Solution hub: Manage AI risk from the data your AI can reach
Data and AI Resilience in the Mythos Era
BigID gives security, privacy, and compliance teams a current, contextual picture of the data estate and the controls to act on it. Crown jewels discovered and classified across cloud, on-prem, SaaS, and unstructured sources. Every AI pipeline, copilot, and agent mapped to the sensitive data it can reach. DSPM and AISPM on one platform, with access governance, context-aware DLP, and automated retention and deletion bringing the attack surface down continuously. The organizations that already know what they hold, what it is worth, and how it is governed are the ones that stay resilient as Mythos-class capability reaches the broader market.
Highest possible score in eleven criteria, and a Leader, one of three among the ten vendors evaluated
Intuit classification challenge
96.5%, ranked #1
Weighted precision and recall on a 40,000 record dataset, first against 19 other vendors from the US and Israel
Free risk assessment
Two weeks
From connecting your first source to an executive-ready report on high-risk data and AI, agentless, on your real data
What BigID does here
Put every AI system on the same governed inventory as your data
Claude Mythos compressed exploit discovery from weeks to hours, and in its first weeks of restricted deployment through Project Glasswing it identified more than 10,000 high or critical severity vulnerabilities in the world's most systemically important software. What an AI-powered attacker finds behind an exploit is the part BigID changes: decades of accumulated sensitive data spread across cloud, SaaS, on-prem systems, data lakes, and collaboration platforms, most of it ungoverned, unclassified, and invisible to the teams accountable for protecting it. Resilience against that is built in the data estate, well before an incident.
Know what you hold
Continuous discovery and classification across cloud, on-prem, SaaS, unstructured repositories, and AI ecosystems, correlating sensitivity, identity, location, and business value so the highest-impact assets are named rather than assumed.
Govern how AI reaches it
Every pipeline, RAG architecture, copilot, and agent mapped to the sensitive data in its reach, with ungoverned inputs flagged before they enter model context and third-party AI tracked alongside internal deployments.
Act on business context, not identity alone
Controls that evaluate why data is being accessed, applying policy by sensitivity, business purpose, and risk, then remediating, retaining, and deleting to bring exposure down continuously.
See what BigID can do
Watch it work
Mythos Risk: Find Exposed Data Before AI Does
Finding the sensitive data an autonomous attacker would reach first, and cutting the exposure down before anything goes looking for it.
One system of record for the data estate, and the controls to act on it
BigID connects the sources, reads the data directly rather than inferring from metadata, and holds one inventory that carries sensitivity, identity, location, and business value together. Discovery, risk, governance, and enforcement all run against that same record, which is what makes a posture question answerable on the day it is asked instead of assembled during an incident.
The inventory is current, so an exposure question is answered with what is true today, including everything created or copied since the last review.
A new AI surface arrives governed, with the data each pipeline, copilot, and agent can reach mapped at deployment rather than discovered afterwards.
The reporting already exists, so a board or regulator question about what was exposed and how it was governed has a documented answer.
Sources connect agentlessly, and BigID reads the data itself rather than inferring from metadata, which is what lets one inventory carry sensitivity, identity, location, and business value together. Everything downstream runs against that single record: classification, risk assessment in business context, access governance across people and AI systems, and the controls that enforce policy and dispose of what is no longer needed. The outcome is a named set of crown jewels, AI surfaces governed at deployment, an attack surface that comes down continuously, and posture reporting that is ready when it is asked for.Capabilities
From crown jewels to enforced controls, what BigID puts in place
The groups below run in the order resilience gets built: find what matters, govern what your AI can reach, control who and what gets to it, close the credential paths, dispose of what is no longer needed, and keep the reporting current enough to answer for all of it.
Crown jewel discovery
Resilience starts with knowing what matters most. BigID reads data directly rather than inferring from metadata, correlating sensitivity, identity, location, and business value, so what comes back is a named inventory of the assets that carry the highest business impact if they are exposed.
