Solutions Hub: data access governance and activity monitoring

Fine grained data access governance for employees and AI agents

BigID governs access at the data layer: which users, groups, service accounts, external collaborators, and AI agents can reach sensitive data, whether that access has ever been used, and whether it is appropriate given what the data actually contains. Access intelligence, entitlement reviews, activity monitoring, and automated remediation run in one platform, for human and non-human identities alike.

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Identity aware by design

The ID in BigID

Every access decision is grounded in what the data contains and which identity can reach it

Identities covered

Human and non-human

Employees, contractors, and AI agents under one policy model

Enforcement scope

Files to tables

Files, folders, buckets, tables, and databases across unstructured, warehouse, and lake sources

What access governance covers now

Access governance outgrew the entitlement list

An entitlement list shows who holds access. BigID adds whether that access is still justified, whether anyone has used it in two years, and whether the identity holding it is a person at all. Employees, contractors, and the AI agents acting for them now sit under one discipline, run against the same classified inventory, so an entitlement decision and an activity finding describe the same object.

Entitlements

BigID reads permissions metadata at the source and reviews it against policy, surfacing the conflicts for attestation instead of exporting them to a spreadsheet.

Behavior

BigID watches real usage alongside every permission, so a violation is measured by what an identity did, not only by what it was permitted to do.

AI agents, sanctioned or not

BigID discovers agents, sanctioned and unsanctioned, shows the data each one reaches through the identity it runs under, flags behavior that steps outside policy, and revokes that access when it does.

An agent never holds access of its own. It inherits what the identity behind it can reach, so an over-permissioned or unsanctioned agent is an access problem, and BigID closes it at that identity: find the agent, see the data in its reach, watch what it does, and revoke the access when its behavior steps outside policy.

See what BigID can do

Watch it work

BigID Fine-Grained Access Control Demo: Review Permissions & Reduce Data Exposure

Reviewing permissions at the file and folder level and taking the excess access off, which is where a data access governance program starts.

Demo

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Find open access to sensitive data, tell people from AI agents, and revoke what should not be there.

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What BigID adds to the permission list

BigID reads permissions metadata from every source in the coverage list, then attests the privileges that conflict with policy, watches what the identities holding them actually do, revokes when behavior and policy disagree, and traces the exposure when something has already left.

An agent's reach is visible through the identity it runs under, so access left over from a finished project comes off.

Reviewers see usage next to every permission, so access nobody has touched in two years is the first to go.

When a file leaves through a share link, BigID traces it to the store it came from and to who else could reach it.

BigID reads permissions metadata, attests the privileges that conflict with policy, monitors what human and non-human identities actually do with the access they hold, revokes access when behavior and policy disagree, and traces an exposure back to the store it came from. All of it runs against one inventory, which is what lets an entitlement decision and an activity finding describe the same object.

Start here

Where teams start with data access governance

Who can reach our sensitive data, and at what granularity? Permissions metadata read from unstructured, warehouse, and data lake sources, down to the individual file, folder, bucket, and table. Permissions and privileges → How do we run access reviews people can trust? Scheduled reviews for compliance plus targeted attestation aimed at the privileges that conflict with policy. Permissions and privileges → Is anyone behaving the way their permissions assume? Real usage monitored for humans and non-humans, with UEBA on unusual downloads, sharing, and cross border transfers, and violations defined against policy. Activity monitoring → Can we take access away without a ticket queue? Automated or human in the loop revocation across data sources and clouds, share link removal, and owner approval flows in ServiceNow, Jira, or natively in BigID. Remediation and revocation → If data leaves, where did it come from and how far did it get? AI powered data matching that traces exfiltrated data back to its source, and exposure analysis by account, identity, or agent. Breach investigation →

Capabilities

Who has access, whether they use it, and whether they should

The standard moved from "who has access to what" to "who has access to what sensitive data, has that access been used, and is it appropriate given what the data contains". BigID answers all three.

Fine grained permission and privilege management

Permissions metadata shows who has access. BigID reviews it on a schedule and attests the privileges that conflict with policy, so reviewers spend their time on the conflicts.

Read permissions metadata across diverse unstructured, warehouse, and data lake sources
Complete visibility into users, groups, non-human identities, service accounts, external collaborators, and AI models with access to sensitive data
Enforce fine grained access to files, folders, buckets, tables, and databases
Run regular permission reviews for compliance
Run regular and targeted attestation where privileges conflict with policy
Automatically identify overexposed sensitive data so permissions come off before an incident
Retire stale access by removing permissions that have gone unused
Report on policy compliance on a regular cadence
Delegated entitlement reviews: data owners keep or revoke file by file, with decisions routed through Jira or ServiceNow
Sophisticated workflows to review and refine privileges over time
Investigate violations to understand exposure impact
Handle the inherited and temporary permissions defined for AI, and measure intent

Access activity monitoring and unusual behavior

An entitlement list shows the same line for access nobody has touched in two years and access pulling ten thousand records at 2am. Monitoring actual usage tells them apart.

