In 2025, Data Security Posture Management (DSPM) has become essential for organizations that run workloads across multiple clouds. This guide ranks the nine leading DSPM tools, explains their strengths, weaknesses, pricing models, and best use cases, and offers practical advice for choosing the right platform.
The best Data Security Posture Management tools in 2025 are Wiz, Cyera, and Laminar. Wiz excels at holistic multicloud coverage; Cyera offers deep data classification with policy automation; Laminar is ideal for agentless discovery across cloud data stores.
Data Security Posture Management (DSPM) is a fast-growing category focused on continuously discovering, classifying, and protecting sensitive data in public cloud services.
Unlike Cloud Security Posture Management (CSPM), which audits infrastructure configurations, DSPM inspects the data layer itself – automatically detecting where data lives, what type it is, who accesses it, and whether the controls applied meet internal policy and external regulations.
The adoption of GenAI workloads, multicloud data lakes, and SaaS-native ETL pipelines has exploded the volume and spread of sensitive data. Human-driven inventory processes can no longer keep pace.
DSPM tools bring automated discovery and risk scoring, enabling security and data teams to prioritize remediation and avoid costly breaches or compliance fines.
We compared products against seven weighted dimensions: feature depth (25%), ease of deployment (15%), pricing transparency (10%), integration breadth (15%), performance and scalability (15%), support and ecosystem (10%), and customer satisfaction (10%).
Scores are based on vendor documentation, verified peer reviews on Gartner Peer Insights and G2, and publicly available benchmarks.
Wiz tops the list for its unified CNAPP platform that now includes mature DSPM capabilities acquired from Raftt in late 2024. It deploys agentless scans across AWS, Azure, GCP, and OCI, automatically mapping data stores, classifying records with ML-based detectors, and correlating findings with IAM misconfigurations and vulnerabilities.
Users praise its single dashboard and near-real-time graph engine that prioritizes “toxic combinations.” Downsides include premium pricing and fewer on-prem connectors.
Cyera focuses exclusively on data layer visibility. Its deep-learning classifiers recognize over 300 data types, including vector embeddings and code repositories, making it attractive for AI-heavy enterprises. The policy engine offers pre-built templates for GDPR and CCPA, then suggests least-privilege fixes. Customers highlight fast onboarding – most see first results within two hours.
Feature coverage outside the major clouds is still maturing.
Laminar pioneered the agentless DSPM model and remains a market stalwart. Strengths include lightweight deployment, intuitive risk heat maps, and automated remediation playbooks via Jira or ServiceNow. Its recent “Shadow Sync” module detects rogue data copies spun up by CI/CD jobs. Laminar’s interface is praised for clarity, but drill-down analytics can feel limited compared with Wiz.
Dig’s strength lies in real-time data flow monitoring.
The platform taps cloud audit logs and native APIs to detect suspicious data exfil within seconds. Security teams value the correlation between data flows and user behavior analytics. Dig offers a usage-based pricing model, which can spike during incident investigations.
Sentra offers multilayer discovery that spans cloud services and SaaS apps such as Salesforce and GitHub. Its AI Risk Advisor groups findings by business impact and prescribes fixes. Users appreciate Sentra’s built-in compliance reports for ISO 27001 and PCI DSS.
Some reviewers cite occasional false positives in document-centric repositories.
Normalyze leverages a graph-based data catalog to tie sensitive data to users and resources. The policy builder supports natural-language queries, which shortens the gap between discovery and remediation. However, the UI can feel busy, and IAM correlation on Azure remains a work in progress.
DataGuard stands out for its granular object-level lineage in S3 and GCS buckets. Security engineers love the visual graph of data object permissions.
The product integrates well with SIEMs but requires deeper security expertise to tune policies. Pricing is subscription-based and mid-market friendly.
IBM folded Polar Security into its Cloud Pak for Security suite. The tool brings powerful discovery across IBM Cloud and mainframe data sources, making it appealing to large enterprises with hybrid estates. Drawbacks include a heavier installation footprint and slower innovation cadence compared with startups.
Open Raven
Open Raven is an open-architecture platform with strong extensibility via Terraform and custom detectors. Its community edition is popular among security engineers who want visibility without immediate cost. Enterprise features such as automated policy remediation and SOC integrations are catching up but not yet best-in-class.
Start with read-only discovery to build a baseline. Use tiered sensitivity labels (public, internal, restricted) to avoid alert fatigue. Integrate DSPM findings with existing ticketing systems to ensure accountability.
Finally, coach developers on secure data handling; technology alone cannot eliminate data risk.
Even the best DSPM cannot protect data it cannot see inside ad-hoc SQL queries. Galaxy’s version-controlled SQL workspace provides a single source of truth for analytics code, making it easier for DSPM tools to map lineage and monitor access.
By embedding context-aware AI and granular permissions, Galaxy limits accidental data exposure at the query layer – a perfect companion to any of the DSPM leaders above.
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CSPM secures cloud infrastructure configurations such as IAM and network settings. DSPM focuses on discovering and protecting the data itself, adding classification, lineage, and policy enforcement for sensitive information.
Modern platforms use pattern matching, NLP, and machine-learning models trained on labeled datasets. They inspect object metadata, content samples, and access logs to tag data types like PII, PHI, or intellectual property.
Galaxy centralizes and versions SQL queries so security teams can understand data flows originating from analytics code. When paired with a DSPM tool, Galaxy provides clear lineage, reduces shadow queries, and strengthens overall data governance.
Organizations report faster compliance audits, reduced breach likelihood, and lower manual discovery costs. Gartner estimates a 30 percent reduction in data-related incident spending after 12 months of DSPM adoption.
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