Polymer DSPM

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

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.

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

Provides granular incident reporting on data loss events.

Integrates with SIEM platforms for incident response.

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.

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

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.

Produces audit trails and regulation-mapped reports such as GDPR, HIPAA, and PCI DSS data inventories from discovery and access findings, with alerts on policy violations, so that evidence of data-handling practices can be handed to auditors without manual assembly. Custom and stakeholder-specific reporting is a common weak spot across products.

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.

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

Maps how sensitive data moves and transforms through AI pipelines, including model training sets, third-party AI API calls, prompts and model outputs, and vector databases holding embeddings, and flags where regulated data is exposed to a model or a downstream AI service. Depth of coverage for embeddings, fine-tuning data, and third-party AI platforms varies across products.

Identifies sensitive data flowing into large language models and AI assistants such as Microsoft Copilot and ChatGPT, and enforces which generative AI services may use it, in which geographic region, and under which entitlements, reporting unsanctioned AI use. Right-sizing entitlements to stop oversharing before an AI assistant is rolled out is the most common form; blocking is usually delegated to DLP.

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.

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.

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

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.

Automatically detects and anonymizes sensitive fields (names, addresses, contact details) inside prompts or pasted content before submission to an AI service, allowing the interaction to proceed with redacted data instead of blocking it outright.

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.

Shows contextual guidance to the user at the moment a risky AI interaction is blocked or modified, explaining why the action was stopped and steering the user toward sanctioned corporate tools, turning enforcement events into awareness moments.