Solutions Hub: AI Governance

Govern every model, every agent, and the data behind them

BigID finds the AI running across your enterprise, sanctioned and shadow, maps the data each system trains on, retrieves and reaches, and turns your AI policy into controls enforced at the data layer, in the cloud or entirely inside your own boundary. The evidence builds as you go, ready for the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, and the regulation that comes next.

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What it finds

Models, agents, and the data they touch

Sanctioned and shadow AI across cloud AI runtimes, SaaS, code, and vendor tools

What it proves

Which model trained on what data

Lineage, risk assessments, and audit trails, mapped to the frameworks you report against

Where it runs

Anywhere, including air-gapped

One platform from multi-tenant SaaS to fully disconnected, with every capability in every mode

Why governance starts with the data

AI governance holds when it runs on the data underneath

Enterprises are deploying models, copilots and agents faster than any committee can review them, and for most companies data risk is the largest part of AI risk. AI runs on files, email, chat, code and vector stores as much as on databases. BigID governs the models and that data together, so policy becomes a control and every control leaves evidence behind. The program builds on data discovery and classification, access governance, and privacy and compliance.

One inventory for models and the data feeding them

Every model, agent, pipeline and data source in one place, with the owner, the purpose, the data it trains on or retrieves, and who can reach it. Governance teams start from the real estate, with no spreadsheet to reconcile.

Policy that runs as a control

Acceptable use, access and accountability rules are enforced at the data and model layer, then recorded in the GRC tool you already report from. The policy document and the running system say the same thing.

Governance that keeps pace with drift

Models change, data changes, and regulations change. Continuous discovery and assessment keep the inventory, the risk scores and the evidence current, so the next audit reads from today's estate.

See it operate

Watch it work

BigID's Agentic Data-Driven Risk Assessments for AI and Privacy Compliance

AI and privacy risk assessments built from the data an AI system actually touches, with an agent carrying the assessment through, for AI systems, privacy programs and regulatory compliance.

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A governance program that proves itself as it runs

AI governance works as a loop: know what you are running, set the rules, apply them where the data lives, and watch for drift. BigID runs every stage on the same data intelligence, which is why each pass leaves behind the inventory, assessments, lineage and audit trail a regulator asks for.

AssessKnow what you are running. A complete picture of every model, agent and pipeline, and every data source feeding them, before a single policy is written.

DefineSet the rules early. Acceptable use, access controls, accountability and behavioral standards across the whole AI program, mapped to the frameworks you answer to.

EnforceApply them at the data and model layer. Guardrails on prompts, least privilege for agents, and clean, compliant data before training, with violations tasked for remediation.

MonitorKeep pace with drift. Continuous discovery and assessment as models, data and regulations change, so governance posture stays current between audits.

Sovereign AI

Govern data, models and AI workflows entirely inside your own boundary

Sovereign AI keeps the data, the models, and the systems that govern them inside a boundary you define. BigID is built once and deployed anywhere, so discovery, classification, governance and agentic workflows run the same way in a sealed, air-gapped environment as they do in the cloud, with zero outbound connectivity and no phone-home telemetry.

Local control planeEverything stays inside. Configuration, scan orchestration, findings, dashboards, APIs, AI prompts and audit logs remain within your environment.

Bring your own modelYour approved models power BigID's AI. Classification and data intelligence run without sending sensitive data or metadata to a third-party model.

Built-in product securityKeys and credentials stay yours. Customer-controlled encryption and key management, credentials in your own vault, and least-privilege scanning.

Automation beyond accessAgentic workflows included. APIs, reporting, policy orchestration and MCP-based workflows for agents and copilots work the same in self-managed and air-gapped deployments.

Deploy as Multi-tenant SaaS Single-tenant cloud Bring your own cloud Private cloud Hybrid On-prem Fully air-gapped

