3HUE builds AI governance that fits how the business actually runs — across data, vendors, model risk, human review, and executive decision rights — before adoption expands beyond what security and compliance can support.
CISO and risk leadershipLeaders building AI control frameworks that connect to existing security, privacy, and GRC programs.
Legal, compliance, and procurementTeams managing AI regulatory obligations, acceptable use policies, and vendor contract requirements.
Technology and data leadershipCTOs, data leads, and architects who need governance guardrails before AI systems move into production.
Policy and framework development sessions with security, legal, compliance, and technology stakeholders — scoped to your operating model.
Founder-led advisory with direct involvement in framework design, policy drafting, and stakeholder alignment.
AI governance policy suite, risk register, vendor oversight framework, decision rights matrix, and IR integration guide.
The structural elements that make AI governance operational and auditable.
Clear guidance on which AI applications are approved, which require oversight, and which are prohibited — with rationale employees and auditors can follow.
Defines when human review is mandatory before AI output is acted upon, who is responsible, and how override decisions are documented.
Maps AI decision authority to specific roles, defines escalation thresholds, and establishes governance bodies responsible for AI oversight.
Establishes ongoing monitoring requirements for AI systems in production and a defined governance review cycle aligned to business risk.
AI governance is not a standalone program — it maps to the controls and frameworks already in place.
AI governance controls map to your existing ISMS, risk register, and security policy structure rather than operating as a parallel program.
AI governance artifacts — policies, vendor assessments, risk registers — are structured to support SOC 2 Type II and other assurance requirements.
Framework designed with GDPR, AI Act, NIST AI RMF, and sector-specific obligations (financial services, healthcare) in mind.
Governance outputs are structured to integrate into existing GRC tools and evidence management workflows.
Signs you need AI governance now — before the next audit cycle, enterprise sales cycle, or incident.
AI tools in production without documented acceptable use policy or vendor oversight.
Enterprise customers or boards asking for AI transparency, risk documentation, or SOC 2 coverage of AI systems.
AI decisions affecting customers, employees, or regulated processes without defined human review requirements.
Legal or compliance team uncertain about AI obligations under existing privacy, data, or sector-specific regulations.
Start with the AI Snapshot to surface gaps, then build the governance framework your program needs before scale.
