Cyera AI Security Platform

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

Acts on discovered data risks either natively or by orchestrating third-party DLP, IAM, EDRM, and ticketing controls: revoking over-permissioned access, quarantining or moving misplaced data, encrypting or masking unprotected files, and applying protection labels. Whether actions execute natively or only through integrated tools, and the breadth of available actions, are the primary differentiators; many DSPM products still leave enforcement to the integrated control.

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.

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.

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

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.

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.

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.

Discovers and classifies sensitive data held in on-premises estates without first migrating it to cloud: Windows file servers, SharePoint Server, NAS, self-managed relational databases such as SQL Server, Oracle, PostgreSQL, and MySQL, and mainframe environments including Db2. Cloud-first products often cover these sources slowly or not at all; depth of mainframe and legacy coverage is a primary differentiator.

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.

Extends access analysis to non-human AI identities, mapping which AI agents, copilots, and stand-alone models can reach which sensitive data stores and flagging over-broad or unsanctioned model access before it is exploited. Coverage of agent frameworks and model identities, and whether findings feed entitlement right-sizing before an AI rollout, vary across products.

Keeps scanned content and derived metadata inside the customer's own environment or chosen jurisdiction during discovery and classification, through in-tenant or in-region scanning, self-hosted deployment, and regional data-plane options, so that sensitive content never crosses a border the organization has not approved. Distinct from Data Residency Compliance, which checks where the organization's own data is stored. The range of sovereignty options varies across products.

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

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

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.

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.

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

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

Consumes sensitivity labels from data classification tools (Purview, Varonis, Nightfall) to apply access governance policies based on data sensitivity tier.

Maps effective permissions to sensitive data stores, identifying every identity with access, at what level, and whether that access has been used recently.

Identifies sensitive data sets with no active owner, no recent access, or no business justification for retention, surfacing candidates for deletion or archival.