Start here

Where AI governance programs start

What AI is actually running across the enterprise? BigID discovers models, agents, copilots and AI applications across cloud AI runtimes, SaaS, on-prem and dev, by connector type: LLMs, agents, context artifacts, AI libraries in code, access tokens and MCP servers. Each one is linked to an asset in the inventory, with its owner and purpose. Inventory and shadow AI → What data is each model trained on, retrieving, or exposed to? BigID classifies the data behind every AI system, structured and unstructured, including vector stores, and records which models trained on what data, with lineage from source to output. Data for AI → Is this AI system cleared: assessed, recorded, and free of open risk? BigID confirms the AI assessment is complete, the RoPA is complete, and no risk is left open, with assessments that capture the model, the data and the usage together and rank initiatives by legal risk. Privacy AI assessments → How do we show compliance with the EU AI Act, NIST AI RMF and ISO/IEC 42001? Check each AI system and each agent against the regulations, frameworks and internal policies that apply, build your own controls alongside them, and generate evidence for inventories, risk reviews, access, monitoring and incidents. Compliance and evidence → How do we turn responsible AI principles into something enforceable? Tie each principle to a control: keep toxic, regulated and unnecessary data out of training, limit what sensitive data outputs can surface, apply zero trust to model access, and keep an audit trail of AI activity. Responsible AI → How do we keep employees' use of Copilot and ChatGPT inside policy? BigID monitors what gets shared with copilots and assistants, flags sensitive data at the prompt, alerts on out-of-policy conversations, and preserves each employee's access rights when a prompt recalls sensitive data. Employee AI use → Who governs what our AI agents can reach and do? Agents are discovered as identities, mapped to the data they access, the tools they invoke and the owner accountable, held to least privilege, and monitored in real time with evidence-ready audit trails. Agent governance → Can we govern AI without our data, prompts or findings leaving our environment? Yes. The same BigID runs on-prem, in private cloud and fully air-gapped, with the control plane, audit logs and AI prompts kept inside your boundary and your own approved models powering BigID's AI. Sovereign AI →

Capabilities

From the first inventory to the audit, on one platform

Grouped in the order a governance program usually adopts them. Find the AI, understand the data it runs on, assess the risk, prove compliance, and hold it to responsible AI principles, then extend the same controls to the employees using AI every day and the agents acting on their behalf.

AI asset discovery and shadow AI

Governance starts with a complete list. BigID finds the AI in use across the enterprise, including the copilots, libraries, vendor models and MCP servers nobody registered, and catalogs each one with the context a reviewer needs.

Find every AI data source by connector type: LLMs, agents, context artifacts, and more
Discover AI models, agents, copilots and applications across cloud, SaaS, on-prem and dev environments
Look across cloud AI runtimes for models running outside approved workflows
Find AI and LLM libraries in code and folders, and AI access tokens inside applications
Discover MCP servers and poll their tools to see which data they expose
Identify vendors and third parties that use unsanctioned AI
Find unauthorized users of models, and unauthorized model use of sensitive data
Asset link: confirm every AI source is linked to an asset in the inventory
Catalog models, agents, applications, datasets, pipelines, vendors, owners and business uses in one registry
Record each system's purpose, owner, datasets, controls, risk assessments, lineage, policies, approvals and incidents

Govern the data AI trains on and retrieves

Most AI risk is data risk. BigID classifies what feeds every model, records which model learned from which data, and keeps training sets and RAG sources clean and compliant before they reach a model.

Classify structured and unstructured data, including code, chat and vector databases, by sensitivity and risk
Categorize data by business taxonomy and semantic context, in any language
Record which models trained on what data, and detect sensitive data in training sets
Map data flows and access lineage within AI initiatives, from source data to AI output
Label vectors in vector stores by sensitivity or line of business, and apply access controls to RAG applications
Curate training and RAG datasets with natural language search across the whole estate
Redact or tokenize problem attributes before training, natively or through third-party services
Run staged datasets through privacy and AI framework compliance checks
Apply retention to AI data, including disposition for expired consent or opt-out of automated decision making
Automate data steward work with an AI copilot: labeling, domains, business descriptions and inferred ownership

Privacy AI assessments and risk

Assessments start from what an AI system actually touches: the sensitivity of its data, who can reach it, and how it is used. Questionnaires add what only a person can answer, and an AI system is cleared once its assessment and RoPA are complete and no risk is left open.

Confirm the AI assessment is complete, the RoPA is complete, and there is no open risk
Automate AI risk assessment across in-house and vendor AI
Capture risk from the model, the data and the usage together
Augment data-driven findings with questionnaires
Prioritize AI initiatives by legal risk
Measure security risk by the sensitivity of data shared with AI, with internal and external AI kept separate
Assess identity risk: which users, groups and agents can access each AI system, and whether that access is right-sized
Unified risk posture across data risk, AI model risk, access risk and agentic risk
Vulnerability insights in AI SPM, scanning code and conversations
RoPA grounded in discovered data, with AI-assisted data flow maps that stay current as systems change
Auto-fill assessments from existing evidence such as SOC 2 and ISO reports, and from related PIAs and RoPAs
Route findings and approvals to accountable owners, and track each one to closure with an audit trail

AI regulatory compliance and agent policy alignment

Check every AI system and every agent against the regulations, frameworks and internal policies you answer to, add your own controls, and track all of it from one dashboard. The evidence comes from the running program, so it is current the day an auditor asks.

