Bigeye AI Trust Platform
by Bigeye
Governance, observability and runtime enforcement for the data your AI agents are allowed to touch
The Bigeye AI Trust Platform extends Bigeye's data observability heritage into AI governance, combining lineage, sensitivity classification and runtime policy enforcement over the data that AI agents consume. It is aimed at data and governance leaders at regulated enterprises who need to prove which agent accessed which dataset, whether that data was certified, and whether it was fit to answer on.
The Bigeye AI Trust Platform is the data-observability vendor Bigeye's move into AI governance, first announced in June 2025 and built around three pillars the company calls governance, observability and enforcement. It is designed to answer three questions before an AI agent acts on enterprise data: is the data reliable, is the agent authorised to access it, and is the dataset approved for this use. Observability contributes end-to-end column-level lineage across modern and legacy stacks, freshness and anomaly detection, and automated scanning that classifies sensitive or regulated fields. Governance layers on dataset certification, ownership assignment and a business glossary so there is an authoritative record of which data is blessed for which purpose. Enforcement is delivered by AI Guardian, announced on 10 December 2025 and released to enterprise customers in private preview, which sits as an access gateway between AI systems and data sources and evaluates each request against organisational policy. AI Guardian runs in three escalating modes — monitoring, which builds audit trails; advising, which injects context and guidance; and steering, which prevents access outright — letting teams start observationally and tighten as confidence grows. The platform also ships an Agent Trust Hub, an MCP server and developer tooling, plus bigAI for AI-driven data quality, and integrates with Snowflake Intelligence. Bigeye was founded in 2019 by Kyle Kirwan and Egor Gryaznov, who previously ran Uber's first data warehouse; Eleanor Treharne-Jones is chief executive. The company positions the EU AI Act's 2026 obligations as the primary regulatory driver.
The head of data governance or the chief data officer at a regulated enterprise rolling out AI agents against a warehouse, who must evidence agent-level data access to auditors.
A single control point that decides, per request, whether an agent may touch a dataset — with the lineage, sensitivity and certification context behind that decision recorded.
At a Glance
- Category
- Governance & Security
- Pricing
- Contact for pricing, Subscription
- Target Market
- CIOs, Chief Data Officers, Data Engineers, Data Governance Leads, Compliance Officers
- Deployment
- Cloud-first, API-based
- Founded
- 2019
- Headquarters
- San Francisco, United States
- Team Size
- 11-50
Key Features
- ✓AI Guardian runtime enforcement
Acts as a gateway between AI systems and data, evaluating every request against organisational policy before access is granted.
- ✓Three enforcement modes
Monitoring, advising and steering let teams start with audit trails and progressively tighten to outright access prevention.
- ✓End-to-end data lineage
Column-level lineage across modern and legacy stacks shows where data originated and how it moved before reaching an agent.
- ✓Sensitive data scanning and classification
Automatically identifies regulated or high-risk fields so policies can be written against sensitivity rather than table names.
- ✓Dataset certification and governance workflows
Certification, ownership assignment and a business glossary establish which datasets are approved for which purposes.
- ✓Data quality and anomaly monitoring
Tracks freshness and anomalies so agents are not silently reasoning over stale or broken pipeline output.
- ✓MCP server and developer tooling
Exposes the trust layer to agent frameworks through Model Context Protocol rather than requiring bespoke integration work.
Capabilities
Use Cases
- •Gating a warehouse-connected agent
An analytics agent is restricted to certified datasets, with requests for uncertified or sensitive tables blocked at runtime.
- •EU AI Act audit readiness
Monitoring mode produces the access trail regulators expect, showing which agent read which regulated data and when.
- •Preventing sensitive data exposure in RAG
Automated classification catches regulated fields before retrieval pipelines quietly embed them into a vector index.
- •Diagnosing a wrong agent answer
Lineage plus freshness monitoring shows whether the agent reasoned correctly over data that had already gone stale upstream.
- •Rolling out agents incrementally
Teams start in monitoring mode to learn real access patterns, then move to steering once policies reflect actual usage.
Ideal For
Best For
- ✓Preventing AI agents from reading sensitive or regulated fields they were never approved for
- ✓Producing audit trails of agent-level data access for EU AI Act and similar compliance regimes
- ✓Restricting agents to certified, curated datasets rather than the whole warehouse
- ✓Detecting when agents are answering from stale or anomalous data before decisions are made on it
- ✓Giving data teams end-to-end column-level lineage across both modern and legacy stacks
Not Ideal For
- ✗Organisations without an existing warehouse-centric data estate — the value depends on lineage and classification over governed tables
- ✗Teams wanting mature, generally available enforcement today; AI Guardian entered private preview in December 2025 rather than GA
- ✗Buyers who need transparent published pricing, since Bigeye does not publish a pricing page at all
- ✗Small data teams whose governance need is satisfied by native warehouse controls such as Snowflake or Databricks row-level policies
Integrations
Deployment
Market Analysis
Pros
- ✓Unifies data quality, lineage, sensitivity, governance and enforcement in one system instead of stitching four tools together
- ✓Strong enterprise reference base carried over from the data observability product, including USAA, Zoom, Hertz, Cisco and Freedom Mortgage
- ✓Graduated enforcement modes make it deployable without an all-or-nothing policy cutover
- ✓Well-timed against EU AI Act obligations taking effect in 2026, which existing catalogues were not designed to satisfy
Cons
- ✗AI Guardian was still private preview as of its December 2025 announcement, so production maturity is unproven and no independent benchmark exists
- ✗No published pricing at all — the pricing page 404s — which makes evaluation slow and comparison against Monte Carlo or Immuta difficult
- ✗Security and compliance certifications for the AI Trust Platform are not stated on the public pages reviewed, so SOC 2 and ISO status could not be confirmed
- ✗Publicly disclosed funding is the $45M Series B from September 2021; no newer round is public, which is a long gap for a company entering a competitive new category
- ✗Small vendor at 11-50 employees competing against Collibra, Immuta and the warehouse vendors' own native governance features
Pricing
Enterprise
Contact for pricing
- ✓AI Trust Platform
- ✓AI Guardian access requests via bigeye.com
- ✓Lineage, classification and governance workflows
Bigeye publishes no pricing page — the URL bigeye.com/pricing returns a 404 — and the AI Trust Platform is sold through enterprise sales only, with AI Guardian access granted by request rather than self-serve signup. There is no free tier or public trial for the AI governance capabilities, and no plan tiers are disclosed publicly, so total cost of ownership cannot be modelled without engaging the vendor directly.
Security & Compliance
Connect
Sources
This page was written from 5 sources, 3 on domains other than bigeye.com.
- 1.bigeye.com — ai trust platformvendor
- 2.bigeye.com — introducing the bigeye ai trust platformvendor
- 3.einpresswire.com — bigeye announces ai guardian to give enterprises control ove
- 4.aithority.com — bigeye introduces the first platform for governing ai data u
- 5.aithority.com — bigeye raises 45m series b to scale leading data observabili
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