ENS Compliance for AI Systems in Public Administration: A Practical Guide

Why the ENS matters for AI projects in public administration

Royal Decree 311/2022 (National Security Framework, ENS) requires Spanish public administrations to implement technical and organizational security measures proportional to risk. When AI systems are introduced (models, data pipelines, citizen-facing interfaces), securing infrastructure is not enough: you must integrate specific controls throughout the model lifecycle.

Beyond the ENS, AI projects in the public sector must align with the GDPR (data protection) and emerging obligations from the EU AI Act. This guide offers practical steps to turn regulatory requirements into concrete controls applicable to municipal projects or public entities.

Quick mapping: ENS controls relevant to the AI lifecycle

• Inventory and classification: record AI assets (models, datasets, APIs) and classify their potential impact on service delivery and citizens' rights.
• Access and privilege management: identity controls for developers, operators, and vendors; separation of environments (development, staging, production).
• Information protection: encryption in transit and at rest, key management, pseudonymization of personal data, and data minimization.
• Logging and auditing: traceability of data inputs/outputs, model versions, and decision logs (records of relevant inferences).
• Continuity and incident response: recovery plans, regular testing, and management of model-specific vulnerabilities (e.g., data poisoning).
• Procurement and vendors: contractual clauses to ensure ENS compliance, control of subcontracting, and audit rights.

Operational checklist (8 priority steps)

1. Inventory and impact analysis (2–3 weeks)
   • List all AI systems in active projects or pilots.
   • For each: data processed, automated functions, affected services, and risks to citizens' rights.
2. Classification and proportional measures (1 week)
   • Assign an impact level that guides required measures (e.g., high if it affects benefits or rights).
   • Document the decision for ENS audits.
3. DPIA and algorithmic risk assessment (2–4 weeks)
   • Carry out a Data Protection Impact Assessment (DPIA) and a technical risk assessment (bias, robustness, transparency).
4. Design minimum technical controls
   • Encrypt sensitive data; pseudonymize before training.
   • RBAC and multi-factor authentication in production environments.
   • Version models and datasets; preserve artifacts for potential audits.
5. Record traceability and explainability
   • Maintain inference logs, relevant inputs, and basic explanations for decisions in critical cases.
   • Technical documentation (model card) describing scope, limitations, and metrics.
6. Contractual clauses and vendor testing
   • Include ENS and GDPR requirements in contracts: subcontracting, data location/transfers, audits, and incident notification obligations.
7. Deployment procedures and continuous monitoring
   • Automated regression tests, data drift monitoring, and performance/security alerts.
   • Rollback plan with clear criteria.
8. Training, governance, and compliance testing
   • Clear roles (data controller, system owner, security administrator).
   • Incident drills and periodic review of controls.

Practical examples: proportional controls by use

• Citizen-facing chatbot (access to personal data): perform a DPIA, log relevant interactions, encrypt data at rest and in transit, enforce strict access control, and ensure human intervention for sensitive decisions.
• Internal analytics system to optimize collection routes: minimize personal data, apply pseudonymization, restrict access, and monitor operational drift.

Integration with GDPR and the EU AI Act

• GDPR: ensure legal bases for processing, provide transparent information, and enable mechanisms to exercise rights (access, rectification, objection).
• EU AI Act: determine whether the system is "high-risk" (critical services, automated decisions that affect rights) and prepare required documentation (registry, conformity assessment, post-market monitoring).

Quick implementation recommendations

• Prioritize high-impact assets and interactions that involve citizens directly.
• Start with organizational controls (roles, contracts, DPIA) that don’t require immediate technical changes.
• Automate basic monitoring (logs, metrics) to detect issues early.
• Include ENS requirements in new tenders and review existing contracts.

Clear action for the reader

Take 60–90 days to complete these initial steps: 1) inventory and classification, 2) DPIA and risk assessment, 3) ENS/GDPR contractual clauses with providers, 4) monitoring and rollback plan. Document each phase to demonstrate compliance in audits. If you need methodological support or a baseline assessment, tools like OptimGov Ready can speed up diagnosis and the roadmap.

Takeaway: securing AI in public administration is not only a technical challenge; it requires embedding ENS controls from inventory through continuous monitoring. Start by identifying the highest-impact assets and apply proportional measures over the next 8 weeks.