What is DSPM?
Data security posture management, or DSPM, is a security discipline focused on helping organizations continuously understand and reduce data risk in cloud environments. DSPM goes beyond identifying where sensitive data exists by evaluating how that data is accessed, protected, and potentially exposed across cloud services. This includes sensitive data used in analytics pipelines and AI workflows, such as training datasets, feature stores, and model artifacts.
Modern DSPM solutions automatically discover and classify sensitive data across multi cloud environments, including cloud storage, databases, and applications. They then assess data risk by analyzing access permissions, encryption status, configuration weaknesses, and exposure paths that could allow sensitive information to be accessed or exfiltrated.
Rather than treating all data findings equally, DSPM prioritizes risk based on context. This includes factors such as who can access the data, whether it is publicly exposed, how it moves between systems, and the potential business impact if it were compromised. This risk based approach helps teams focus remediation efforts on the data exposures most likely to lead to a security incident.
DSPM is increasingly critical as organizations store larger volumes of sensitive data in distributed cloud environments. With data constantly moving across services and accounts, manual tracking is not feasible. DSPM provides continuous visibility and context so teams can reduce their data attack surface and prevent breaches before they occur.
Choosing a DSPM Solution? Start Here.
Our buyer’s guide breaks down the must-have features, evaluation criteria, and an RFP template to help you make the right choice.
DSPM vs CSPM
DSPM and CSPM address different but closely related aspects of cloud security. Both are essential, but they answer different questions and focus on different sources of risk.
CSPM focuses on the security posture of cloud infrastructure. It evaluates how cloud services, networks, identities, and workloads are configured, identifying misconfigurations that could expose the environment or enable attackers to move laterally. CSPM helps teams understand infrastructure risk and prioritize the configuration issues that could lead to security incidents.
DSPM focuses specifically on data risk. It discovers and classifies sensitive data, analyzes who can access it, and evaluates how that data could be exposed or exfiltrated. DSPM answers questions that CSPM alone cannot, such as where sensitive data lives, whether it is properly protected, and how access patterns could create risk.
The two disciplines are complementary. Infrastructure misconfigurations often create the conditions that expose sensitive data, while data exposure increases the impact of infrastructure risk. Without DSPM, teams may understand where infrastructure is misconfigured but still lack visibility into whether sensitive data is actually at risk. Without CSPM, teams may know where sensitive data exists but not how attackers could reach it.
Together, DSPM and CSPM provide a more complete picture of cloud risk by connecting data exposure with infrastructure, identity, and access context.
What are the benefits of DSPM?
DSPM helps organizations reduce data risk by bringing continuous visibility, context, and prioritization to cloud environments where sensitive information is widely distributed and constantly changing. As data volumes grow and AI driven workloads become more common, DSPM plays a critical role in helping teams understand which data is at risk and how to reduce that exposure effectively.
Wiz research shows 47% of companies have at least one exposed cloud database, with over 20% containing sensitive data.
1. Clear visibility into sensitive data across cloud and AI workloads
DSPM provides continuous visibility into where sensitive data is stored across cloud services, accounts, and regions. This includes structured and unstructured data in databases, storage services, applications, and data pipelines used for analytics and AI.
By maintaining an up to date inventory of sensitive data, including data used for model training, feature engineering, and experimentation, organizations eliminate blind spots created by cloud data sprawl and emerging AI use cases.
2. Reduced data exposure through contextual risk assessment
Not all sensitive data presents the same level of risk. DSPM evaluates data exposure in context by analyzing access permissions, encryption, network exposure, and how data moves between systems.
This contextual approach helps teams prioritize the data risks most likely to be exploited, such as publicly exposed datasets, overly permissive access to training data, or sensitive information flowing into unintended systems. AI assisted classification and prioritization further reduce noise and help teams focus on what matters most.
3. Smaller data attack surface as environments scale
As organizations adopt more cloud services and AI driven workflows, the number of paths to sensitive data increases. DSPM helps reduce the data attack surface by identifying unnecessary data copies, excessive access, and insecure storage or transfer patterns.
Continuous monitoring ensures that new exposures are detected quickly as environments evolve, helping teams limit how and where sensitive data can be reached, including data that feeds analytics platforms and AI models.
4. Continuous compliance without slowing innovation
DSPM supports compliance with data protection regulations by continuously evaluating how sensitive data is stored, accessed, and protected. This includes data subject to regulations such as GDPR, HIPAA, and PCI DSS, as well as internal governance requirements.
