Trying to track where sensitive data lives in the cloud can feel like a never-ending game of whack-a-mole. One minute you've mapped your critical data stores—next, someone spins up a new S3 bucket or Snowflake schema with exposed PII. And when a breach or audit hits? Chaos. That’s why DSPM solutions have become mission-critical for cloud data security.
What is DSPM?
This is where data security posture management (DSPM) becomes key. DSPM refers to a set of solutions that continuously oversee and assess an organization’s cloud data security practices and configurations, aiming to uncover weaknesses and potential threats across all cloud platforms. As Gartner describes it, DSPM gives organizations detailed visibility into the location of sensitive data, who can access it, how it’s being used, and the overall security status of that data or application.
The evolution of data security and why DSPM matters now
From perimeter-based castles to cloud-native chessboards, let’s see how data protection has evolved to meet modern infrastructure realities.
From traditional DLP to modern DSPM
Remember the good old days when data loss prevention (DLP) tools were the hottest thing in security? They worked reasonably well when data mostly stayed put in on-premises environments with clear perimeters. But then the cloud happened, and those legacy tools started showing their age:
They rely on agents (and we all know how much developers love installing agents).
They generate so many false positives that you need a dedicated team just to sort through alerts.
They're painfully slow and resource-intensive.
They lack the cloud context to understand what actually matters.
Another reason DLP tools are no longer up to the task of protecting businesses? According to Forrester's State of Application Security 2024 report, 21% of enterprise breaches in the past year originated from attacks targeting employees' remote work environments. DSPM evolved specifically to address these challenges. Modern DSPM solutions—especially those built on a security graph like Wiz—don’t just scan data in isolation. They correlate it with identity, workload, and configuration context to eliminate blind spots. That context is critical for pinpointing real risks like toxic combinations, not just listing every exposed S3 bucket.
Cloud-specific data security challenges
Cloud environments throw some unique curveballs at security teams:
Data sprawl across multi-cloud environments: Your data now lives across AWS, Azure, GCP, and countless SaaS platforms, and they each have their own security models and blind spots.
Shadow IT run amok: Teams spin up resources faster than you can say, "security review," creating unknown data repositories outside centralized visibility.
Perimeter? What perimeter?: Traditional security assumed clear boundaries. In cloud-native architectures, there's no clear edge. We see resources constantly scale up, down, and move across regions all the time.
Containerization and serverless computing: These modern architectures introduce ephemeral resources that traditional security tools simply can't track effectively.
The cost of inadequate cloud data protection
The "we'll deal with it later" approach to data security comes with a steep price tag. Let’s start with the regulator’s baseball bat: GDPR violations can trigger fines of up to 4% of global revenue or €20 million (whichever’s higher) for mishandling EU citizen data. For a mid-sized SaaS company pulling $100M annually, that’s a $4M penalty staring them down.
And if that’s not enough, IBM’s 2023 breach report paints a grimmer picture. The average incident now costs $4.45 million, with healthcare and financial sectors bleeding over $9M per breach. And hey, these aren’t just abstract numbers. We’re talking about 168 days of firefighting (51 days to contain), developer teams getting pulled into forensic audits instead of making progress on feature work, and CTOs losing sleep over Q3 deliverables slipping.
On top of everything, a huge chunk of breach costs stem from operational downtime and customer churn. For a moment, imagine your fintech’s PostgreSQL cluster gets cryptojacked. While your SREs rebuild from backups, transaction processing halts. Merchants can’t process payments, support tickets pile up, and your NPS score tanks 30 points overnight. Meanwhile, your cloud bill balloons from emergency scaling of security tools you didn’t budget for. Now, that’s an operational domino effect that will turn into reputational damage. Just ask companies that have become cautionary tales in security circles.
Main points to consider when evaluating DSPM solutions
Cut through vendor hype by focusing on seven core dimensions. These areas will help you evaluate which tools offer real coverage, align with your team’s workflows, and support long-term cloud data governance.
1. Cloud-native coverage and visibility scope
Effective DSPM begins with understanding what a solution actually scans. Does it cover only object storage (like S3 and GCS), or does it extend across databases, SaaS apps, ephemeral stores, and modern data platforms like Snowflake, Databricks, or Postgres in Kubernetes?
Modern tools should offer agentless, cloud-native scanning—analyzing cloud APIs, bucket metadata, IAM configs, and workload relationships without requiring agents or inline proxies. Just as important: Can they scan across multi-cloud and SaaS environments from a single control plane?
Look for DSPMs that highlight what’s not being scanned—gaps in permissions, connectors, or regions—so you’re never working with a false sense of coverage.
2. Data classification accuracy and flexibility
Sensitive data discovery is only useful if it’s accurate. Classification should be its own evaluation pillar—not just a sub-feature.
