS patch updates.
About CISA
The Cybersecurity and Infrastructure Security Agency is the United States lead for safeguarding critical infrastructure from cyber and physical threats. It partners with public and private sectors.
CISA publishes alerts, best practices, and coordinates incident response to help organizations prevent, detect, and mitigate evolving threats to essential services.
Through international collaboration and sector initiatives, CISA advances resilience, promotes secure by design principles, and supports risk management across interconnected systems.