Continuous discovery and classification across cloud, on-prem, SaaS, unstructured repositories, and AI ecosystems
100+ sources and hundreds of file formats, agentless, with nothing installed on the sources themselves
PII, credentials, source code, regulated records, and model training data identified as distinct classes
Shadow data surfaced from the systems nobody has reviewed in years
2,000+ pretrained categories, plus prompt based classification for the data types specific to your business
Change based rescanning, so new and modified data is reassessed as it changes rather than on a quarterly calendar
Business context attached to every finding, so a result carries impact as well as sensitivity
Ranked #1 in data classification by Intuit, at 96.5% weighted precision and recall
AI and agent risk management
Most organizations are deploying AI faster than they are governing it. BigID maps sensitive data exposure across every pipeline, RAG architecture, copilot, and autonomous agent, so a new AI surface arrives with its data reach already known and governed from the start.
Map what sensitive data is exposed to each AI system, model, and agent
Identify which models can reach regulated information, before a regulator does
Flag ungoverned inputs before they enter model context
Discover unsanctioned models and shadow AI, including unauthorized use of sensitive data
Identify MCP-exposed AI data across the environment
Track third-party AI tool risk alongside internal deployments
Discover internal and external agents, and govern their privileges like any other identity
Infer agent intent from configuration and behaviour, for early warning on a setup heading the wrong way
Detect anomalous agent access against a baseline built from that agent's own activity
Monitor coordinated behaviour across multiple agents coming soon
Data access governance
As agents become more autonomous, the shift from identity-centric to data-centric security stops being optional. BigID governs access by what the data is and why it is being reached, applying the same review, attestation, and enforcement to employees and AI systems on one inventory.
Fine grained permission and entitlement analysis across users, service accounts, and AI systems
Agentic access controls for autonomous workflows, evaluated against intended use and permission scope
Access review and attestation on a cadence, so over-permissioning surfaces before an incident does
Policy violating access monitored, with revocation that runs against the source
Context-aware DLP that accounts for sensitivity, data type, and business purpose
Access privileges preserved for employees interacting with copilots and agents
Sensitive data sharing with copilots and agents blocked, with guardrails enforced on prompts
Unusual download volumes and cross border data sharing flagged against a real behavioural baseline
Secrets security
Credentials left in code, logs, and chat are the cheapest path from one system to the next, for an autonomous attacker and a human one alike. Finding them, scoping them to your environment, and remediating them closes those paths in advance and keeps the vault you already run accurate.
Out of the box AI for diverse credential types: service accounts, privileged passwords, API keys, tokens, certificates, and connection strings
Coverage everywhere credentials actually end up, including code, Slack, Teams, files, logs, tickets, and wikis
Review and refine what counts as a secret, so scope matches your environment rather than a generic pattern list
Agentic supervision on discovery, so findings are checked for accuracy before they reach a queue
Duplicate and reused credentials correlated across systems, since reuse is what turns one finding into a path
Full end to end remediation, including complete agentic automation
Handoff to the vault or secrets manager you already run, so findings land where rotation happens
Attack surface reduction
Every duplicate, obsolete, and unused record is one more thing to protect, one more thing to report on, and one more thing to lose. BigID finds it, puts it in front of the owner who can confirm it, and disposes of it responsibly, which lowers exposure and storage cost at the same time.
Exact duplicates, near duplicates, and similar files found across every connected source
The broadest redundant, obsolete, and trivial data detection available
Stale and unused data identified from real access activity rather than file timestamps alone
Out of retention and out of policy data flagged against the regulation that applies to it
Delegated review, so the business owner confirms a finding before anything is touched
Sandboxing and tombstoning, so data leaves reach before a deletion decision is final
Automated retention and deletion that bring the attack surface down continuously
Legal hold and retention checked first, with a full audit trail on every disposition
Posture, reporting, and audit readiness
When the board or a regulator asks what was exposed and how it was governed, the answer should already be documented. DSPM and AISPM run on one platform here, with risk-based prioritization and reporting that reflects posture as it stands rather than a snapshot from last quarter.
DSPM and AISPM in a single platform, over one inventory
Continuous exposure monitoring across every environment where sensitive data lives
Risk-based prioritization, so teams work the assets that carry real business impact first
Automated remediation workflows tied to the finding that triggered them
Audit-ready reporting on current posture, for the board and for the regulator
AI regulatory and control compliance, including the EU AI Act and the NIST AI Risk Management Framework
Customizable dashboards, with ETL into S3, BigQuery, Snowflake, and Databricks
Reporting through Power BI, Looker, and Tableau, plus bespoke reports from inside Claude, Copilot, and GPT
AI security use cases
Where teams put this to work across the AI estate
The same inventory and the same controls cover the full scope of AI risk, from the tools nobody approved through to the data used to train and ground the models you did.
