DATA GOVERNANCE
Govern with confidence, without stopping the business.
Data compliance and innovation can go hand in hand, and they must. Our approach governs data as it moves through each AI use case, so controls build up as value is delivered rather than holding everything back.
THE PROBLEM
AI amplifies existing data risk
Agents draw on many sources at once, so all data becomes critical. Over-retention, data loss and IP leakage take on new weight, and the idea of reporting quality only against critical data elements starts to look outdated.
The answer is not a bigger central team. It is the right administrative metadata, sensitivity labels and privacy rules, applied automatically and checked in real time.
PRINCIPLES
How we govern data for AI
Drawn from published thinking and from current-state data discovery work across multi-brand, multi-jurisdiction estates.
SHIFT 01
Tag at ingestion, not downstream
Move from fixing data after the fact to tagging, classifying and labelling it as it enters the organisation. AI can help do this intelligently.
SHIFT 02
Govern all data, end to end
Structured and unstructured, from the point it enters to the point of use. Not just what sits in the data warehouse.
SHIFT 03
Controls embedded by design
Clearly defined policies, rich metadata and real-time controls that prevent breaches of policy as they happen, not quarterly reviews that find them later.
SHIFT 04
Curate data products, then step aside
Self-service reporting is here. The data office curates trusted data products so the business, and its AI assistants, can self-serve safely.
PRACTICE
Map by operating domain, not by brand
Front, middle and back office, plus reporting. Common patterns appear before brand-by-brand detail, so discovery is faster and cheaper.
PRACTICE
Retention is a rules matrix, not a single setting
Retention rules vary by jurisdiction, data category and controller. Published policy is compared against what actually happens to data before AI touches it.
PRACTICE
Purpose-scoped data movement
Data moves into analytics and AI platforms only for named purposes, with personal data segregated or masked, and exposed through governed semantic layers.
PRACTICE
Traceability over autonomous generation
AI speeds up evidence production, but every flow, risk and finding cites its source, is reviewed by a person, and discloses where AI helped.
TYPICAL DELIVERABLES
Evidence your board can rely on
Lightweight, portable outputs your teams can maintain. No new platform dependency.
Application inventory and high-level data-flow maps
Sensitive-data register and records of processing
Retention rules matrix, comparing policy with actual practice
AI, cyber and privacy risk register, with every finding linked to its source
A prioritised remediation roadmap and an executive summary