Solution hub: discovery and classification for data and AI agents

Discovery and classification for your data, and for the AI agents reaching it

The most comprehensive enterprise solution for finding, classifying, and categorizing sensitive data: hundreds of connectors across the data center, cloud, SaaS, and code, agentless from day one, with scan depth you set per source rather than one full scan setting. Two things get discovered here, not one: the data, read and classified into 2,000+ categories, and the models and agents reaching that data, sanctioned or not, found as assets in their own right. Every control downstream of this, and every tool you feed, inherits whatever both of them get right or wrong.

Get a free data risk assessment ▶Watch it work

Forrester Wave, Q2 2026

A Leader

Highest possible score in 11 criteria for sensitive data discovery and classification

Intuit classification challenge

Ranked #1

96.5% weighted precision and recall on a 40,000 record dataset, against 19 other vendors

What gets discovered

Data and AI agents

Sensitive data across hundreds of sources, and the models and agents reaching it, in one inventory

Where the category is now

Access decisions, DLP policies, and AI guardrails all run on the label

Discovery and classification used to be the thing you finished before the security work started. Now access decisions, DLP policies, retention rules, and every AI guardrail run on those labels directly, which means the accuracy, depth, and freshness of classification set the ceiling on everything downstream. Three things decide whether that ceiling is high enough.

Depth you choose

Metadata, sample, or full scan, set per source and per policy, so scan cost stops deciding how often the estate gets looked at.

Accuracy you can audit

Findings reviewed, refined, and attested inside the product, by the data owners who can tell a false positive from a real one.

Two things to find, not one

The data, and the AI reaching it. An agent is discovered as an asset, with the identity it runs under and the data in its reach; the data itself is read and classified.

See what BigID can do

Watch it work

BigID platform overview

Connecting a source, seeing what the classification found in it, and acting on that finding in the same place it was produced.

Overview

In this hub

Start here

Five questions, and where BigID answers each one

Do we have to scan everything to learn anything? Metadata only pre-scanning maps the estate before any content is read, smart sampling gives a first assessment in hours, and full scans go where the sampling says they are worth running. Discovery in depth → Can we trust what the classifier tells us? 2,000+ pretrained categories, LLM categorization, agentic supervision that checks its own work, in-product review and refine, and delegated validation by the people who own the data. Data identification AI → Will it find our AI agents as well as our data sources? Hundreds of connectors across data center, cloud, SaaS, and code, agentless from day one, plus discovery of the models and agents themselves, sanctioned and unsanctioned, and the data each one can reach. Coverage and connectivity → What happens when the scan hits a petabyte, or a platform that throttles? Category first lateral scale out, ephemeral scanners that spin up and down with the workload, snapshot based offline scanning, and cloud optimization built to work around throttling on platforms like M365. Scan scale and speed → Who runs this once the project team moves on? Schedules, blackout windows, exclusions, change based scans, and event triggers, all under full API and MCP control, including scan control and reporting from inside Claude, Copilot, Gemini, and GPT. Operationalization and reporting →

One scan model, four depths, set per source

Most scanning decisions are really budget decisions: a full content scan across everything is the only setting, so it runs quarterly at best and the inventory is stale the week after. BigID separates depth from coverage, so the estate can be mapped continuously and the expensive detail goes where the sampling says it belongs.

A full scan is the only setting, so the estate gets looked at once a quarter and every finding is answered with "that was true in March".

A SaaS platform throttles the scanner, and a job scoped in hours stretches across days while the scan window closes behind it.

The classifier is right most of the time, and nobody can say which findings are in the other part, so results get argued over instead of acted on.

Two kinds of asset go in, and coverage and depth are separate settings for both. BigID connects to hundreds of data sources with no agents to deploy, and finds the models and agents reaching them, then decides per source how far to look: metadata alone to map the estate, sampling for a fast read on what a source holds, configurable sampling where more detail is worth the time, and uncapped full scans on structured and unstructured data where it matters. A usable assessment lands two rows in, which is what lets the inventory stay current instead of being rebuilt once a quarter.

Capabilities

How deep the scan goes, how accurate it is, and what it costs to stay current

Six groups, in the order the work runs: decide how deep to look, identify what is there, reach every source it lives in, hold that up at petabyte scale, run it without a project team, and report on it where the rest of the business already works.

