Senior-led AI governance advisory that turns principles into operating controls. Six areas — governance structure, risk, responsible AI, compliance, data, and lifecycle — built so AI can run in regulated, high-stakes environments with an audit trail, not a leap of faith.
AI governance breaks into six areas. Engage them together as a program, or start with the one that is on fire. Each is a scoped, senior-led engagement with an operating-grade deliverable — not a policy PDF that dies in a drawer.
Who decides, who owns, who signs off.
Stand up the structure that makes AI accountable — an oversight council, decision rights and RACI, tiered approval gates, and the policies that turn principles into operating rules people actually follow.
Tier the risk before it tiers you.
Risk-tier every use case and model, run model-risk reviews, and put proportionate controls, RAID logs, and monitoring against the risks that actually matter — from bias to security to silent failure.
Fair, transparent, and human-overseen.
Operationalize responsible-AI principles — fairness and bias testing, transparency and explainability, human-in-the-loop checkpoints, and disclosure standards — as controls you can evidence, not slogans on a slide.
Mapped to the frameworks assessors use.
Map your AI estate to the EU AI Act, NIST AI RMF, and ISO/IEC 42001; close the gaps; and assemble the documentation, evidence, and audit trail that make an assessment — internal or external — a formality.
Govern the data the model learns from.
Bring lineage, quality, and access control to the data feeding AI, and align it to privacy law (GDPR / CCPA) — consent, minimization, retention, and the controls that keep training and inference clean.
Governance that survives production.
Put governance into the model lifecycle itself — deployment approval gates, versioning and provenance, live monitoring for drift and degradation, and an incident-response path for when a model misbehaves.
Governance engagements start with an honest maturity read and end with controls wired into how your teams actually build and ship AI. No theater, no shelfware policy, no junior-staffed handoffs.
Governance maturity and risk assessment across your AI estate, mapped to the EU AI Act, NIST AI RMF, and ISO 42001. Gaps ranked by risk, not by checklist.
Target operating model, policies, risk tiers, and controls designed to your context and appetite — proportionate, not bureaucratic. Board-ready.
Stand up the council, approval gates, and lifecycle controls, and wire them into the tools and rhythm your teams already use to build and ship.
Evidence, audit trail, and live monitoring so governance holds in production — plus an incident path for when a model drifts or fails.
The whole point of Stratenity is AI that runs where most AI cannot — because governance is a kernel feature, not a bolt-on. The same discipline governs every engagement.
Every consequential AI output is versioned and traceable — source, model, prompt version, and approver. Audit logs queryable by workspace, actor, and time range.
Approval gates on consequential actions. AI accelerates the work; a human signs off on anything that ships to a customer, a board, or a regulator.
Every recommendation carries its reasoning — source documents, retrieval IDs, and assumptions. No unexplained outputs, no unaccountable decisions.
Mapped to the EU AI Act, NIST AI RMF, and ISO/IEC 42001 — so your governance speaks the language assessors, auditors, and boards expect.
Governed AI, deployed where it matters. Not a pilot. A capability.
