Inventory and classify
Use cases, data classes, and model versions are registered with owners, DPIAs, and control objectives.
/ Knowledge & Governance /
AI your board, legal team and regulators can sign off on.
Build the controls, audit trails and risk framework that turn AI deployments from a liability into a governed, defensible part of your operations.
The problem
Shadow AI running with no visibility or control
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Shadow AI with no visibility or control
Teams are using AI tools nobody approved, on data nobody audited, producing outputs nobody can explain. The exposure grows daily.
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EU AI Act obligations with no implementation path
Legal teams understand the regulation. Engineering teams don't. Nobody has translated policy into architecture.
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Governance that arrives after the build
Risk and compliance reviews happen at the end — when changing the system is expensive and the business is already committed.
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Controls that block adoption
Heavy manual review creates bottlenecks. Teams bypass governance rather than work within it, recreating shadow AI.
How it works
Governance that travels with the model—not a binder on a shelf
Embed controls
Human review, logging, drift checks, and rollback hooks are wired into prompts, tools, and deployment pipelines.
Report continuously
Dashboards for committees and regulators tie incidents, changes, and attestations to live systems.
Frameworks like EU AI Act readiness map to concrete checks—not checklists only.
AI your board, legal team and regulators can sign off on.
What's included
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
AI inventory and risk classification
Maps all AI use cases in your organisation and classifies them by EU AI Act risk tier.
Governance framework design
Defines ownership, accountability (RACI), review cadence and escalation paths for every AI system.
Human oversight architecture
Designs the oversight layer for high-risk systems — who reviews, what triggers review, how decisions are logged.
DPIA patterns for AI
Data protection impact assessment templates adapted for AI and agentic systems.
Audit trail infrastructure
Logging and traceability layer across AI systems so every decision can be explained and reviewed.
EU AI Act compliance readiness
Gap analysis against current obligations and a sequenced implementation path — not legal advice, operational delivery.
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Results
What changes when this runs in production
Results vary by number of AI systems, regulatory context and existing documentation maturity.
6–10 weeks
From gap analysis to first compliant AI system in production
–80%
Share of ungoverned AI tools brought under formal oversight
–60%
Reduction in time to produce compliance documentation for a given AI system
How we work
From policy PDFs to operating rhythm for AI and data risk
Frame risk
Frame risk
Week 1–2
Use cases, data classes, and regulatory hooks are catalogued with accountable executives.
Design controls
Design controls
Week 3–5
Lifecycle gates, documentation, and monitoring are aligned to EU AI Act and internal policy.
Pilot register
Pilot register
Week 6–9
A subset of models and vendors runs through the full workflow; gaps become a backlog.
Enterprise embed
Enterprise embed
Week 10+
Dashboards, attestation cycles, and third-party reviews integrate with existing GRC tools.
Maturity and decentralisation of AI adoption change workload; we align waves to board priorities.
Get started
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.