Discovery in depth

The first DSPM, DLP, DAG, or DAM to treat scan depth as a setting rather than a fixed tradeoff. Map the estate from metadata alone, sample for a fast read, then spend full scan time where the sampling says it is worth spending.

Metadata only pre-scanning, unique in the category, so the estate can be mapped before any content is read
Smart sampling for a fast assessment of what a source actually holds
Customizable sampling, so scan detail is configured per source and per policy
High speed full scans across structured and unstructured data, with no volume or frequency caps

Data identification AI

Accuracy is the dependency nobody audits until an access decision has already been made on it. This group is about getting the label right, proving it, and giving the people who own the data a way to correct it.

2,000+ pretrained and tuned categories of sensitive data
LLM based categorization and taxonomy detection
Prompt based LLM classification: define what you are looking for in natural language
Logic for finding combinations of data attributes across structured and unstructured data
Category first review and refine, natively in product, for the people who own the taxonomy
Custom model training inside the product
Bring your own AI, so classification runs on a model you have already approved
Agentic supervision, so classification checks its own work
Ranked #1 for accuracy in Intuit's 20 vendor classification challenge, at 96.5% weighted precision and recall
Delegated workflow for attestation and validation of findings, a first among DSPM, DLP, and DAM

Coverage across data and AI

Broader coverage than any other discovery and classification solution, none of it agent based. Two kinds of asset get found here: the data stores, and the models and agents reaching them. Forrester scored BigID 5 out of 5 on both cloud and on-premises data source coverage.

Hundreds of connectors spanning the data center, cloud, SaaS, and code
100% agentless from day one, with nothing to deploy or maintain on the sources themselves
One click automatic data source provisioning
One platform for structured, semistructured, and unstructured data
Data at rest and data in motion, under the same classification
No pre knowledge of schema required
Hundreds of file formats, including the specialized CAD formats industries like semiconductor run on
Discover the models and agents themselves, sanctioned and unsanctioned, and keep monitoring them as they change
See what data each model or agent can reach, through the identity it runs under
Classify the data inside the AI plumbing too: RAG pipelines, vector stores, and prompts, against the same categories
Simple setup for cloud and hybrid cloud, so a POC converts into production in days

Scan scale and speed

Petabyte workloads and throttled SaaS platforms break scanning in different ways, and both of them show up as a scan window that will not close. Scale out, snapshots, and just in time rescans are what keep the window open.

Petabyte scale, with category first lateral scale out of scanners for the largest workloads
Full speed and performance telemetry while a scan runs, not after it fails
Intelligent cloud and SaaS optimization that works around platform throttling, including M365
Advanced snapshot support for secure offline scanning
Ephemeral cloud scanners that spin up and spin down with the workload
Just in time scanning of new and modified data, so one change does not trigger a full rescan
Deployable as an ultra fast embeddable SDK inside custom applications and data pipelines

Scan operationalization

Discovery stops being a project the moment scans run on a schedule nobody has to defend. Frequency, depth, blackout windows, and triggers are all configuration, and all of it is reachable by API, by MCP, and from the assistants your teams already work in.

Configure frequency, detail depth, blackout windows, exclusions, change based scans, and event triggers
Full agentic supervision and reporting across the scan estate itself
100% API and MCP coverage for advanced control
Native scan control and reporting from inside Claude, Copilot, Gemini, and GPT

Reporting and audit

The inventory is only useful where the decisions get made, which is rarely inside the security console. Reporting runs to the warehouse, the BI tool, and the assistant, on the same data the scan produced.

Customizable in-product dashboards
Bespoke AskBigID reports from inside Claude, Copilot, and GPT
ETL into S3, BigQuery, Snowflake, and Databricks
Customer reporting through Power BI, Looker, and Tableau

Where it runs

Hundreds of connectors, hundreds of file formats, and the AI reaching all of it

The same classification runs against every data family below, so a finding in a warehouse table and a finding in a file share describe sensitivity the same way. The last two families are different in kind: the AI assets reaching that data, found and inventoried alongside it. Nothing is installed on the sources themselves.

Cloud and SaaS

Microsoft 365, Google Workspace, Box, and Dropbox, with cloud optimization built for platforms that throttle.

