Transformation & Data · Virtual Department
AI Governance
An AI assurance team that lets the organisation use AI confidently — and prove it.
Mandate
Govern model use. Route requests through controls. Review every consequential output.
Cadence
Continuous routing · Daily review queue · Monthly assurance report
Reports to
CIO · CRO · Audit Committee
What this department delivers
Six outcomes that move every quarter. Calculated in Python, reviewed by a human, evidenced at source.
Model registry
Approved · restricted · banned
Routing policy
Use-case → model
Review queue
Human-in-the-loop
Prompt + output log
Auditable
Sensitive data guardrails
Per route
Assurance report
Regulator-ready
Inside the workflow
Five governed stages. Every output traceable from claim to source.
- 01
Catalogue
Input
Models · vendors
Method
Risk classification
Output
Approved registry
- 02
Route
Input
Use case
Method
Policy-driven routing
Output
Controlled access
- 03
Run
Input
Prompt + data
Method
Guardrailed call
Output
Logged output
- 04
Review
Input
Consequential outputs
Method
Human-in-the-loop
Output
Signed-off result
- 05
Report
Input
Activity
Method
Assurance pack
Output
Committee report
AI Governance — Weekly review pack
Trust by design
How AI Governance outputs are governed
- 1
Connect evidence
- 2
Validate & protect
- 3
Select method
- 4
Calculate
- 5
Explain
- 6
Review
- 7
Prove value
Signals it produces
Atlas-8 doesn't ship dashboards in search of a question — it ships decisions waiting for a sign-off.
- Unapproved model use
- Sensitive prompt detected
- Output review SLA breach
- Hallucination flagged
- Vendor change risk
- Cost-per-task drift
Without Atlas-8
- Shadow AI everywhere
- No log, no defence
- Sensitive data leaks
- Regulator-day surprises
With Atlas-8
- AI use governed by policy
- Every output traceable
- Reviewers in the loop
- Assurance on demand
Integrations & data sources
Add the AI Governance to your virtual organisation.