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Governance that enables delivery—not blocks it.

A practical framework: risk tiering, control mapping, approval workflows, and audit-ready artifacts.

Overview

We help enterprises build practical AI governance programs that satisfy regulators, protect users, and scale with your AI adoption.

Who this is for

Ideal buyer / team

CISO, compliance, risk, or legal teams—often alongside AI/ML engineering leads

Best-fit workflow

AI use-case approval, risk assessment, vendor evaluation, and audit evidence generation

Typical environment

Regulated industries or organizations scaling AI adoption without consistent governance

Strong fit when…

When leadership, legal, or compliance teams are asking 'how do we govern AI?' and ad-hoc projects are proliferating without controls

When to use this

  • You're deploying AI in regulated industries (healthcare, finance, government)
  • Leadership or legal teams are asking 'how do we govern this?'
  • You need to demonstrate responsible AI practices to customers or auditors
  • Ad-hoc AI projects are proliferating without consistent controls

What you get

Use-case inventory + risk tiering
Control framework mapped to your regulatory landscape
Vendor / model risk assessment support
Data handling and classification policies
Audit artifacts (evidence packets, decision logs)
AI operating model (roles, review cadences, escalation paths)

What you'll hand stakeholders

AI System Risk Assessment — per-use-case risk tier with rationale
Control matrix — controls mapped to risk tier and regulation
Approval workflow with eval gates — staged sign-off from risk, legal, and engineering
Audit-ready packet — evidence bundle for internal audit or regulator review

Reference architecture

AI governance framework with risk registry, policy engine, and compliance reporting

  • Use-case registry with risk tiering (low / medium / high / critical)
  • Control library mapped to NIST AI RMF, EU AI Act, and sector regs
  • Approval workflow engine with eval-gate integration
  • Audit artifact generator (evidence packets, decision logs)
  • Vendor and model risk tracking with review cadence enforcement
  • Executive dashboard with governance scorecard

Typical timeline

Week 1–3

Governance assessment, use-case inventory, risk tiering, and policy drafting.

Week 4–6

Control framework build, vendor risk process, and approval workflow design.

Week 7–9

Audit artifact templates, dashboard development, and integration with eval pipelines.

Week 10–12

Training, operating model rollout, and first governance review cycle.

Success metrics

  • AI use cases inventoried and risk-tiered across the organization
  • Automated compliance reports for regulated use cases
  • Board-ready AI governance scorecard
  • <2 week model approval cycle time

Security & governance included

  • Role-based governance access
  • Immutable audit trail
  • Encrypted model registry
  • Policy version control
  • Segregation of duties enforcement

Integration-first

We connect to your stack

ServiceNow
Jira
Confluence
Microsoft 365
Azure AD
Okta

Frequently asked questions

Want to talk through your use case?

Book a 30-minute discovery call. We'll scope the workflow, map integrations, and outline security controls—no commitment required.