Because DSPM operates continuously, teams can maintain compliance even as data is reused across analytics and AI initiatives, without relying on manual audits or slowing development and experimentation.
5. Faster remediation and improved operational efficiency
By prioritizing data risks based on context and potential impact, DSPM reduces the time security teams spend triaging findings. Clear ownership, actionable remediation guidance, and automation help teams resolve high impact data risks more efficiently.
As a result, organizations can improve data security posture while supporting rapid cloud adoption and responsible AI usage.
How DSPM works
DSPM helps organizations eliminate data security blind spots by continuously discovering sensitive data, evaluating risk in context, and supporting timely remediation. Rather than relying on periodic scans or manual inventories, DSPM operates continuously so teams always understand where sensitive data lives, who can access it, and how it could be exposed.
As highlighted in the Wiz report Cloud Data Security Snapshot: Current Exposure Trends, misconfigurations and insufficient access controls are common drivers of sensitive data exposure, underlining the need for robust DSPM strategies.
Data discovery and cataloging
DSPM begins by automatically discovering sensitive data across cloud environments. This includes structured and unstructured data stored in databases, object storage, applications, analytics platforms, and data pipelines.
Automated discovery and classification allow DSPM to identify sensitive information at scale, including personal data, financial records, regulated data, and proprietary business information. This approach also extends to data used in analytics and AI workflows, such as training datasets, feature stores, and experimentation environments.
By maintaining a continuously updated inventory, DSPM eliminates blind spots caused by shadow data, forgotten storage locations, or rapidly changing cloud services.
Data mapping and access visibility
Once data is discovered, DSPM builds visibility into how that data is accessed and how it moves through the environment. This includes understanding which users, roles, services, and applications can access sensitive data, as well as how data flows between systems.
Mapping data access and movement provides critical context. It helps teams understand not just where data exists, but how it could be reached, copied, or exposed. This is especially important in modern environments where data is reused across multiple services and feeds downstream analytics and AI systems.
Security assessment and risk prioritization
DSPM evaluates data risk by analyzing access permissions, encryption status, configuration weaknesses, and exposure paths. Rather than treating all findings equally, DSPM prioritizes risk based on context and potential impact.
For example, DSPM can distinguish between sensitive data that is tightly controlled and internal, versus data that is publicly accessible or reachable through overly permissive access. AI assisted classification and prioritization help DSPM scale this analysis by reducing false positives and highlighting the risks most likely to lead to data exposure.
This risk based approach allows teams to focus remediation efforts where they will have the greatest effect.
Configuration and policy management
DSPM supports enforcement of data protection policies across cloud environments. This includes identifying when sensitive data is stored without encryption, when access permissions exceed what is necessary, or when data handling practices violate internal or regulatory requirements.
By continuously evaluating configurations and policies, DSPM helps teams detect drift early and maintain consistent data protection even as environments evolve. Clear guidance supports remediation without requiring deep manual investigation.
Monitoring, alerting, and reporting
DSPM continuously monitors for changes that introduce new data risk. This includes new datasets, changes in access permissions, movement of sensitive data to new locations, or exposure caused by misconfiguration.
Dashboards and reports highlight the most critical data risks first, helping teams focus attention where it matters. Reporting also supports governance and compliance efforts by providing visibility into data protection posture over time.
Remediation and response
DSPM supports remediation by connecting prioritized risks to clear actions. This can include adjusting access permissions, enabling encryption, restricting data movement, or isolating exposed datasets.
Automation helps teams respond more quickly and consistently, reducing the window of exposure. By tying remediation directly to prioritized risk, DSPM enables organizations to reduce data exposure without overwhelming security or cloud teams.
3 high-value use cases for DSPM
DSPM is most valuable when it helps organizations reduce real data risk in complex, fast changing cloud environments. The following use cases illustrate where DSPM delivers the greatest impact.
| Use case | Description | Example scenario |
|---|---|---|
| Data security in complex cloud environments | Hybrid and multi-cloud environments increase complexity, which makes it challenging to maintain data security across the board. In response, DSPM solutions streamline data security across large organizations' multiple cloud environments. | A large international firm uses multiple cloud providers and finds sensitive financial data in an unsecured S3 bucket with a DSPM. The tool alerts security and adjusts permissions to prevent exposure. |
| Insider threat detection | Most DSPM solutions monitor user access patterns and analyze user behavior. This helps organizations quickly block unauthorized access, changes, and data exfiltration. | An investment bank’s DSPM detects a representative accessing many customer records after hours. The system sends an alert to the security team to investigate the actions for theft. |
| Data privacy compliance | Organizations must comply with certain industry- and country-specific data privacy regulations. DSPM solutions help by providing visibility into security configurations, data handling practices, and access controls, as well as providing regulatory compliance reports. | A large online retailer utilizes DSPM to identify and classify customer information for GDPR. Its system finds EU resident data on non-EU servers, which violates requirements. The company can quickly remediate the issue for compliance. |
What are some challenges associated with DSPM?
