Find, classify, and categorize sensitive data with hundreds of connectors across the data center, cloud, SaaS, and code, agentless from day one, with scan depth you set per source. Two kinds of asset get found here: the data, read and classified into 2,000+ categories, and the models and agents reaching it, sanctioned or not, inventoried as assets in their own right. Every control downstream, and every tool you feed, runs on what this finds.
Data security, privacy, governance, and AI security all start from the same inventory: what data you have, where it lives, and how sensitive it is. BigID discovers sensitive data across cloud and on-premises sources, classifies it with a blend of techniques and enrichment, and keeps that inventory accurate and current at petabyte scale, so access decisions, DLP policies, retention rules, privacy programs, and AI guardrails are built on findings you can trust.
Metadata, sample, or full scan, set per source and per policy, so scan cost stops deciding how often the estate gets looked at.
Findings reviewed, refined, and attested inside the product, by the data owners who can tell a false positive from a real one.
Two kinds of asset, one inventory. 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.
Connecting a source, seeing what the classification found in it, and acting on that finding in the same place it was produced.
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See a metadata scan, a smart sample, and classifier-level findings come together in the platform.
BigID separates depth from coverage, so the estate can be mapped continuously and the expensive detail goes where the sampling says it belongs.
Metadata, sampling, and full scans are set per source, so the estate is looked at continuously and every finding reflects this week.
Cloud optimization works around platform throttling, so a job scoped in hours finishes in hours.
Findings go to the data owners who can tell a false positive from a real one, so results get acted on.
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.
Map everything you own from metadata alone, get a fast read on what each source holds, then run full scans where the findings justify them.
Accurate classification is what every downstream control depends on. 2,000+ pretrained categories, LLM and prompt based classification, and custom models identify the data, accuracy is proven in independent testing, and the people who own the data review and correct what it finds.
Hundreds of connectors, 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, and Gartner’s 2026 Magic Quadrant for Data and Analytics Governance Platforms credited the breadth of out-of-the-box connectors, especially for semistructured and unstructured sources.
Scale out, snapshots, and just in time rescans keep petabyte workloads and throttled SaaS platforms inside the scan window.
Discovery becomes an operating process once scans run on a schedule. 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.
Reporting reaches the places decisions get made: the warehouse, the BI tool, and the assistant, all on the same data the scan produced.
The same classification runs against every type of source below, so a finding in a warehouse table and a finding in a file share describe sensitivity the same way. AI models and agents are different in kind: they are found and inventoried as assets alongside the data they reach. Nothing is installed on the sources themselves.
Microsoft 365, Google Workspace, Box, and Dropbox, with cloud optimization built for platforms that throttle.
Amazon S3, Azure Blob, and Google Cloud Storage, down to the bucket and prefix.
Snowflake, Databricks, and BigQuery, plus relational and NoSQL on prem and managed, with no pre knowledge of schema required.
SMB, NFS, CIFS, and NetApp, including mainframe environments and the legacy shares nobody owns.
Sanctioned and shadow alike, discovered as assets with the data in their reach, plus the RAG pipelines, vector stores, and prompts behind them.
Data in motion, code repositories, and the embeddable SDK for classifying inside your own applications.
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.
Outside tests and an internal workflow both back it. 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.
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.
Yes. The same data classification covers personal, financial, and health data. 2,000+ pretrained categories identify PII and other regulated data in structured and unstructured sources, in the languages your business runs on (Forrester scored BigID 5 out of 5 on language support). The classified inventory is what privacy and compliance programs work from, including GDPR, CCPA, HIPAA, and PCI DSS, and it feeds privacy and compliance automation directly.
It is the foundation DSPM is built on. Data security posture management (DSPM) depends on knowing what data exists, where it lives, and how sensitive it is. Discovery and classification produces that inventory, and DSPM, access governance, data lifecycle management, and AI governance each work from the same findings.
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.
A scoped assessment on your real data tells you more than a demo on sample data, across the formats, volumes, platforms, and AI agents you need covered.
