AuditBoard to acquire FairNow, adding automated AI governance to its risk platform
October 23, 2025
AuditBoard has agreed to acquire FairNow, an end-to-end AI governance platform. Financial terms were not disclosed.
The goal is simple: fold FairNow's intelligent, automated AI compliance guidance into AuditBoard's Connected Risk Platform so customers can maintain compliance and manage AI-related risk with less friction and more speed.
FairNow brings a streamlined AI registry, dynamic risk assessments, and automated compliance checks. Combined with AuditBoard's natural-language workflows, continuous auditing and monitoring, document intelligence, and agentic AI, the result points to a single platform that can run AI risk from intake to oversight.
FairNow founder and CEO Guru Sethupathy said: "FairNow believes that governance will be the key enabler in the age of AI - managing risks while building trust and driving adoption… Joining AuditBoard is the logical next step in our journey to scale our impact."
AuditBoard also launched Accelerate, an AI solution to boost workflow efficiency and document intelligence for GRC teams. CEO Raul Villar Jr. said the acquisition, paired with Accelerate, will set the platform apart and help customers transform risk and compliance programs.
What managers should know
- Expect tighter control over AI use: an AI registry to track models, owners, and purposes across the business.
- Faster compliance response: automated guidance mapped to policies and emerging regulations reduces manual review cycles.
- Continuous oversight: monitoring and dynamic risk scoring keep model risk current instead of point-in-time.
- Less swivel-chair work: natural-language workflows and document intelligence reduce context switching for GRC and audit teams.
- Clearer accountability: ownership, evidence, and auditability consolidated in one place.
Immediate action plan for GRC leaders
- Stand up or update your AI inventory: confirm owners, use cases, data sources, and business impact for each model.
- Align controls with recognized frameworks and regulations. If you don't have one, start with the NIST AI Risk Management Framework and track developments on the EU AI Act.
- Define evidence standards: what proof is required for model design, testing, bias checks, security, and monitoring.
- Set up lifecycle checkpoints: intake, approval, pre-production validation, and ongoing monitoring with clear SLAs.
- Assign RACI: product, data science, security, legal, compliance, and audit roles for each AI use case.
- Measure what matters: time to approve, number of issues per model, control coverage, and cost per review.
Why this deal matters
- AI experimentation keeps growing while regulations tighten. Centralized governance reduces risk without stalling delivery.
- Automation trims manual evidence collection and review cycles, freeing teams to focus on high-risk areas.
- End-to-end visibility improves board reporting and supports defensible decisions under scrutiny.
What to watch next
- Integration timeline: how quickly FairNow's registry and assessments surface inside AuditBoard workflows.
- Control mappings: depth of alignment to NIST, ISO/IEC standards, and pending regulations.
- Monitoring depth: drift detection, bias metrics, third-party model oversight, and incident response triggers.
- Pricing and packaging: whether AI governance features require separate licensing.
- Ecosystem connectors: data science platforms, model registries, issue trackers, and cloud services.
If your team needs to upskill on AI governance and risk, explore practical programs by role and certification paths: Courses by Job and Popular Certifications.
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