DSPM can significantly improve data security, but its effectiveness depends on how well it is implemented and integrated into the broader cloud security strategy. Several common challenges can limit the value organizations get from DSPM if they are not addressed upfront.
Data discovery and classification: Manual or inefficient classification tools can expose your team to more errors and inefficiencies, especially in multi-cloud environments. To achieve agentless scanning throughout your cloud computing environment, you can implement tools with automated discovery and machine learning.
Policy and compliance management: Poor or misaligned policies can lead to lackluster security enforcement, threats, and exposure. To combat this, make sure your DSPM works with IAM tools, the proper compliance frameworks, and your infrastructure.
Assessment and monitoring: Not all DSPM tools provide efficient real-time alerts or prioritized insights, or they may only give you a peek into an ununified security environment. Instead, you can choose a tool that unifies your entire security posture and immediately provides dashboards and alerts that prioritize high-risk issues.
Shadow data: Information that bypasses security protocols could expose your data to exposure and compliance violations. To secure your information properly, you can leverage DSPM tools to map out shadow data throughout your cloud environment.
Integration: Whether you have a legacy system or use multiple cloud security tools, you could face gaps and visibility issues that prevent you from keeping your users, customers, and teams safe. You can review your security system and define the gaps to fix these issues. Then, you can adopt a CNAPP like Wiz that unifies your security for a holistic, more effective posture.
An introduction to DSPM tools
DSPM tools vary in how they approach data discovery, classification, and risk management. Some focus primarily on identifying and classifying sensitive data, while others emphasize broader context by integrating data risk with cloud infrastructure, identity, and exposure signals.
When evaluating DSPM tools, organizations should consider how well a solution fits their cloud environment, security workflows, and scale requirements. Factors such as deployment model, cloud coverage, and integration with existing security tooling can significantly impact effectiveness and operational overhead.
Below are examples of DSPM solutions, each reflecting a different approach to data security posture management.
| Tool | Key features and use cases |
|---|---|
| Wiz | Wiz provides DSPM as part of a unified cloud security platform. Its DSPM capabilities focus on discovering sensitive data and evaluating data risk in the context of cloud exposure, identities, and infrastructure. This integrated approach helps teams understand how sensitive data could be reached and prioritize remediation based on real risk. |
| BigID | BigID’s tool discovers and classifies data in your cloud or hybrid environment, helping to improve your compliance management and risk assessment process. |
| Sentra | Sentra’s solution provides automated information discovery and classification. The tool also improves visibility and control access for cloud landscapes. |
| Varonis | Varonis’s DSPM provides data discovery and response features with many data classes that detect and mitigate threats. It also helps you manage your compliance, even with a significant infrastructure. |
If you want to see Wiz’s tool in action and some of its use cases, continue below for a walkthrough.
What to look for in a DSPM solution
Choosing a DSPM solution is not just about finding sensitive data. The most effective solutions help teams understand how data risk emerges in real cloud environments and how to reduce that risk without adding operational complexity.
When evaluating DSPM tools, consider the following capabilities.
1. Rapid, agentless visibility into critical data
To streamline visibility into critical data, select a DSPM solution that quickly scans your organization's infrastructure for sensitive data without installing agents on individual systems.
2. Centralized dashboard and reporting
The DSPM solution must provide a centralized dashboard with comprehensive reporting capabilities, real-time monitoring, and customizable visualizations for better insights into your organization's data security posture.
3. Continuous detection and prioritization of critical data exposure
Look for a DSPM solution that continuously monitors and detects critical data exposure. The solution should also offer automated data classification to help you prioritize risks and address the most critical ones.
4. Data lineage mapping
Consider a DSPM solution that implements data lineage mapping to understand and trace the data lifecycle: origin, movement, transformation, and storage. This will help you detect backdoors and non-compliance issues.
5. Real-time remediation
Choose a DSPM solution that allows you to automatically remediate identified security issues in real time with minimal human intervention.
