AI security review for government & public sector

Government and public sector AI operates under obligations that no private-sector deployment shares. Automated decisions affecting citizens' rights — benefits eligibility, visa processing, fraud detection, law enforcement risk scoring — trigger the highest tier of AI Act classification. National AI strategies impose sovereign infrastructure requirements. Freedom of information laws require explainability that most AI systems were never designed to provide.

The EU AI Act classifies many public-sector AI applications as high-risk or prohibited outright. Social scoring is banned. Biometric identification in public spaces is restricted. AI used in migration, asylum, and border control management falls under the strictest obligations. Procurement teams must demonstrate conformity assessment evidence before deployment — not after an incident.

Beyond regulation, there is the democratic obligation: citizens cannot opt out of government services the way they can switch banks. When AI makes or informs decisions about benefits, enforcement, or access to services, the standard of evidence for that system's safety is fundamentally higher than commercial AI.

Government AI faces attack surfaces that commercial threat models underweight:

• Prompt injection in citizen-facing AI. A chatbot providing guidance on benefits, tax obligations, or immigration procedures is a high-value target for prompt injection — attackers can attempt to elicit incorrect guidance that citizens rely on to their detriment, or extract information about internal decision logic that enables gaming the system.
• Agentic AI with administrative decision authority. An AI agent that can approve, deny, escalate, or flag cases in administrative proceedings requires approval boundaries and human-in-the-loop controls that are legally mandated under Article 14 of the EU AI Act. The blast radius of an autonomous incorrect decision is a citizen's rights — not a commercial loss.
• RAG over classified and sensitive documents. A retrieval-augmented system over policy documents, case files, intelligence reports, or internal communications requires security-classification-aware retrieval. Standard vector similarity search does not respect document classification levels, need-to-know boundaries, or temporal declassification schedules.
• Multi-jurisdiction AI deployments. A system deployed across member states, federal and state agencies, or cross-border partnerships faces conflicting data sovereignty requirements, different national AI strategies, and divergent transparency obligations — each jurisdiction may impose additional controls beyond the base EU AI Act requirements.
• Supply-chain risk in AI procurement. Government AI systems often involve commercial model providers, system integrators, and cloud infrastructure vendors. Each link in the chain introduces dependency on third-party model behaviour, data handling practices, and infrastructure sovereignty that must be assessed and controlled independently.

Drel maps every assessment to the frameworks most relevant to government AI procurement and governance:

• EU AI Act (full scope) — prohibited practices identification, high-risk classification, conformity assessment evidence, Article 9 risk management, Article 13 transparency, Article 14 human oversight
• ISO/IEC 42001 — AI management system evidence for government AIMS certification, clauses 6 and 8 mapped to procurement requirements
• NIST AI RMF 1.0 — govern, map, measure and manage AI risk aligned with US federal AI directives and EO 14110
• OECD AI Principles — transparency, accountability, robustness, and human-centred values mapped to national AI strategy requirements
• OWASP LLM Top 10 — controls for LLM-specific application risks in citizen-facing systems
• OWASP Agentic Top 10 — controls for agentic AI systems with administrative tool access
• National AI strategies — mapped coverage for member-state-specific requirements beyond the EU AI Act baseline

Government AI procurement is committee-driven. Multiple stakeholders must sign off: the CISO for security posture, the DPO for data protection impact, the business owner for operational fitness, the AI governance lead for regulatory compliance, and often an ethics board for societal impact. Drel's multi-stakeholder sign-off workflow is designed for exactly this structure.

The clearance record maps directly to procurement documentation requirements: threat model evidence for conformity assessment, control plan with owners and deadlines for risk mitigation plans, evidence gaps flagged as blocking production for go/no-go decisions, and re-review triggers for ongoing monitoring obligations.

For government teams managing portfolios of AI systems across departments, Drel's cross-assessment views provide the AI system inventory that Article 60 registries and national supervisory authorities increasingly require. Each system carries its clearance status, residual risk profile, and control implementation timeline — the oversight dashboard that governance committees need to discharge their obligations.

The EU AI Act Article 13 requires that high-risk AI systems be designed to enable human understanding of their outputs. Freedom of information requests increasingly target AI decision-making logic. Citizens have the right to meaningful information about the logic involved in automated decisions under GDPR Article 22.

Drel's assessment record documents the system architecture, data flows, decision boundaries, and human oversight mechanisms in a format that can be disclosed to oversight bodies without revealing proprietary model weights or security-sensitive implementation details. The evidence basis classification (explicit, inferred, assumed, verified) provides the audit trail that demonstrates due diligence in risk assessment.