Methodology

A structured view of AI governance readiness.

SHIELD translates recognised obligations and management practices into six areas leaders can examine, evidence and improve.

Six dimensions

What SHIELD examines

Whether AI use is tied to a stated strategy with named owners, budget and measurable outcomes.

Controls that catch wrong, harmful or fabricated output before it reaches a customer or a decision.

Whether the data and systems behind AI use are governed, secure and fit for the purpose claimed.

Cost, usage and performance discipline across AI spend, including run-away usage controls.

Mapping of AI use to the obligations that actually apply, with records that would survive review.

Whether the board sees AI risk on a regular cadence, with escalation routes and recorded decisions.

Regulatory mapping

Each baseline has a different legal status and scope. SHIELD shows alignment separately rather than combining them into a single claim of compliance.

European Union

Risk-tiered obligations for providers and deployers of AI systems touching the EU market.

Sets the classification, documentation, transparency and human-oversight duties that other regimes are converging on.

ISO/IEC

Certifiable management-system requirements for governing AI across its lifecycle.

The practical checklist an auditor uses: policy, roles, impact assessment, controls, review.

Hong Kong regulators

Expectations for licensed and authorised institutions using AI in regulated activity.

Defines senior-management accountability, model validation and outsourcing duties in this market.

US NIST

Voluntary framework organised around Govern, Map, Measure and Manage.

Gives a common language for describing AI risk to boards, clients and counterparties.