Modern DSPMs go beyond RegEx to use machine learning models that learn your organization's data patterns (e.g., distinguishing phone numbers from telemetry IDs). Look for solutions that offer:
Confidence scoring to validate detections
Custom classifiers via YAML or UI (e.g., to detect genomics, source code, or telemetry)
AI/ML models that adapt to structured and semi-structured data
Data previews and validation workflows to prevent false positives from derailing response efforts
3. Risk prioritization and contextual analysis
Thousands of sensitive data findings mean nothing if they’re all treated equally. The best DSPM platforms combine:
Data sensitivity
Identity access patterns
Environment exposure
Surrounding misconfigurations or vulnerabilities
Leading platforms, like Wiz, tie findings into a security graph, showing which data is truly at risk based on real attack paths. For example, a data store that’s public, contains PCI, and is reachable from a compromised workload gets flagged as a critical issue—while an isolated staging bucket with PII might get lower priority.
This context is essential for focusing remediation where it matters most.
4. Governance, compliance, and response
Discovery without governance is just more noise. Your DSPM should help enforce data security policies, support compliance audits, and streamline incident response across teams. Key areas to evaluate include:
Built-in remediation workflows (masking, encryption, permission revocation)
Customizable policies (e.g., geo-fencing, encryption enforcement, tagging)
Policy-as-code and CI/CD integration for preventative enforcement
Support for compliance reporting (GDPR, HIPAA, CCPA, ISO 27001, etc.)
Integration with IR tools (e.g., ticketing, Slack alerts, or automated playbooks)
5. Persona-based workflows and reporting
Different teams have different needs from DSPM. Your data security platform should support varied personas:
Security teams need cross-cloud views, attack path context, and remediation control.
Data teams may only need targeted reports: e.g., “Where is all customer PII across production Snowflake?”
Compliance teams want dashboards and exports for auditors.
Developers need alerting in pull requests, not just static reports.
Look for tools that support custom views, flexible exports, and workflow integrations tailored to your internal stakeholders.
6. Remediation and workflow integration
Discovery means nothing without action. Evaluate how well the DSPM solution plugs into your remediation loops:
Integration with GitHub, Jira, ServiceNow, Slack, or other tooling
Prebuilt automation options (e.g., auto-remediate, or submit for human review)
CI/CD integrations to catch policy violations before deployment
Ability to define and enforce guardrails as code
7. Integration with broader cloud security platforms
The cloud native application protection platform (CNAPP) convergence trend presents both opportunities and challenges. Integrated DSPM-CNAPP solutions reduce tool sprawl by correlating data risks with workload vulnerabilities and identity threats.
Modern DSPM solutions must function as both standalone systems and integral components of a broader CNAPP strategy. This loose coupling ensures that data risks are not siloed from infrastructure or identity risks—and that remediations can be orchestrated across layers (e.g., blocking public access, revoking permissions, or securing exposed workloads from a single interface).
Top 7 DSPM tools compared
The DSPM market includes both point solutions and broader platforms. While standalone tools focus narrowly on data discovery and classification, modern CNAPP platforms integrate DSPM into a broader security graph—correlating data risks with infrastructure, identity, and application context. This approach reduces tool sprawl and enables faster, more effective remediation by unifying visibility across the entire cloud stack.
Based on core capabilities, cloud alignment, and remediation depth, here are seven top DSPM solutions—ranging from dedicated tools to CNAPP platforms with built-in DSPM. Use this breakdown to compare their strengths, ideal use cases, and limitations.
Wiz
G2 rating: 4.7 out of 5 ⭐ (702 reviews)
Overview: Cloud-native, agentless CNAPP with integrated DSPM capabilities
Key features:
AI-powered data classification that combines RegEx with ML to accurately identify sensitive data types across structured and semi-structured cloud stores—custom classifiers support use cases like PHI, source code, or telemetry.
The Wiz Security Graph: A real-time, unified map that correlates data stores, identities, misconfigurations, vulnerabilities, and network paths for full context.
Agentless data discovery across AWS, Azure, GCP, and SaaS without requiring deployment overhead or inline proxies.
Toxic combination detection that highlights real attack paths—e.g., sensitive data exposed via public access, exploitable identities, or vulnerable workloads.
Code-to-data tracing that links data exposure risks back to commits, misconfigured CI/CD pipelines, or infrastructure drift.
Developer-ready remediation workflows, including issue previews, policy suggestions, and enforcement guardrails directly in PRs or CI/CD pipelines.
Ideal use cases: Organizations seeking unified security with graph-based risk correlation that cuts through noise
Considerations: Best fit for cloud-first organizations looking to consolidate security tools and improve developer collaboration. Wiz stands apart as the only DSPM solution built on a cloud-native security graph that connects data risk to identity, misconfigurations, workload posture, and real attack paths. It's ideal for security teams who need fast, contextual insights and developers who want to fix issues where they work.
Microsoft Defender for Cloud
G2 rating: 4.4 out of 5 ⭐ (302 reviews)
Overview: Integrated cloud security platform with expanding DSPM functionality
Key features:
Integrates natively with Azure, leveraging Entra ID access logs and Purview Unified Catalog for enhanced data security
Enables automatic data classification of sensitive SharePoint content and maps access risks to specific Azure AD service principals
Ideal use cases: Microsoft-centric environments and hybrid cloud deployments
Considerations: Capabilities vary across cloud providers, with the strongest support for Azure environments.