6. CI/CD integration for data exposure prevention
Opt for a DSPM solution that integrates with continuous integration/continuous deployment (CI/CD) pipelines. Most DSPM solutions with this capability automatically scan and enforce security policies from code, infrastructure, and dependencies for more comprehensive coverage.
7. Automated compliance assessments
A DSPM solution must be able to scan for compliance violations, generate compliance reports, and provide recommendations to address non-compliance issues.
8. Ability to protect sensitive AI training data
As organizations continue to explore AI's potential, the risk of sensitive data swells. For example, in 2023, Wiz discovered that Microsoft AI researchers accidentally exposed 38 terabytes of data. This is just one example of the new data security risks and attack surfaces that security teams now grapple with.
AI systems are increasingly reliant on sensitive data. These AI models receive training on massive amounts of data, often including sensitive information such as PII, financial data, and health records. To safeguard sensitive AI training data in the cloud, organizations must extend their DSPM capabilities to AI. Because of these challenges, a DSPM tool should automatically detect sensitive training data and proactively remove its attack paths.
9. Scalability and performance
The DSPM solution must be easily scalable for enterprises and large organizations to avoid performance lags when datasets spike to quintillions. A CNAPP that incorporates a DSPM and all other essential security solutions can provide a holistic and practical answer for scalability – no matter the organization's size.
Should DSPM be a stand-alone solution?
DSPM works best as part of an integrated cloud security platform rather than a standalone tool. Organizations are consolidating point solutions to reduce complexity and improve security effectiveness.
Why integrated DSPM delivers better results:
Unified risk view: Correlates data risks with infrastructure vulnerabilities and misconfigurations
Reduced alert fatigue: Prioritizes threats based on complete attack path analysis
Streamlined operations: Single platform for security, DevOps, and compliance teams
Benefits of CNAPP-integrated DSPM:
CNAPP is an end-to-end solution that covers CSPM, CIEM, and CWP. Ideally, a CNAPP should include DSPM, though most traditional platforms lack this capability. Adding DSPM enables organizations to consolidate data and cloud security risks into a priority-based list, then identify vulnerabilities and attack paths for fast remediation.
A CNAPP with DSPM captures data origin and flow, securing data movement between cloud storage and application networks.
CNAPP solutions correlate and prioritize security risks before alerting teams, which reduces alert fatigue. DSPM further cuts down alerts so security teams can focus on critical vulnerabilities that require immediate action.
A stand-alone solution, like a siloed DSPM solution, misses the benefits of an integrated approach. Wiz takes a unified cloud security strategy, baking DSPM into other cloudsec use cases.
Wiz DSPM
Wiz delivers a leading DSPM solution as part of a unified cloud security platform that helps organizations understand and reduce real data risk. Rather than treating data exposure as a standalone problem, Wiz connects sensitive data to the cloud infrastructure, identities, and access conditions that determine how that data could actually be reached.
Wiz continuously discovers and classifies sensitive data across cloud storage, databases, managed services, applications, code repositories, analytics pipelines, and AI workflows. This includes data used for model training and experimentation, which is often widely distributed and difficult to track. Broad, agentless coverage helps eliminate blind spots and maintain an accurate view of sensitive data as environments evolve.
To prioritize risk, Wiz correlates data findings with cloud context such as public exposure, identity permissions, vulnerabilities, and lateral movement paths. This graph based analysis shows not just where sensitive data lives, but how attackers could reach it and which paths need to be addressed first. Clear visibility into who can access which data also helps teams reduce excessive permissions and improve data governance.
By integrating DSPM with posture, identity, and exposure context, Wiz helps organizations reduce their data attack surface and remediate the most critical risks faster. Continuous assessment supports compliance and data sovereignty requirements while allowing teams to protect sensitive data used across cloud and AI initiatives without slowing down operations.
Mattress Firm is a great example of a company leveraging integrated DSPM. Customers are the company’s most precious assets, and using DSPM allows it to discover and protect its customers’ data across databases in multi-cloud environments.
At Mattress Firm, we believe in delivering unparalleled service to our customers, and that includes keeping their data safe. Wiz’s data security posture management solution helps us easily answer the question of what data is stored where, helping us protect our customer data in the cloud.
Sloan Rabon, Manager, Application & Cloud Security, Mattress Firm
Are you interested in learning how an integrated DSPM could work in your environment? Schedule a personalized demo today to learn how Wiz can help you improve your overall security posture, meet compliance regulations, reduce your attack surface, and secure complex multi-cloud environments.
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