Object storage and lakes

Amazon S3, Azure Blob, and Google Cloud Storage, down to the bucket and prefix.

Warehouses and databases

Snowflake, Databricks, and BigQuery, plus relational and NoSQL on prem and managed, with no pre knowledge of schema required.

File shares and data center

SMB, NFS, CIFS, and NetApp, including mainframe environments and the legacy shares nobody owns.

AI models and agents

Sanctioned and shadow alike, discovered as assets with the data in their reach, plus the RAG pipelines, vector stores, and prompts behind them.

Pipelines, code, and custom apps

Data in motion, code repositories, and the embeddable SDK for classifying inside your own applications.

Third party validation

A Leader in The Forrester Wave for sensitive data discovery and classification

The Forrester Wave™, Q2 2026

“BigID is engineered for performance and petabyte scale.”

The Forrester Wave™: Sensitive Data Discovery And Classification Solutions, Q2 2026

Named a Leader, one of three among the ten vendors evaluated
Cited for strengths in discovery across cloud and on-premises sources, mainframe environments included, and for a blend of classification techniques, enrichment, and tuning that covers use cases from compliance and information governance through to AI security and governance

Highest possible score, current offering

5/5Cloud data source coverage
5/5On-premises data source coverage
5/5Enrichment for classification
5/5Language support
5/5Tuning to improve accuracy
5/5Integrations
5/5Secure-by-design commitments

Highest possible score, strategy

5/5Innovation
5/5Roadmap
5/5Partner ecosystem
5/5Adoption

Read the Forrester Wave report →

The Intuit classification challenge

Intuit put 20 vendors from the US and Israel through a head to head classification challenge
The test set was a synthetic dataset of more than 40,000 records, half for training and half for evaluation
Scoring was precision and recall across data types, plus a global cross-label assessment, run by Intuit's own security team
BigID won it, at 96.5% weighted precision and recall

“BigID's technology demonstrated impressive capabilities in accurately classifying data at scale.”

Gleb Keselman, Director of Security Software Engineering, Intuit

Read how the challenge was run →

And 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 how Fiserv runs it as one control plane →

Before you scope it

What teams ask before they start

Do we have to full scan a petabyte to get started?

No, and the deployment model assumes you will not. Metadata only pre-scanning maps the estate without reading content, smart sampling gives you a first assessment of what each source holds, and full scans get pointed at what the sampling flagged. Depth is set per source, so the inventory can stay current instead of being rebuilt once a quarter.

How do we know the classification is right?

Two outside tests and one internal workflow. Forrester scored BigID 5 out of 5 on tuning to improve accuracy and on enrichment for classification. Intuit ran 20 vendors against a 40,000 record challenge and BigID won it at 96.5% weighted precision and recall. Inside the product, agentic supervision has classification check its own work, and findings go to the data owners who can tell a false positive from a real one.

Does this find our AI agents, or only the data they touch?

Both, and they are two different jobs. The models and agents are discovered as assets, sanctioned and unsanctioned, with the identity each one runs under and the data in its reach, then monitored as they change. The data is read and classified, inside RAG pipelines, vector stores, and prompts as well as in the stores behind them. One inventory holds both, which is what lets an AI guardrail and a data policy describe the same object.

Take it further

What to read when classification is the thing holding everything else up

Analyst report / start here

The Forrester Wave™: Sensitive Data Discovery And Classification Solutions, Q2 2026

Ten vendors scored against 25 criteria, with BigID named a Leader and given the highest possible score in eleven of them. The evaluation criteria alone are a usable shortlist for your own.

Get the report →

Benchmark How BigID won Intuit's classification challenge 20 vendors, a 40,000 record dataset, and 96.5% weighted precision and recall. Read the write-up → Customer story Fiserv: one data intelligence and policy control plane How Fiserv runs data intelligence and policy control from a single place. Read the story → Free assessment Discover your data and AI risk An agentless, cloud native assessment that pinpoints where sensitive data and AI risk live, and how to fix them. Start it →

Point us at the source you have never been able to scan.

A scoped assessment on your real data tells you more than a demo on sample data, across the formats, the volumes, the platforms, and the AI agents that have been out of reach.

Book a live demo Run a free risk assessment