Cyera
G2 rating: 5 out of 5 ⭐ (1 review)
Overview: Cloud-native, AI-powered DSPM platform that pioneered security across SaaS, PaaS, and IaaS environments
Key features:
AI-native discovery engine that autonomously classifies data with more than 95% accuracy, learning an organization's unique data patterns rather than relying on simple RegEx matching
Automated remediation workflows for applying encryption, correcting logging configurations, and implementing proper cloud tags without manual intervention
Agentless architecture that connects to datastores with a single IAM role, dramatically simplifying deployment across complex environments
Ideal use cases: Organizations with complex multi-cloud environments seeking high-accuracy data classification and automated remediation
Considerations: Most valuable when integrated into existing security workflows with clear remediation policies defined upfront
Palo Alto Networks Cortex Cloud
G2 rating: 4.1 out of 5 ⭐ (93 reviews)
Overview: Comprehensive CNAPP with expanding data security components
Key features:
Multi-cloud coverage across major providers
Integrated DevSecOps capabilities
AI-powered risk prioritization for data findings
Compliance monitoring automation
Ideal use cases: Large enterprises with diverse cloud footprints
Considerations: Implementation complexity and resource requirements can be significant.
Securiti
G2 rating: 4.8 out of 5 ⭐ (43 reviews)
Overview: Unified Data + AI Command Center platform with integrated DSPM capabilities recognized as a leading innovator in the space
Key features:
Knowledge graph technology that correlates security intelligence across hybrid multi-cloud environments for contextual understanding of data relationships
Data flow mapping that visualizes sensitive information pathways
Automated remediation capabilities that streamline compliance with existing regulations and emerging AI governance frameworks
Ideal use cases: Organizations looking to safely harness data for AI initiatives
Considerations: Implementation complexity may require thoughtful integration planning for existing security and data governance frameworks.
BigID
G2 rating: 4.5 out of 5 ⭐ (15 reviews)
Overview: Data intelligence platform that combines privacy, security, and governance with comprehensive DSPM capabilities powered by generative AI
Key features:
Auto-discovery technology that supports hundreds of data sources across structured, unstructured, on-prem, and cloud environments to eliminate blind spots
Classification engine that combines RegEx with advanced AI/ML techniques
End-to-end risk management from discovery through remediation with automated labeling, tagging, retention, and encryption to streamline security workflows
Ideal use cases: Organizations requiring unified data governance and privacy compliance in a single solution
Considerations: Multiple deployment options allow flexibility but require careful selection based on your specific infrastructure and security needs.
SentinelOne Singularity Cloud
G2 rating: 4.9 out of 5 ⭐ (107 reviews)
Overview: AI-driven security platform with cloud security capabilities
Key features:
The BigID integration brings data lineage tracking to Singularity's XDR platform, enabling SOC teams to trace breach impacts through data relationships rather than just infrastructure.
The solution also features unified visibility across endpoints and the cloud, along with automated response capabilities.
Ideal use cases: Organizations prioritizing automated threat detection and response
Considerations: Integration with existing security workflows requires careful planning.
Implementation best practices for DSPM
To get real value from a DSPM solution, implementation needs to go beyond data discovery. Here’s how leading teams roll it out effectively:
Start with agentless data discovery
Prioritize solutions that integrate via cloud APIs for immediate visibility—without agents or downtime. Use this early map to surface unknown data stores, shadow assets, and overly exposed sensitive data across multi-cloud and SaaS.
Correlate with existing risk signals
Integrate DSPM into your cloud security platform (CNAPP, CSPM, CIEM) so you can prioritize not just based on data type, but based on who has access, what the surrounding risks are, and whether that data is reachable by an attacker. This is where graph-based solutions shine.
Establish shared remediation workflows
Connect your DSPM to developer tools (e.g., GitHub, Jira, Slack) to route alerts and suggested fixes directly into workflows your teams already use. Security should guide; dev should own the fix.
Operationalize policy as code
Define and enforce data security policies (like encryption, masking, and access controls) through IaC guardrails and CI/CD policy engines. DSPM becomes proactive when it helps prevent issues, not just flag them.
Measure what matters
Track improvements in:
% of sensitive data with full context (identity, exposure, workload)
Reduction in toxic data/identity/infrastructure combinations
Mean time to remediation (MTTR) for critical data risks
Policy coverage across cloud environments (encryption, access, region)
Parting thoughts
The days of perimeter-based security are long gone. In cloud environments, data is the new perimeter, and it's constantly shifting. DSPM solutions give cloud data security teams the visibility and control they need to protect sensitive data wherever it lives.
Looking ahead, DSPM will continue to evolve alongside cloud technologies. AI/ML integration will improve detection capabilities, zero-trust data access models will become standard, and DSPM will increasingly converge with broader cloud security platforms to provide unified protection.
Wiz stands out by dynamically correlating sensitive data, identities, misconfigurations, vulnerabilities, and network paths into a single, prioritized risk view. This graph-based approach eliminates noise and helps teams break real attack paths to sensitive data—before they’re exploited.
Ready to see it in action? Request a demo and explore how Wiz secures sensitive data across your entire cloud stack.