AI Governance Evidence Sprint

For leadership, compliance, finance, HR, IT or operations teams that need to structure AI use cases, accountable owners, data boundaries, vendor dependencies, review controls and evidence actions.

Problem

Why this matters

AI decisions are often discussed but not converted into a retained inventory, control evidence, owner decisions, escalation rules and management-ready actions.

Outcome

What RIXA delivers

A human-reviewed governance evidence pack with row-linked findings, a control/evidence matrix, evidence requests, management decisions and a 30 / 60 / 90 roadmap.

What the client receives

Concrete deliverables

AI use-case and ownership register

AI governance findings register

Control/evidence matrix

Evidence request tracker

Permitted-use and escalation decisions

Management brief

30 / 60 / 90 action plan

Business risk

Consequences of leaving it unresolved

Unclear AI accountability

Shadow or unapproved AI use

Weak data and vendor boundaries

No retained human-review evidence

Management decisions without traceability

Process

How the sprint works

Confirm scope and priority AI use cases

Collect intake and supporting evidence

Run an optional facilitated evidence session where facts are unclear

Analyse ownership, data, vendor, human-review and traceability controls

Generate row-linked findings and evidence requests

Run premium, output and traceability reviews

Deliver the reviewed management pack and roadmap

Professional limits

What this package is not

Not generic AI training

Not a legal AI Act classification opinion

Not model validation or technical security testing

Not compliance certification

Requires client and human reviewer validation

FAQ

Is this only a workshop?

No. A working session may be included, but the product is the reviewed evidence pack and action plan.

How is it different from the Quick Scan?

The Quick Scan covers up to five priority use cases and produces a compact baseline. The Evidence Sprint provides deeper evidence mapping, findings, controls, traceability and management outputs.

Next step

Start with a defined AI governance evidence question.

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