AI Trust & Data Access Framework

Project: AI Trust & Data Access Framework

Situation

A European client needed a consistent way to use sensitive enterprise data for BI and AI without losing control of security, privacy, accountability, and regulatory requirements.

Task

Turn governance requirements into an operating product covering data classification, provider assessment, security, privacy, approval, and ongoing review.

Action

Structured AI and BI use-case registration around purpose, users, data, outputs, ownership, and platform choice
Mapped data flows across sources, transformations, analytical models, and AI interfaces
Defined classification criteria and connected them to controls and approval levels
Established assessment criteria for AI providers and platforms, including retention, transfer, access, logging, and contractual safeguards
Added screening and escalation paths for privacy, compliance, and AI risk
Defined approval records, control packages, reassessment triggers, and lifecycle monitoring
Used workshops across management, business, data architecture, BI, security, and privacy teams to establish responsibilities

Result

Delivered a repeatable framework for moving an AI or BI idea from business purpose and data assessment to controlled approval and ongoing review.

Technologies

AI governance, data classification, BI governance, data architecture, cloud security, privacy assessment, provider due diligence, approval workflows