Cyberhaven Flow

Assigns risk scores to discovered data based on sensitivity, access exposure, and configuration, then continuously monitors access patterns and policy compliance to surface the highest-risk data stores for action.

Discovers and classifies sensitive data (PII, PHI, payment data, IP, secrets) across structured and unstructured stores by combining deterministic techniques such as patterns, keywords, and validators with AI/ML techniques such as unsupervised clustering and small language models. Breadth of the technique blend, and whether classification extends to prompts, model outputs, and vector databases, are the primary differentiators; products that rely on pattern matching alone sit at the low end.

Baselines how users and service accounts normally access sensitive data stores and flags unusual access behavior in real time, such as mass downloads, off-hours access, or first-time access to regulated data, with detailed audit logs for investigating insider risk and compromised accounts. Often sold as data detection and response (DDR); products differ in whether detection uses ML baselining or static rules.

Identifies sensitive data in locations outside authorized data stores, development databases containing production PII, unprotected S3 prefixes, forgotten data lake partitions.

Maps effective permissions to sensitive data stores across cloud IAM, database roles, and SaaS permissions, identifies over-privileged access and dormant entitlements.

Discovers and classifies sensitive data across a heterogeneous cloud estate in one inventory: object storage, managed data warehouses and lakes, cloud database services, and SaaS applications, including sources that are not supported out of the box through custom connectors. Breadth of supported sources and depth per source vary; on-premises and mainframe estates are covered under On-Premises and Mainframe Data Discovery.

Traces the lineage of sensitive data across its life cycle, from origin through movements and transformations between storage locations, services, and users, surfacing unexpected cross-region transfers, shadow copies, and retention policy violations. Lineage depth (table and column level versus store level) varies; AI pipelines are covered under AI Pipeline Data Security.

Improves classification precision over time through administrator false-positive flagging, classifier threshold and rule tuning, custom classifier authoring, and workflows that route uncertain results to data owners for validation or exception handling. Whether stakeholder feedback retrains the classifiers, or only suppresses individual findings, is the primary differentiator.

Identifies sensitive data as it is created or moves through real-time data flows and pipelines, keeping the inventory current between full scans instead of relying solely on scheduled connector-based rescans of data at rest. Continuous discovery at petabyte scale is an architectural differentiator; most products rescan on a schedule.

Enriches classification results with context beyond the content itself, such as data lineage, effective permissions, storage location, owner, and business metadata, so that a record is labeled by what it is and how it is used rather than by pattern matches alone. Depth of contextual inputs, and whether they change the assigned sensitivity, vary widely across products.

Provides an automated incident response workflow for data loss events.

Discovers and enforces data policies for content stored in or transiting through cloud applications and storage, extending DLP coverage to SaaS environments without endpoint agents.

Applies sensitivity labels to data automatically based on content analysis and context without requiring users to manually classify documents before policy enforcement.

Detects and controls sensitive data entered into generative AI tools, applying block, redact, or warn actions before data leaves the organization.

Correlates DLP policy violations with user behavioral context, distinguishing routine data movement from anomalous exfiltration patterns associated with insider threat or account compromise.

Captures a replayable record of how sensitive data was accessed, copied, and transferred during an exfiltration event, giving investigators step-level evidence beyond point-in-time alerts.

Extracts text from images, scanned PDFs, and screenshots to classify and detect sensitive data that would bypass text-pattern matching.

Correlates user-centric content inspection across multiple channels to detect data loss.

Monitors and enforces data movement policies on endpoints, blocking or logging USB transfers, clipboard operations, print jobs, and screen captures of content matching classification policies.