AI Governance Framework
Find the AI control gaps before your regulator, your customer, or your acquirer does.
AI governance failures rarely show up in a demo. They show up in an incident, an audit, or a diligence data room, after the damage is done. NexusDiligence's AI Governance Framework applies a structured, standards-based audit to the AI systems a portfolio company has already deployed, so gaps are found and fixed on your timeline, not someone else's.
Why this matters
Boards are increasingly asked to attest to AI risk oversight, and buyers are starting to diligence AI governance the way they diligence cybersecurity. Few portfolio companies can currently produce evidence of a formal AI risk process, inventory of models in production, documented testing, defined accountability, or incident response procedures specific to AI. That gap is now a value and exit risk, not just a compliance nice-to-have.
The audit framework
The audit is structured around the four core functions of the NIST AI Risk Management Framework (AI RMF 1.0), adapted for the pace and evidence standards of a portfolio-company environment:
Is there a named owner for AI risk, a documented policy, and a defined escalation path? Does accountability exist at the board or executive level, not just within an engineering team?
Is there a current inventory of AI/ML systems in production or pilot, including third-party and embedded AI (e.g., AI features inside SaaS vendors)? Are context, intended use, and impacted stakeholders documented for each?
Are deployed models tested for accuracy, bias, and reliability on a recurring basis, with results retained as evidence? Is there a defined process for evaluating vendor/third-party model claims?
Are there response procedures for model failure, drift, or a harmful output incident? Is there a change-management gate before a new AI system reaches production?
Findings are scored per function on a control-maturity scale and mapped to specific evidence artifacts (policies, logs, test results, tickets) rather than self-reported answers, consistent with the evidence-based approach used across all NexusDiligence platforms.
What you'll get at launch
- A function-by-function control maturity scorecard (Govern / Map / Measure / Manage)
- A prioritized remediation list ranked by exposure (regulatory, customer contract, exit diligence)
- Board-ready governance attestation language your company can actually support with evidence
- Alignment notes showing where existing SOC 2 / ISO 27001 controls already partially satisfy AI governance requirements, to avoid duplicate work
Benchmark & standards alignment
Primary framework: NIST AI Risk Management Framework (AI RMF 1.0) and its Generative AI Profile. Cross-referenced against ISO/IEC 42001 (AI management systems) and, where relevant to EU-exposed portfolio companies, structured to map cleanly to EU AI Actrisk-tiering concepts. This keeps the audit output usable regardless of which specific regulatory regime a portfolio company ultimately has to satisfy.
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