AI Data Gating
Policies sit between your stores and your pipelines, blocking sensitive fields before they ever reach a training run — with a full audit trail of every blocked field.
Solutions / Trust in AI
AI data governance that ships with your models
Control what data can be used in AI systems, enforce eligibility rules, and prove every model trained on data you can defend.
What's included
Model Bias Auditing
Automated scans flag skewed representation and proxy variables in training sets, so fairness reviews start from evidence instead of guesswork.
Dataset Readiness Certification
Grade every dataset for accuracy, lineage, and consent coverage. Models train only on certified data — uncertified sets are quarantined automatically.
How it works
From raw stores to certified training data
- 1
Inventory training sources. Map every store, bucket, and feature table your ML teams touch.
- 2
Set eligibility rules. Define which categories, consents, and quality grades each use case may consume.
- 3
Gate the pipelines. Enforcement runs inline — sensitive data is masked or blocked at ingestion.
- 4
Certify and document. Every model ships with lineage, bias results, and an approval record auditors accept.
"We gated three training pipelines in the first month. Our first governed forecasting model passed legal review in days — the previous one took four months."
VP of Data Platform, Atlas Logistics — read the case study