AI & Data Governance Advisory

AI governance that survives contact with production.

Most AI governance stops at policies and slide decks. That fails in production, where provenance, access, and traceability become audit exposure. Virtuellence finds where governance breaks, ranks the gaps by blast radius, and designs controls your platform can enforce.

Best fit for: Snowflake-first and modern cloud-data enterprises moving AI into regulated or high-stakes workflows.

governance-state / your-platform
RAW → CURATEDlineage tracedcontracts enforcedGoverned
DELIVERY / SHAREclearance tagsrow + column policyGoverned
AI / MODEL LAYERprovenance unknownno access boundaryUngoverned
DECISIONSauditable?reversible?At risk
The problem

Where AI governance breaks in production.

Governance that looked complete on paper tends to fail at four specific seams once models start making real decisions on real data.

Unknown provenance

Nobody can say with confidence which data trained or fed a model, or whether it should have been allowed to.

Weak access boundaries

Clearance and row/column policy stop at the warehouse edge; the model layer reads what it likes.

Untraceable decisions

When a regulator or auditor asks why a decision was made, the lineage doesn't reach the output.

Unenforceable policy

The governance program is a document. Nothing in the platform actually prevents the thing the policy forbids.

The first engagement

The 45-minute AI Governance Risk Review.

A focused working session on your actual data and AI platform. You leave with a clear read on your governance posture, whether or not we work together afterward.

  • 01A map of where your AI and data controls are enforceable today, and where they stop.
  • 02Your highest-risk gaps ranked by blast radius across access, lineage, provenance, and decision traceability.
  • 03A prioritized roadmap to close them before audit, regulatory, or production failure forces the issue.
Book the Risk Review
Engagements

Three ways to work together.

Scoped to a decision or a deliverable, not an open-ended retainer. You get an architect of record. Decisions made, ADRs written, the path to production owned.

01

AI Governance Readiness Sprint

2–4 weeks · before you scale AI

Design the enforceable substrate: lineage, access model, data contracts, and the metadata catalog that ties them together, so governance lives in the platform, not a policy PDF.

02

Production AI Assurance Review

fixed-scope audit

An adversarial pass over how data and models move through your systems. Where provenance breaks, where access isn't bounded, where a decision can't be traced or reversed. Findings ranked by real impact.

03

Fractional Data & AI Architect

ongoing · architect-of-record

Senior architecture capacity for teams standing up a governed data and AI platform without the leader in the seat yet. Direction set, decisions owned, the build kept honest.

How

The difference is where the controls live. Governance you can't enforce is just a document. Real assurance is built into the substrate: queryable lineage, enforceable clearance, and contracts the platform honors automatically.

The method

Diagnose before prescribe.

01

Diagnose

Map the real platform: how data and models actually move, not the architecture diagram.

02

Map risk

Locate where governance breaks and rank each gap by blast radius.

03

Prioritize

Sequence the controls that close the most exposure for the least disruption.

04

Implement

Design controls the platform enforces, and document them so they outlast the engagement.

Background

Built in production, at regulated scale.

  • +20+ years of data architecture leadership across Capital One, Disney, Halliburton, and public-sector / aerospace programs (HII / NASA, Booz Allen).
  • +Production Snowflake medallion & governance architectures, lineage, secure data sharing, and clearance-tag enforcement at regulated scale.
  • +SnowPro Advanced Architect and AWS Certified Solutions Architect – Professional. Verify credentials →
  • +Writing on data and AI governance architecture. Read the thinking →

How I work

One senior architect, not a pyramid. You work directly with the person making the decisions. No junior team learning on your platform.

Engagements are scoped and finite. The goal is to leave your team able to enforce governance without me, not to manufacture a dependency.

Every decision gets written down. ADRs, not verbal handshakes, so the reasoning survives staff changes and audits.

Start here

Find out where your AI governance actually stands.

Book the 45-minute Risk Review. You'll leave with a mapped risk surface and a prioritized roadmap, useful whether or not we work together.