lakeFS for AI Governance and Compliance
Governance and Compliance Built Into Your AI Data Infrastructure
Regulations like the EU AI Act, GDPR, and FDA rules are catching up with enterprise AI initiatives, and most AI and data infrastructures are not ready.
lakeFS builds governance into the infrastructure itself: controlled access, preventive data controls, and compliance evidence – a light layer, with data staying in place, so your AI teams don’t slow down.
- Reduced risk
- Lower cost
- Faster AI delivery
- Peace of mind
The compliance clock is ticking
Regulators now expect proof of what data trained your AI systems, and what data agents accessed or modified.
Fines and penalties reach into the millions or even a percentage of global revenue.
Why AI stacks fail audits
Your stack has the pieces. It can't connect them. And it can't control them.
The systems in your stack may include audit logs, time travel, snapshots, or run histories. But none of them can tie the pieces together across everything an AI system actually touches: structured data, images, documents, video, and the metadata that carries the context. And none of them govern what your teams, tools, and agents do with that data as a whole.
That connected, controlled picture is exactly what regulators and auditors ask for:
- Which data trained which model?
- Can you reproduce a model's or an agent's behavior?
- What data did an agent access or modify?
5%
Only 5% of organizations say their data is adequately ready to support AI
Source:
Dun & Bradstreet AI Momentum Survey, 2026
Governance built into the infrastructure
The Solution: One control plane between your data and your AI
lakeFS solves this problem with a light infrastructure layer that sits between the data and the AI technology consuming it – data stays in place, and nothing gets replaced. As the control plane for AI-ready data, lakeFS makes governance hold across every tool, user, and agent by design, not by wiring it up system by system and data source by data source.
Controlled and isolated data access
Manage access for tools, users, and agents from one place. Every data consumer works in an isolated, zero-copy environment on production data, governed centrally.
Preventive data controls
Enforce data contracts and quality gates by policy. Bad or non-compliant data is stopped at the door, before it reaches production, and any mistake rolls back in seconds.
Compliance evidence, built in
Every model is tied to the exact dataset version behind it. Audit trails and lineage are captured
automatically across every workload and agent action, so
audits are answered from built-in
evidence, not manual reconstruction.
AI is powered by multimodal data. Govern all data types equally: structured, semi-structured, and unstructured, including the metadata that carries the context. One control plane. Every data type.
Business Impact
- Reduced risk
Preventive controls and rollback in seconds shrink the blast radius of any mistake.
- Lower cost
Less compliance plumbing per project, less audit labor, no duplicated storage.
- Faster AI delivery
Less data wrangling and manual governance work per team. AI ships instead of stalling.
- Peace of mind
THE DIFFERENCE
With and without lakeFS
With lakeFS
Audit readiness
Evidence is reconstructed by hand: months of team time per audit, while approvals wait.
Every model is tied to an immutable dataset version, with a verifiable history that shows exactly what changed, when, and by whom.
Every new data consumer widens risk, and access is re-configured system by system.
When things go wrong
Errors propagate silently into production;
recovery takes hours or days.
Bad data is stopped at the door, and mistakes roll back in seconds.
Audit readiness
Without lakeFS
Evidence is reconstructed by hand: months of team time per audit, while approvals wait.
With lakeFS
Built-in evidence and a complete chain of custody, ready when the auditor asks.
Model-to-data traceability
Without lakeFS
With lakeFS
Data access for teams and agents
Without lakeFS
With lakeFS
When things go wrong
Without lakeFS
With lakeFS
REGULATIONS
Built for the frameworks your AI will be measured against
From the EU AI Act’s data governance and logging obligations to FDA 21 CFR Part 11, GxP, GDPR, ISO 26262, and the NIST AI RMF, lakeFS supports the traceability, reproducibility, and audit evidence required.
EU AI Act (Reg. (EU) 2024/1689, Art. 10 & 12) From Aug 2, 2026
What it requires
Data governance for high-risk AI systems, including provenance and traceability between datasets and model versions, and automatic event logging.
HOW lakeFS HELPS
Immutable commits tie each model to the exact dataset version behind it. Built-in lineage and a complete version history support the traceability and records these articles call for.
Timeline & exposure: Transparency obligations apply from August 2, 2026. High-risk obligations follow on December 2, 2027 (stand-alone systems) and August 2, 2028 (AI embedded in regulated products, including medical devices). Fines for high-risk violations reach €15M or 3% of global revenue. Source: eur-lex.europa.eu
FDA 21 CFR Part 11 (electronic records and signatures) IN FORCE
What it requires
HOW lakeFS HELPS
Timeline & exposure: In force. Non-compliance can trigger FDA 483 observations, warning letters, and product recalls or import bans. Source: ecfr.gov
FDA GxP / AI-ML in regulated products IN FORCE
What it requires
HOW lakeFS HELPS
Timeline & exposure: In force. Gaps can delay submissions and clearances, or draw findings during inspection. Source: fda.gov
GDPR (Reg. (EU) 2016/679) IN FORCE
What it requires
HOW lakeFS HELPS
Timeline & exposure: In force. Fines up to €20M or 4% of global revenue for the most serious violations. Source: eur-lex.europa.eu
ISO/IEC 42001 (AI management systems)Industry standard
What it requires
An auditable AI management system covering the full AI lifecycle, with Annex A controls for data governance – including data quality, provenance, and documented data management processes (control A.7).
HOW lakeFS HELPS
Versioned datasets, automatic lineage, and immutable history supply the data provenance and documented data-management evidence the standard’s data controls call for.
Timeline & exposure: Voluntary but certifiable – unlike the NIST AI RMF, it sets auditable requirements. Published December 2023, and increasingly requested by enterprise buyers and used to demonstrate readiness for the EU AI Act. Source: iso.org
ISO 26262 (automotive functional safety) Industry standard
What it requires
HOW lakeFS HELPS
Versioned datasets and immutable history keep the data side of the trace chain intact and reproducible, so evidence can be reconstructed exactly at audit time.
Timeline & exposure: Industry standard for ASIL-rated automotive systems; central to the safety case and supplier audits. Source: iso.org
NIST AI Risk Management Framework Referenced in procurement
What it requires
Traceability, documentation, and accountability across the AI lifecycle to make AI systems trustworthy and auditable.
HOW lakeFS HELPS
Timeline & exposure: Voluntary framework, increasingly referenced in procurement and enterprise AI governance. Source: nist.gov
Get the regulation-by-regulation breakdown in the whitepaper: Governance and Compliance Built Into Your AI Data Infrastructure
FAQs
What is AI data governance?
AI data governance means controlling and evidencing what data AI systems and agents access, modify, and train on – across structured, semi-structured, and unstructured data. lakeFS builds it into the infrastructure as three capabilities: controlled access, preventive data controls, and compliance evidence.
What does the EU AI Act require for training data?
Articles 10 and 12 require data governance for high-risk AI systems, including provenance and traceability between datasets and model versions, and automatic event logging. Transparency obligations apply from August 2, 2026, with high-risk obligations following in 2027 and 2028.
How does lakeFS support compliance audits?
How does lakeFS govern AI agents?
Does lakeFS govern unstructured data?
Yes. lakeFS manages structured, semi-structured, and unstructured data – including images, video, audio, documents, and the metadata that carries the context – within a single version-controlled environment, so audit trails, access policy, and reversibility apply to every data type
Does lakeFS replace my existing data infrastructure?
Govern your AI data without slowing teams down
