AI Governance & Risk
Move fast without breaking trust. Get practical governance that accelerates approvals, reduces risk, and keeps you audit-ready.
The Challenge
Regulatory uncertainty, missing model inventory, inconsistent documentation, and slow approvals block adoption.
Our Approach
Pragmatic policy set, control library, model inventory, and evaluation practices integrated into delivery—not bolted on.
Key Outcomes
Clarity on What's Allowed
Policies and decision rights that empower teams.
Audit-Ready Evidence
Documentation and approvals captured by default.
Reduced Risk Exposure
Systematic controls for quality, safety, privacy.
Faster Approvals
Lightweight gates aligned with real delivery.
Executive Confidence
Transparent reporting and oversight cadence.
Core Deliverables
- • Governance framework & RACI
- • Control library mapped to common regulations and internal policies
- • Evaluation templates and approval workflows
- • Model inventory and documentation standards
- • Training and adoption materials
Cross-links: Embed gates in MLOps & ML Systems; align responsibilities with AI & ML Architecture.
Our Method — AtelaMind Responsible AI Framework
Assess & Map
risks, policies, gaps
Define Controls
proportionate, testable guardrails
Embed in Delivery
templates, checkpoints, automation
Operate Oversight
forums, reporting, escalation
Review & Improve
continuous assurance and updates
Engagement Models
Case Study
Global Utility — Established AI policy set, model inventory, and evaluation cadence across regions and use cases.
FAQs
Are you legal counsel?
No—We align controls to regulations and your risk appetite, complementing Legal/Compliance.
Will this slow teams down?
We integrate lightweight gates into existing workflows.
How are you different from big firms or cloud PS?
Independent, senior-only, practical evidence over powerpoint; no product sales agenda.
How do you prove effectiveness?
Controls are testable; evaluation is measured and auditable.