Shadow AI
Unsanctioned models discovered, unauthorized model use of sensitive data found, unauthorized users and vendors surfaced, and MCP-exposed AI data identified.
AI security and governance
AI security posture management and AI risk assessment, agentic access and security controls, and compliance against the EU AI Act and the NIST AI framework.
AI prompt security
Data sharing with copilots and agents monitored, sensitive sharing blocked, guardrails built and enforced, employee access privileges preserved, and labeling applied for Copilot.
Secure AI data prep
Discovery and classification by risk and business context, a searchable catalog of gen AI data for curating training sets, and an audit of which data went into which model.
Agent access security
Agent identification and discovery, access permission governance, and activity monitoring with data tracking and traceability.
Where it runs
One platform across every place sensitive data accumulates
The same discovery, the same classification, and the same controls run across every family below, agentlessly, with nothing installed on the sources themselves. 100+ cloud, on-prem, SaaS, and unstructured sources come back as one system of record rather than as several disconnected inventories.
Public cloud
AWS, Azure, and Google Cloud, including object storage down to the bucket and the prefix.
SaaS and collaboration
Microsoft 365, Google Workspace, Box, Dropbox, Slack, and Teams, where most of the copies and most of the open share links live.
Dev, code, and pipelines
Repositories, CI logs, build artifacts, and data in motion, with the embeddable SDK for classifying inside your own applications.
Data center and mainframe
SMB, NFS, CIFS, and NetApp, plus mainframe environments and the legacy shares that predate the current team.
Warehouses, BI, and business systems
Snowflake, Databricks, and BigQuery, alongside marketing, content management, and BI platforms holding copies of their own.
AI models and agents
Sanctioned and shadow alike, with the identity each one runs under, the data in its reach, and the RAG pipelines and vector stores behind it.
Third party validation
The scores and benchmarks behind the claims on this page
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 against 25 criteria
Cited for strengths in discovery across cloud and on-premises sources, mainframe environments included, and for a blend of classification techniques, enrichment, and tuning covering use cases from compliance and information governance through to AI security and governance
Frost & Sullivan 2025 Company of the Year for AI Governance
A Leader across all DSPM research, including the Omdia DSPM Universe, CB Insights for DSPM, TAG, GigaOm, and Frost & Sullivan
A Leader across four GigaOm evaluations: DSPM, Data Security Platforms, Unstructured Data Management, and Data Access Governance
A Leader in every Privacy Management evaluation, including the Forrester Privacy Management Wave and the IDC Privacy Compliance MarketScape
Named in Gartner’s 2025 Market Guides for DLP and for AI TRiSM, and represented more than 30 times across Gartner’s 2025 Hype Cycles for Security, Privacy, and AISPM
And from the teams running it
“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
“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, on automating data management for a few million customers across many systems
Where does this sit alongside our endpoint and network stack?
Underneath it, as the data layer those controls act on. Endpoint, network, and detection tools answer what is happening. BigID answers what is at stake: which data an event actually touched, what it is worth to the business, who and what had access to it, and whether policy was being enforced. That is the layer a resilience program, a regulatory notification, and a board briefing all draw on, and it has to exist before the incident rather than be assembled during one.
We already run DLP and a secrets manager. What changes?
Both get more accurate, and both get context they cannot generate themselves. BigID sits above the DLP tools you already run as the policy engine, correlating violations against the real data landscape and turning thousands of alerts into a prioritized queue with remediation guidance. For secrets, it finds the credentials the vault has no record of, in code, chat, logs, and old runbooks, then hands them back to the vault where rotation happens.
How quickly can we see our own estate?
A complimentary two week engagement on your real data. The assessment is agentless and cloud native, so there is nothing to deploy on the sources, and it returns an executive-ready report identifying the high-risk data and AI across your cloud environments with next-step recommendations. Teams typically use it to size the problem for a budget conversation before scoping anything larger.
Get the picture of your data and AI estate that resilience runs on.
A complimentary two week engagement on your real data, agentless and cloud native, returning an executive-ready report on the high-risk data and AI across your cloud environments with next-step recommendations.
