Practitioner Resources
Resources
Frameworks, playbooks, checklists, reference architectures and toolkits, published as the practice develops them.
Case 01: Customer AI assistant launch decision (simulated case)
A simulated pre-launch security finding raises a difficult deployment decision. The architecture, proposed controls, test evidence and conditions that would need to be met before approval could be considered.
Case 02: The vendor's AI was approved to predict failures. Its access told a different story.
Reconciling contract, capability, evidence and approval before an industrial vendor AI deployment.
Case 03: The certificate predates the feature.
Separating when assurance was issued, what it covers, and whether it establishes anything about a new AI feature added to a business-critical SaaS platform.
The ADGL Methodology
The full AI Deployment Governance Lifecycle (ADGL) methodology: five phases, eighteen operational controls, risk scoring, RACI and a twelve-week delivery schedule, for governing an AI deployment before it reaches production.
Executive Capability Overview
A concise overview of AI for U&I's advisory approach, governance methodology, engagement models, and practitioner experience for executive decision makers.
AI Governance Maturity Framework
Assess where AI governance actually stands, across ownership, risk evaluation and assurance, built on the OPERA methodology.
Third-Party Risk Assessment Checklist
Assess vendor risk proportional to criticality and data access, from onboarding through ongoing monitoring.
DPDP Readiness Briefing
India's Digital Personal Data Protection Act, framed around the governance decisions it actually requires.
Technology Risk Whitepaper
Building risk appetite statements and control ownership models that hold up under audit.
Discuss a governance challenge
Most engagements begin with a working conversation about a specific problem, not a formal proposal.