Case Study: CER Directive Compliance for AI Infrastructure

Context and Challenge

A large multi-site energy and utilities operator had spent several years modernizing operational technology and data platforms to support predictive maintenance, demand forecasting, and anomaly detection. A growing portion of those capabilities relied on AI workloads running across a hybrid estate: on-premises systems close to industrial control environments, private cloud capacity for centralized analytics, and a limited set of approved public cloud services for development and model training.

The move to AI-driven operations brought measurable operational benefits, but it also introduced new regulatory and resilience questions. The Critical Entities Resilience (CER) Directive raised the bar for how essential service providers demonstrate their capacity to prevent, withstand, and recover from disruptions. For AI infrastructure—where data pipelines, model-serving endpoints, and automated decision support can become operational dependencies—the directive’s emphasis on resilience planning, risk management, and incident handling created a clear challenge:

• AI had become part of critical service delivery, even when the AI system itself was not “safety-critical” in a traditional sense.
• Operational teams were not fully aligned on where AI sat in the resilience scope: was it an IT service, an OT dependency, a business application, or all three?
• Documentation existed in pockets (security, business continuity, vendor management), but evidence was fragmented and not consistently tied to critical service outcomes.
• The organization faced growing scrutiny over third-party concentration risk, especially where model development tools and data processing services relied on external providers.

The objective was not simply to “pass an audit.” It was to establish a repeatable compliance posture proving that AI-enabled infrastructure could support essential services under stress—cyber incidents, supply constraints, telecom outages, physical disruption, and staffing shortages—while maintaining safe operations.

Approach and Solution

The organization adopted a resilience-first compliance program that treated AI systems as operational dependencies and mapped them to critical services. The work was organized into five connected streams.

1) Define critical services and map AI dependencies

The program began with a service-oriented view: identify the essential services delivered, the minimum service levels, and the dependencies required to sustain them. AI components were then mapped into the dependency chain:

• Data sources (sensor feeds, maintenance logs, market data)
• Ingestion and transformation pipelines
• Feature stores and model training environments
• Model registries and deployment pipelines
• Inference endpoints used by operator consoles and back-office systems
• Alerting, ticketing, and workflow tools that turned predictions into actions

This exercise surfaced a key insight: several AI models were not directly controlling equipment, but their outputs were used to prioritize maintenance and optimize load balancing. If AI services degraded, the organization would not necessarily fail immediately—but it could drift into higher operational risk over days or weeks. That “slow burn” failure mode needed to be reflected in resilience planning.

2) Establish a CER-aligned resilience control framework for AI

Next, the organization translated CER expectations into practical controls for AI infrastructure. Instead of treating compliance as separate from engineering, the controls were expressed as design requirements and operational runbooks:

• Resilience by design: redundancy, failover pathways, and capacity planning for model-serving and data ingestion
• Operational continuity: defined manual fallback processes when AI outputs were unavailable or suspected to be unreliable
• Change control: governance for model updates, data pipeline changes, and configuration drift
• Incident management: AI-specific triage paths (e.g., data poisoning suspicion, model performance collapse, dependency outages)
• Third-party risk management: dependency inventory, contractual resilience expectations, and exit strategies for high-impact services

A major shift was the decision to treat key AI services as tiered resilience assets (similar to critical applications), rather than experimental analytics. That classification drove clearer recovery objectives and prioritized engineering work.

3) Harden AI infrastructure with availability and integrity safeguards

Several technical measures were implemented to support withstand-and-recover capabilities without overcomplicating the architecture:

• Separation of environments: stricter segmentation between OT-adjacent zones, enterprise IT, and development/training environments
• Resilient model serving: active-active or active-passive deployments for highest-impact inference services, with controlled degradation modes
• Data pipeline controls: buffering for intermittent connectivity, schema validation, and automated pipeline health checks
• Model integrity mechanisms: signed artifacts, controlled promotion from staging to production, and rollback procedures
• Observability: monitoring that combined infrastructure health with model health (latency, error rates, input drift indicators, output distribution shifts)

Importantly, resilience engineering was paired with operational realism. For example, not every model warranted high-availability deployment. The operator defined a model criticality rating based on service impact and time-to-harm, then applied controls proportionately.

4) Build response playbooks and test them with realistic scenarios

The organization updated business continuity and incident response practices to explicitly include AI infrastructure and AI failure modes. Playbooks were created for scenarios such as:

• Loss of telemetry feeds from specific regions
• Corrupted upstream datasets causing inaccurate predictions
• Outage of centralized feature store services
• Degraded inference performance due to capacity saturation
• Compromise of a service account used by deployment pipelines

Tabletop exercises were supplemented with controlled technical tests. In non-production environments, the teams simulated data delays, model endpoint failures, and forced failover events. The key outcome was not just technical recovery, but decision recovery—ensuring operators knew when to trust AI, when to switch to manual processes, and how to communicate status.

5) Create an evidence backbone for audits and continuous assurance

To avoid “compliance scramble,” the program established a structured evidence approach:

• A single inventory linking critical services → systems → AI models → data pipelines → third-party services
• Control mappings showing how each CER-relevant requirement was satisfied (policy, procedure, technical configuration, test evidence)
• A cadence for ongoing reviews: quarterly resilience checks for top-tier AI services and post-change reviews for production model updates

This turned compliance into an operational discipline. Teams could demonstrate not only that controls existed, but that they were maintained, tested, and improved.

Results

The program delivered practical compliance readiness while strengthening day-to-day operations. Results were tracked mostly through internal measures; where figures were used, they were treated as approximate and validated through operational reporting.

Key outcomes included:

• Clearer scope and accountability: AI systems were explicitly tied to critical service delivery, with named operational owners for models, pipelines, and serving platforms.
• Improved recovery capability: the highest-impact inference services gained tested failover paths and documented manual alternatives, reducing uncertainty during disruptions.
• Reduced change-related risk: model deployment and data pipeline changes adopted stronger promotion gates, artifact controls, and rollback readiness.
• Better incident handling: AI-specific runbooks reduced time spent diagnosing whether an issue was “infrastructure,” “data,” or “model behavior,” and improved cross-team coordination.
• Audit-grade evidence: documentation shifted from scattered artifacts to a consistent control-and-evidence structure aligned with resilience expectations.

A notable qualitative result was the change in operational posture: AI was no longer treated as an “add-on analytics layer.” It was treated as a resilience-relevant capability that required the same rigor as other critical digital services.

Key Takeaways

• Start from critical services, not from models. CER alignment is most defensible when AI dependencies are mapped directly to essential service outcomes and minimum service levels.
• Treat AI failure as an operational risk, even without direct control. Decision-support models can still create harmful conditions if they degrade silently or bias priorities over time.
• Use proportional resilience controls. Not all models need high availability, but all production AI systems need clear ownership, change control, and incident pathways.
• Combine infrastructure monitoring with model monitoring. Resilience depends on detecting both downtime and “wrong-time” behavior such as drift, degraded inputs, and abnormal output patterns.
• Document manual fallbacks as first-class controls. The ability to operate safely without AI—even at reduced efficiency—often determines whether disruption remains manageable.
• Build evidence continuously. A living inventory, control map, and testing cadence are more sustainable than one-off documentation efforts.

By reframing AI infrastructure as a resilience dependency and operationalizing CER expectations through engineering and governance, the energy and utilities operator strengthened both compliance readiness and real-world continuity under stress.