Data Governance for AI: Control, Audit, and Scale Every Model in Production
We build data governance for AI programs that make every model, dataset, prompt, and agent traceable, reviewable, and compliant. From model registries and lineage to approval workflows, risk tiering, and EU AI Act readiness, your platform, risk, and ML teams get one governed operating model for production AI.
Turn fragmented AI experiments into a controlled, auditable model lifecycle, without slowing delivery.
- Model registry with versioning, ownership, and environment promotion
- Dataset and feature lineage across training, evaluation, and inference
- Policy-as-code for access, PII, bias, and usage controls
- Risk tiering and approval workflows aligned to EU AI Act and NIST AI RMF
- Continuous monitoring of drift, fairness, and prompt-injection risk
Why do AI models in production still fail audit, compliance, and risk reviews?
Most enterprises have dozens of models and GenAI use cases live, but no single source of truth for what is deployed, who owns it, what data it was trained on, or how it is monitored. That gap turns every audit, incident, or regulator request into a fire drill and blocks scaling AI across business units.
Architecture and Technical Building Blocks
Central registry federated across AWS SageMaker, GCP Vertex, Azure ML, and Databricks.
Connects your data catalog (Unity, Collibra, DataHub) to features, models, and endpoints.
OPA or Cedar enforcing access, residency, and usage at build-time and run-time.
Pipelines for drift, bias, hallucination, and prompt-injection.
Immutable, tamper-evident retention for regulator-grade evidence.
Jira, ServiceNow, and GRC tools for approvals, incidents, and DPIAs.
How We Work: From Governance Assessment to Run
We inventory models, datasets, prompts, and agents, map them to regulatory obligations, and deliver a gap report with a prioritized remediation backlog and target operating model. (2 weeks)
We define risk tiers, policies-as-code, model card and DPIA templates, RACI, and the target governance architecture, signed off by Legal, Risk, Security, and Data/AI leadership. (2-3 weeks)
We deploy the model registry, lineage, policy engine, monitoring, and audit logging; integrate with CI/CD and existing ML platforms; and migrate priority models under governance. (6-8 weeks)
We onboard teams use-case by use-case, train model owners and reviewers, wire approval workflows into delivery, and validate controls with a dry-run audit. (4-6 weeks)
We operate monitoring, audit evidence generation, policy updates, and quarterly control reviews under SLA, so governance keeps pace with new models and regulations. (ongoing)
Business Impact of Production-Grade Data Governance for AI
70-90% faster audit response through auto-generated model cards and evidence
50% shorter time-to-production for new models via standardized approval workflows
100% coverage of production models, prompts, and agents in a single registry
30-40% reduction in AI-related incidents through drift, bias, and injection monitoring
Who This Technical Service Is For
What We Deliver: AI Model Governance Platform and Operating Model
We give platform, risk, and ML teams one governed system of record for production AI, covering classical models and GenAI under the same controls.
Frequently Asked Questions
Data governance for AI extends traditional data governance to cover models, features, prompts, and agents, not just datasets. It adds model registries, lineage from data to deployed endpoints, risk tiering, bias and drift monitoring, and controls specific to LLMs and agentic systems, while still relying on your existing data catalog and quality tooling.
No. You need one governance model that covers classical ML and GenAI, with LLM-specific extensions. We extend your registry, policy engine, and monitoring with prompt versioning, evaluation for hallucination and toxicity, prompt-injection defenses, and PII redaction, so GenAI is governed under the same operating model as traditional models.
Directly. We map each AI use case to EU AI Act risk tiers, implement the required controls (risk management, data governance, technical documentation, logging, human oversight, accuracy, and robustness), and auto-generate conformity evidence. The same framework also covers NIST AI RMF, ISO/IEC 42001, GDPR, and SOC 2.
Yes. We work with MLflow, SageMaker, Vertex AI, Azure ML, Databricks Unity Catalog, Collibra, DataHub, Alation, and OPA/Cedar for policy. The goal is to add a governance layer on top of what you already have, not to replace your ML platform or data catalog.
Typically 10-14 weeks from kickoff to first governed production models: 2 weeks assessment, 2-3 weeks policy and architecture, 6-8 weeks implementation, with incremental rollout afterward. Audit-ready evidence for priority use cases is usually available within the first quarter.
Ownership is explicit. Each model, dataset, and prompt has a named business owner, technical owner, and risk reviewer. Approvals are wired into CI/CD with policy-as-code, and every decision is logged. We define the RACI with your Data, ML, Risk, Security, and Legal teams during the assessment phase.
Third-party models, APIs, and GenAI vendors are registered in the same registry with their risk tier, data-sharing terms, and monitoring hooks. We enforce allowed-use policies, log every call, and apply PII redaction and evaluation suites at the gateway layer, so vendor AI is governed on equal footing with in-house models.
Make Every AI Model in Production Auditable, Compliant, and Scalable
Book a 30-minute, no-obligation governance assessment. We review your current AI inventory, regulatory exposure, and platform maturity, then send you a prioritized roadmap for data governance for AI, including quick wins you can ship in the next 30 days.
Discovery call
A 30-minute review of your AI inventory, regulatory exposure, and platform maturity.
Governance assessment
We map models, datasets, and prompts to obligations and deliver a prioritized roadmap.
Implementation
We deploy registry, lineage, policy, and monitoring, then roll out use-case by use-case.