Assess risk relative to the EU AI Act
Assess alignment with the NIST AI Risk Management Framework
Native alignment to ISO/IEC 42001, alongside GDPR, CCPA and other data regulations
Align agent access to policy, regulatory obligations, and internal controls
Build and track custom compliance controls
Configurable compliance executive dashboards
Identify policy violations, then task and track remediation from the dashboard
Generate evidence for AI inventories, risk reviews, access controls, data governance, monitoring and incidents
A compliance and audit log of which data was used to train which AI
Share findings and evidence with GRC, catalog and ITSM platforms through APIs, MCP, metadata exchange and embeddable libraries

Responsible AI, enforced at the data level

Responsible AI principles hold when they are tied to controls. BigID operationalizes them through data security, compliance and audit: what a model can learn from, what it can reach, what it can reveal, and a record of each.

Identify regulated, personal, toxic, stale, duplicate or unnecessary data before it trains or tunes a model
Limit exposure of personal, sensitive or regulated data through AI outputs
Apply zero trust to AI: monitor and control what data models can, and should, access
Build accountability with audit trails for AI activity
Transparency into model lineage and training data, so every AI system can show what it was built on
Align AI programs with legal, ethical and regulatory standards and your acceptable use policy
Surface policy violations, shadow AI, risky data movement, prompt risk and compliance gaps in one view
Revoke access, quarantine data, enforce policy, minimize exposure, and prove control

Govern how employees use AI

Employees get the copilots and assistants they want, inside rules the business can defend. BigID watches what is shared, flags sensitive data at the prompt, and keeps each person's access rights intact when AI recalls data on their behalf.

Monitor data shared with Microsoft Copilot, ChatGPT, Gemini and agents, with an audit log of what was shared
Detect sensitive data submitted to AI tools and LLM interfaces, and alert on it
Bi-directional scanning of prompts and responses for sensitive terms and combinations of attributes
Define conversational guardrail policies, and alert administrators to out-of-policy conversations
Preserve employee access privileges when a prompt recalls sensitive data
Label sensitive data with labels copilots understand
See which employees can use which copilots and models, correct the permissions, and revoke where needed
Track data activity triggered by AI tools in real time, surfacing anomalous access and unauthorized movement

Govern AI agents as identities

Agents act with real permissions on real data. BigID treats each one as an identity with an owner, holds it to least privilege based on the sensitivity of what it can reach, and keeps a traceable record of everything it touches and produces.

Discover AI agents across SaaS, cloud, on-prem and AI platforms, including non-human and ephemeral identities
Map agents to the data they access, the tools they invoke, and the owners accountable for them
Correlate agent identity with the underlying service accounts and credentials
Enforce least privilege for agents based on data sensitivity and business context
Continuously re-evaluate entitlements as an agent's scope, data or risk changes
Monitor agent interactions with sensitive data in real time, detecting unusual queries, bulk extraction and policy drift
Govern how agents use data and call each other
Alert on violations aligned to AI TRiSM, privacy and security policies
Track what agents ingest, transform, generate and expose, with lineage from source to output
Automate remediation: revoke access, quarantine data, or trigger an approval
Run AI governance enforcement as an agent, from BigID or from Claude, Copilot, GPT and Gemini

Coverage

Every kind of AI, and the data it reaches

Discovery and governance run agentless across cloud, SaaS, on-prem, AI and dev environments, sorted by connector type, so the inventory covers what the business built, bought and adopted on its own.