Monitor real data usage by users, service accounts, automated workflows, and AI agents
Activity Explorer: one unified audit log, filtered by identity, operation, resource, or date
Detect suspicious behavior: unusual downloads, data sharing, cross border transfers
Out of the box threat detection policies correlating activity with classification in near real time
Define and detect access policy violations based on actual behavior
Report on overall compliance with policy and with access driven regulations
Alerts on sensitive files shared to personal email or external domains, with accidental misuse separated from suspicious activity
Coverage across structured and unstructured data alike

Access remediation and revocation

BigID takes access away in the system that granted it, with the data owner's approval on record without waiting on a ticket queue.

Automated or human in the middle revocation across diverse data sources and clouds
Revoke internal and external sharing, including links and open access, at the source
Revoke permissions or trigger Cloud DLP actions from the Action Center, or through your IAM and SOAR tools
Revoke access dynamically when a policy violation is detected
Remediation flows with data owner approval in ServiceNow, Jira, or natively in BigID
Automate enforcement with AI agents inside BigID AgentIQ
MCP server and API integration, so internal and third party tools can orchestrate governance workflows

Breach investigation and data and access exposure analysis

After an incident, BigID answers which store the data came from, which identities could reach it, and how far the exposure runs.

Identify the origin of exfiltrated data down to the specific source, through AI powered data matching
Investigate access exposure by account, by identity, or by agent
Blast radius analysis: every file or dataset a compromised account reached in a defined window
Trace the source of an incident and measure its impact radius
Analysis that bounds the exposure rather than listing every object touched

Where it runs

Coverage across collaboration, cloud, and the warehouse

Permission reading, activity monitoring, and revocation run against the same source list, so an entitlement finding and a behavior finding point at the same object.

Microsoft 365

SharePoint, OneDrive, and Teams, including the shares a copilot indexes.

Google Workspace

Drive and shared drives, with link sharing treated as an access path.

Box and Dropbox

Folder and file level permissions, internal and external collaborators.

Data lakes

Object storage including S3, at the bucket and prefix level.

Data warehouses

Snowflake and Databricks, down to tables and columns.

File shares

SMB, NFS, CIFS, and NetApp, including legacy shares brought under review.

Third party validation

Named a Leader in data access governance and in all DSPM research

Analyst recognition

Named a Leader across four GigaOm evaluations, data access governance among them, alongside DSPM, data security platforms, and unstructured data management
Leader placements across all DSPM research: the Omdia DSPM Universe, CB Insights, TAG, GigaOm, and Frost & Sullivan
Named in Gartner's 2025 Market Guides for DLP and for AI TRiSM
2025 Company of the Year for AI Governance, Frost & Sullivan
Represented more than 30 times across Gartner's 2025 Hype Cycles for security, privacy, and AI SPM

In a customer's words

“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

Read the data access governance whitepaper →

FAQ

Answers on IGA, activity monitoring, and AI agents

Does this replace our IGA or IAM?

It works alongside them, on the data layer they do not reach. IGA manages application entitlements and roles. BigID manages the permissions on the data itself: the file, the folder, the bucket, the table. It also feeds sensitivity and ownership context into the identity process you already run, so a reviewer sees what the data is and not only who has access to it.

What does BigID do with permissions metadata once it has read it?

Permissions metadata is the input. BigID attests the privileges that conflict with policy, monitors what the identity holding them actually does, revokes dynamically when behavior breaks policy, and traces an exposure back to the store it came from. The same loop runs across every source in the coverage list.

What about agents that inherit a person's access?

BigID extends access governance to exactly this case. BigID controls access for employees and contractors and for the AI agents acting on their behalf, handles the inherited and temporary permissions defined for AI, measures intent against what an agent actually reaches, and can revoke automatically, including with AgentIQ agents doing the enforcement.

What is data access governance?

It governs access at the data layer: who can reach which data, whether they use it, and whether that access is appropriate. Data access governance reads permissions metadata on files, folders, buckets, and tables, reviews and attests it against policy, monitors real usage, and revokes excess access, for employees, service accounts, and AI agents. BigID runs it on the same classified inventory as DSPM, so every permission is judged by the sensitivity of the data it reaches.

How does data access governance relate to DSPM and DLP?

DSPM finds and classifies sensitive data, data access governance controls who can reach it, and DLP acts on what leaves. BigID runs the three on one data inventory, so entitlement reviews, activity monitoring, and policy decisions describe the same objects. The same findings feed privacy and compliance and AI governance programs.

Resources

Go deeper on data access governance

Whitepaper / start here

The New Standard for Data Access Governance

What data access governance looks like when DSPM, DAG, and DAM share one data model, and why that makes entitlement reviews meaningful.

Read the whitepaper →

Executive brief Agentic risk is an access governance problem How credential chaining and excessive permissions drive agent incidents, and how access governance closes them. Read the brief → Whitepaper Governing non-human identities in a world of autonomous AI DAG and DAM for the agentic era: what changes when the identity holding the access is not a person. Read the whitepaper → Solution brief BigID Data Activity Monitoring Activity Explorer, threat detection policies, blast radius analysis, and automated remediation, source by source. Open the brief →

Point us at a share nobody has reviewed in years.

A scoped assessment on your real permissions and your real activity tells you more than a demo on sample data, across your files, your tables, and the agents reaching both.

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