Polymer DSPM

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.

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

Applies preventative controls automatically such as blocking, encryption, alerting and user justification when sensitive data is detected.

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

Provides an automated incident response workflow for data loss events.

Scores user risk dynamically based on role and behavior to prioritize data loss incidents.

Provides granular incident reporting on data loss events.

Ships policy templates for regulated data types such as PII, PHI and payment or financial data.

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

Integrates with SIEM platforms for incident response.

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.

Discovers and classifies sensitive data (PII, PHI, PCI data, IP) across cloud object storage, relational and NoSQL databases, data lakes, and SaaS platforms using content inspection and ML classification.

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.

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

Continuously monitors data access patterns and flags anomalous or unauthorized access in real time.

Automatically remediates discovered violations, revoking over-permissioned access, moving misplaced data to compliant storage, encrypting unprotected sensitive files.

Identifies data flowing into large language models and enforces data access governance and entitlement for generative AI use.

Detects how sensitive data moves and transforms through AI pipelines to prevent exposure.

Inspects prompts, uploads, and AI-generated responses for sensitive data across modalities, preventing exposure of regulated or proprietary information to third-party AI services.

Defines organizational AI usage policies and enforces them at the point of use - allowing, blocking, redirecting, or constraining specific AI services, models, and features per user, group, or data context.

Discovers and categorizes the organization's use of third-party AI, whether consumed as a service, installed locally, or embedded inside other applications, building a continuously updated inventory of AI usage including shadow AI.