Models and AI runtimes

In-house and cloud-hosted models, sanctioned or running outside approved workflows

AI agents

Agents as non-human and ephemeral identities, with the service accounts behind them

Copilots and assistants

Microsoft Copilot, ChatGPT, Gemini, and the enterprise assistants employees use

MCP servers and context artifacts

The servers and context agents connect through, and the data each one exposes

Training and fine-tuning data

Structured and unstructured sources, staged datasets, and what each model learned from

RAG and vector stores

Vectors labeled by sensitivity, with access controls carried into retrieval

Code and dev environments

AI and LLM libraries in repositories and folders, and AI access tokens in applications

Vendor and third-party AI

Suppliers using AI on your data, assessed alongside what you run yourself

Frameworks and regulations EU AI Act NIST AI RMF ISO/IEC 42001 GDPR CCPA / CPRA AI TRiSM Your own controls

Proof

Recognized for AI governance, trusted with the data underneath it

Analyst recognition

Frost & Sullivan 2025 Company of the Year for AI Governance
A Leader in the QKS SPARK Matrix™: AI Governance
Named in Gartner's 2025 AI TRiSM Market Guide
Represented 30+ times in Gartner's 2025 Hype Cycles, including Security, Privacy and AI SPM
A Leader in The Forrester Wave™: Sensitive Data Discovery And Classification Solutions, Q2 2026
Ranked #1 in data classification by Intuit, across 20 vendors

See the Forrester scoring →

From the field

“BigID gives me better visibility into sensitive data, helps prioritize security protections, reduces attack surfaces, strengthens compliance, and increases operational efficiency overall, a strategic pillar of our AI-First Cybersecurity Transformation.”

CISO, Global Healthcare Company

“We needed to automate processes and data management across systems for the data of a few million customers across a lot of systems and data. BigID was the one solution that did this in the most efficient and sophisticated way, and had more use cases we could add on moving forward.”

Chief Privacy Officer, Global Telecoms Company

FAQ

What governance, privacy and security leaders ask first

We already have an AI policy and a GRC tool. What does BigID add?

The layer that makes the policy true. Your GRC tool records the controls; BigID applies them where the data and the models are: guardrails on prompts, least privilege for agents, compliance checks before training. It then feeds the evidence back to the GRC record, so the system of record and the running estate agree.

Does this cover AI we did not build, like copilots and vendor models?

Yes, and that is usually where the inventory grows fastest. BigID discovers copilots, assistants, vendor AI, AI libraries in code and MCP servers alongside in-house models, then assesses each against the same framework, with the data it can reach and the people and agents who use it.

How does the program keep up as AI regulation changes?

Frameworks are mapped, and your own controls sit beside them. BigID aligns to the EU AI Act, the NIST AI RMF and ISO/IEC 42001 alongside GDPR and CCPA, lets you build and track custom controls, and reassesses continuously, so a new requirement becomes a new control on an inventory you already have.

What is AI governance?

AI governance is the set of policies and controls that keep AI use safe, lawful, and accountable. In practice it means knowing which models, agents, and copilots are running, what data each one trains on or reaches, and who is responsible for it, then enforcing policy and producing evidence. BigID runs that loop on the data underneath, starting from data discovery and classification and extending to access governance for employees and AI agents.

How does BigID find shadow AI?

It discovers AI by connector type across the estate, not by asking teams to register it. BigID looks across cloud AI runtimes, SaaS, on-prem, and dev environments for models, agents, copilots, AI libraries in code, access tokens, and MCP servers, links each to an asset in the inventory with its owner and purpose, and shows the data it can reach. AI assistant readiness covers readiness for copilots and assistants in particular.

How does BigID govern AI agents?

It treats each agent as an identity with an owner and holds it to least privilege. BigID discovers agents, maps them to the data they access and the tools they invoke, enforces access by data sensitivity and business context, monitors their interactions in real time, and automates remediation such as revoking access. The same controls work inside your own boundary through sovereign AI deployment, and AI and agentic security posture management extends them to the posture of the data itself.

Resources

The detail behind the program, and the regulation driving it

Executive brief / start here

Responsible AI: An Executive Brief

Turning responsible AI principles into controls on data, access, and AI activity, so the policy and the running systems say the same thing.

Read the brief →

Solution brief BigID for AI Security and Governance How BigID finds the AI running across your enterprise, governs the data behind it, and enforces policy at the data layer. Read the brief → Guide The 2026 AI Regulatory Landscape Guide The AI regulations and frameworks in play this year, and what each asks of the data and systems behind your AI. Read the guide → Research report The Ungoverned Enterprise: AI Risk, Agentic Access, and the Data Security Gap in 2026 Where agentic access has outrun the controls meant to govern it, and which gaps close first. Read the report →

Bring one AI system you need to govern. We will map it live.

A copilot rollout, a RAG pipeline, a vendor model under review. We will show you what it is, the data behind it, who and what can reach it, and the evidence it produces, on your own estate.

See AI governance on your own estate Get a free risk assessment