Smart AI governance: the new baseline for enterprises and public sector teams
AI is moving faster than most oversight programs. The path forward is clear: make governance simpler and more precise so innovation can scale with confidence.
That's the core message from Guru Sethupathy, founder and chief executive officer of FairNow Inc., who discussed "smart governance" during an interview at Audit & Beyond. His view: organizations already have a decade of work implementing the AI that exists today - the constraint isn't technology, it's how we govern it.
AuditBoard Inc. recently acquired FairNow to expand its AI governance capabilities. The signal is strong: governance is becoming a first-class part of the AI stack, not an afterthought.
Why smart governance is critical
Many organizations will soon run dozens or even hundreds of AI systems across departments. Those systems will draft content, answer citizens, make recommendations, trigger workflows and handle sensitive data.
Without clear guardrails, you're guessing: Are outputs accurate? Are models drifting? Who approves high-risk use cases? If you're not governing that, the business risk is real - from privacy violations to procurement issues to public trust.
What "smart governance" looks like in practice
- AI inventory and risk tiers: Log every model, vendor, and use case. Classify by risk and data sensitivity.
- Policies mapped to controls: Translate policy to action using known frameworks such as the NIST AI Risk Management Framework NIST AI RMF.
- Evidence by default: Model cards, data lineage, prompts, decision logs, performance baselines - all auditable.
- Human-in-the-loop where it counts: Approval flows and escalation paths for high-impact decisions.
- Testing and monitoring: Bias checks, red-team results, drift detection, security posture, and fallback plans.
- Third-party oversight: Vendor due diligence, contract clauses for data use, and ongoing performance reviews.
- Incident handling: Clear thresholds, reporting channels, and post-incident reviews.
- Training and accountability: Role-based training, named owners, and metrics that leadership sees.
For government teams: fast, credible oversight
Public agencies need consistency, proof and transparency. That means procurement standards, algorithmic impact assessments, and records that stand up to audit or FOIA requests.
Anchor your program to recognized frameworks and laws. For example, align risk controls with the NIST AI RMF and track obligations under the EU AI Act where relevant to cross-border services EU AI policy overview.
A simple two-track plan
- Track 1 - Productize governance: Pick a platform or build with existing GRC tools to manage inventory, risks, approvals and evidence in one place. Reduce manual friction so teams can move.
- Track 2 - Accelerate adoption: Identify top AI opportunities, run quick risk triage, and push them through a standard approval and monitoring cycle. Short feedback loops, clear owners.
Questions leaders should ask now
- Where will AI drive outcomes this quarter, and who owns each use case?
- Do we have a complete inventory of models, vendors and prompts in production?
- Which policies apply, and what controls prove compliance?
- How do we measure accuracy, bias, and drift - and how often?
- When is human review required, and what's the escalation path?
- What's our incident playbook and evidence trail if something goes wrong?
What the AuditBoard-FairNow move signals
Governance is getting embedded into everyday operations. The near-term challenge isn't waiting for smarter models - it's making today's AI safe, measurable and auditable at scale.
Reduce friction, prove outcomes and keep receipts. That's how you ship AI with confidence - in business and in government.
Next steps and practical resources
- Review the NIST AI Risk Management Framework and map your current controls.
- Level up your team's skills with role-based learning paths: AI courses by job and popular certifications.
Context: The discussion referenced above took place during the Audit & Beyond event on theCUBE, a livestreaming studio. No sponsors had editorial control over the conversation.
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