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Solution 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
Start here

The questions an access review runs into, and where BigID answers each one

Who can reach our sensitive data, and at what granularity? Permission settings read from unstructured, warehouse, and data lake sources, down to the individual file, folder, bucket, and table. Permissions and privileges → Are our access reviews signing off on anything real? Scheduled reviews for compliance plus targeted attestation aimed at the privileges that conflict with policy, rather than a full recertification nobody reads. 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 blocking, and owner approval flows in ServiceNow, Jira, or natively in BigID. Remediation and revocation → Where did this data 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. Exposure investigation →
Where the category is now

Access governance outgrew the entitlement list

An entitlement list answers one question: who holds a grant. It cannot tell you whether the grant is still justified, whether anyone has used it in two years, or whether the identity holding it is a person at all. Data access governance, activity monitoring, and AI identity control have converged on the same three things, and they have to run against the same inventory to agree with each other.

Entitlements

Permissions read at the source and reviewed against policy, with the conflicts surfaced for attestation instead of exported to a spreadsheet.

Behavior

Real usage watched alongside the grant, so a violation is measured by what an identity did, not only by what it was permitted to do.

Non-human identities

Agents hold inherited and temporary privileges that no joiner, mover, leaver process was built for, and they act on them at machine speed.

Where BigID goes past the permission list

BigID reads permission settings 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 runs under an employee's entitlements and reaches stores nobody scoped for it, long after the project that granted them ended.

A quarterly review recertifies a grant nobody has used in two years, because the reviewer sees the permission and never sees the behavior.

A file leaves through a share link and the investigation opens with no way to say which store it came from or who else could reach it.

Who is asking Employees joiners, movers, leavers Contractors partners and third parties AI agents inherited, temporary grants Investigate trace exfiltrated data to its source, bound the impact One platform, one inventory BigID DAG and DAM Read permissions settings and privileges at the source Review and attest targeted at privileges that conflict with policy Monitor activity forensics and UEBA on real usage Revoke and remediate ServiceNow, Jira, native, AgentIQ agents beyond metadata What they reach Collaboration suites M365, Workspace, Box, Dropbox Object storage and lakes buckets and prefixes, including S3 Warehouses Snowflake, Databricks, tables File shares SMB, NFS, CIFS, NetApp
Reading permission settings is the first of four rows, not the whole product. BigID attests the privileges that conflict with policy, monitors what human and non-human identities actually do with the access they hold, revokes or blocks 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.
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
In this hub
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". Answering all three takes four groups of capability, in the order the work actually runs.

Fine grained permission and privilege management

Permission metadata tells you who holds a grant. It does not tell you whether the grant conflicts with policy, and a full recertification nobody reads does not tell you either. BigID reviews on a schedule and attests on the conflicts.

  • Read permission settings 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
  • 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

A grant nobody has touched in two years and a grant being used to pull ten thousand records at 2am look identical on an entitlement list. Monitoring actual usage is what separates them.

  • 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, blocking, and revocation

Finding the violation is the easy part. The work is taking the access away in the system that granted it, with the data owner's approval on record and without a quarter of ticket routing.

  • Automated or human in the middle revocation across diverse data sources and clouds
  • Block data sharing internally and externally
  • 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
  • Full review and remediation inside Teams and Slack coming soon

Data and access exposure investigation

After an incident the question is not what the policy said. It is which store this data came from, which identities could reach it, and how far the exposure actually 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 the legacy shares nobody owns.

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 →
Before you scope it

What teams ask before they start

Does this replace our IGA or IAM?

No. It governs a layer those tools 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 holds a grant to it.

How is this different from reading permissions out of SharePoint?

Permission metadata is the input, and it is one row of four. 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, not one platform at a time.

What about agents that inherit a person's access?

That is the case the model was extended for. 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.

Take it further

What to read when access reviews stop keeping up

Whitepaper / start here
The New Standard for Data Access Governance

Why permissions-centric governance produces entitlement reviews that are technically complete and operationally meaningless, and what DSPM, DAG, and DAM look like sharing one data model.

Read the whitepaper →
Executive brief Agentic risk is an access governance problem Why agent incidents come from credential chaining and excessive permissions rather than sophisticated attacks. 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 a grant 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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