8.6 Emerging

From voluntary framework to certifiable standard

A self-assessment workflow structured around four risk-management functions that maps a tool's answers onto defined certification tiers.

01 Emerging Challenges

Certifying a citizen-facing tool or agent requires a standard to certify against, and the standard has to say what passes and what does not. The risk-management frameworks that exist give a shared vocabulary and a sensible structure, but they are voluntary and set no threshold, so two builders can both claim to follow one and mean very different things.

The challenge is to turn a framework into a defined, auditable standard a tool can actually be certified against.

02 Assurance

Government needs a certification regime that rests on a recognized risk-management structure but goes beyond it: defined pass/fail thresholds, an auditable record of how a tool was assessed, and a path that lines up with international standards so a certified tool is not certified only locally.

03 Access

An all-or-nothing standard shuts out the small builder for whom full assurance is out of reach, leaving their tool uncertified and so unused even when it is sound. Keep the path open with graduated tiers built on the same framework, so a low-risk tool can reach a meaningful, achievable level of certification rather than failing the only bar on offer.

04 Response surface

Policy design Considered

The response this pattern proposes

A tiered self-assessment converts a tool's coverage of a regulator-referenced risk-management framework into a certification level, supplying the pass/fail tiers the framework itself leaves undefined.

No surface has been built yet; the approach above is the brief for one.

05 Maturity

Emerging

Emerging. Frameworks exist and are referenced by regulators, but no jurisdiction has yet implemented a mandatory, auditable certification regime for general-purpose civic technology tools based on these frameworks.

06 Precedents

NIST AI RMF 1.0 and subsequent updates (US, 2023–2026). The framework organizes AI risk management around four functions: Govern, Map, Measure, Manage. Generative AI risks, supply-chain vulnerabilities, and third-party model assessment are addressed in the Generative AI Profile (NIST AI 600-1, released July 2024), which gives LLM-specific guidance. Further deliverables announced by NIST — including the Cyber AI Profile and SP 800-53 Control Overlays for AI — are provisional and expected in 2026.

Regulatory cross-references. The FTC, CFPB, FDA, SEC, and EEOC all reference NIST AI RMF principles in enforcement guidance, and the framework's crosswalk to ISO/IEC 42001 means adopters are simultaneously building toward international AI management system certification.

Australian Government AI Assurance Framework (2024–2025). Australia's national framework for AI assurance in government was agreed by Data and Digital Ministers in June 2024. The APS AI Plan 2025 requires agencies to develop strategic AI adoption approaches, establish accountability for AI use cases, and undertake risk-based actions.

07 Transferability

High for risk-management structure; moderate as certification basis. The NIST RMF provides the vocabulary and structure a certification regime would assess against, but it is voluntary and defines no pass/fail thresholds.

A government pattern library should treat the RMF (or its Australian equivalent) as the reference framework and define certification tiers on top of it. The Australian AI Assurance Framework is a useful precedent, having taken a government assurance framework toward exactly this kind of structured, accountable use.

08 Where things go wrong

The RMF's Govern/Map/Measure/Manage discipline (accountability for each AI use case and risk-based action) is exactly the assurance process a flawed automated decision lacks. Applied as a mandatory, auditable regime, it forces explicit ownership and measurement of the risk.

09 Sources

7 references US · Australia