As cloud computing adoption skyrockets, companies are managing massive amounts of data. With so much of this data now classified as sensitive, protecting the data has never been more critical.
From a data protection standpoint, perhaps the most difficult challenge is that business-critical data worth protecting now takes so many different forms — from intellectual property to financial data to business confidential information to PII, PCI data and more.
Why is DSPM so important?
Traditional data protection methods, like writing rules to discover what data is worth protecting, simply won’t cut it in today’s cloud-centric environment. And because it’s so easy for your employees to create, modify and share sensitive content with anyone, sensitive data is at risk from data loss.
Organizations must be proactive and deploy security strategies and solutions that address these concerns. If they simply fall back on outdated or on-premises security technology, they face elevated risks of data leakage and deployment complications. Identifying meaningful data risk is crucial, which requires understanding data sensitivity, data lineage, and infrastructure or access configurations.
Today, many organizations are adopting some form of data security posture management (DSPM) to assess their cloud security posture and gain a consolidated view into data risks across the entire environment.
This article will explain what DSPM is, explore DSPM tools and their use cases, and help you choose the right DSPM tool.
What is DSPM?
Gartner coined the term in 2022, stating that “data security posture management (DSPM) provides visibility as to where sensitive data is, who has access to that data, how it has been used and what the security posture of the data store or application is.”
DSPM determines an organization’s security posture by analyzing a “data map” of user access to various datasets so it can identify business risks.
Data security posture management is about minimizing the risk involved with data residing in multi-cloud deployments. It includes data classification techniques to identify sensitive data and also adheres to general security posture strategies to address the context of the data.
Organizations also use DSPM as the basis for data risk assessment and to optimize data security governance implementations.
What are DSPM tools?
Data Security Posture Management (DSPM) tools are designed to automate data security across various environments, including cloud and on-premise systems. As data continues to spread across multiple platforms, businesses need to discover, classify, and protect sensitive data.
DSPM tools provide organizations with a unified solution for addressing these challenges by using advanced algorithms and AI to automatically identify sensitive data, assess risk, and apply appropriate security controls.
In other words, they help organizations discover where their data resides, categorize the types of information they store, and assess the level of risk each data set poses. DSPM tools ensure that data security posture is continually monitored and adjusted in real-time, reducing the risk of a breach or unauthorized access.
DSPM tools go beyond basic security controls by focusing on the entire data lifecycle, making sure that sensitive data is continuously protected — no matter where it resides or moves within an organization.
What are the key features of DSPM tools?
DSPM tools come with several essential features that set them apart from other data security solutions.
Autonomous data discovery: DSPM tools automatically scan and identify all types of data—structured and unstructured, no matter where it resides — across various environments (cloud, on-premises, hybrid). This capability ensures that all sensitive data is located, even if it has been spread across multiple systems.
Data classification: Once data is discovered, DSPM tools classify it based on sensitivity levels, such as personally identifiable information (PII), financial data, or intellectual property. This helps prioritize security actions for the most critical assets.
Risk assessment and remediation: DSPM tools provide continuous risk assessments by monitoring data access patterns, user behavior, and potential vulnerabilities. Typically, remediation is automated — enforcing security policies or alerting security teams when unusual activity occurs.
Access control and monitoring: These tools provide granular visibility into who has access to what data and monitor access activities in real-time. They can also enforce access so that only authorized users can access sensitive information and flag any unauthorized access attempts.
Compliance and reporting: DSPM tools help businesses comply with industry regulations such as GDPR, HIPAA, and CCPA by offering automated reporting and policy enforcement. They simplify the audit process and ensure that data protection standards are consistently met.
What are some use cases for DSPM tools?
DSPM tools are applicable in a variety of business scenarios, including:
Regulatory compliance: Many organizations struggle to meet stringent and ever-evolving regulatory requirements, but DSPM tools help by automatically classifying sensitive data, enforcing compliance policies, and providing detailed audit reports.
Data governance: Businesses with sprawling data architectures can easily lose track of where sensitive data is stored. DSPM tools ensure comprehensive data governance by continuously scanning environments and updating security postures in response to new data flows.
Unauthorized access prevention: By monitoring access behaviors and data flows, DSPM tools detect unauthorized access attempts, reducing the risk of data breaches. They also provide security teams with the information and intelligence required for fast incident response.
How do DSPM tools compare to other data security solutions?
While DSPM tools share some core functionality with Data Loss Prevention (DLP), Security Information and Event Management (SIEM) tools, and Cloud Security Posture Management (CSPM), DSPM delivers more targeted and proactive security measures.
Compared to DLP, which focuses primarily on preventing data leaks, DSPM tools provide a holistic view of data security across the entire organization.
SIEM tools focus on aggregating security events but often miss the nuanced data-centric risks that DSPM tools can identify.
Cloud security posture management (CSPM) can scan a wide variety of cloud resources, giving organizations an in-depth and detailed analysis of potential security vulnerabilities in their cloud environment. CSPM can provide a straightforward, lightweight scan of those resources to provide a basic assessment of potential vulnerabilities. But compared to DSPM, a CSPM cannot identify what data is actually at risk. Additionally, it cannot recognize what security posture it should adhere to — meaning who owns the data and who has access to it.
How do I choose the right DSPM tool?
When selecting a DSPM tool, several key factors need to be considered:
Integration: The tool should easily integrate with your existing infrastructure, including cloud environments, on-premise systems, and hybrid models.
Automation: Look for tools with robust automation capabilities that minimize manual intervention — especially in data discovery, classification, and remediation.
Scalability: The DSPM tool should be scalable and flexible to accommodate escalating data volumes and additional business units over time and grow with operations.
Artificial intelligence and Machine Learning: AI and ML capabilities can significantly enhance the accuracy and efficiency of DSPM tools, helping you to detect risks better and respond to incidents faster.
Compliance support: The tool should provide built-in compliance features for the regulations that affect your business and offer easy-to-use reporting and auditing functions.
What should I look for in a DSPM vendor?
When choosing a DSPM vendor, it’s important to focus on key features that provide visibility, risk mitigation, and ease of use.
The features to look for include:
Comprehensive data discovery: Make sure the DSPM solution can identify at-risk data—both structured and unstructured—across all environments, without relying on manual processes.
Context awareness: The platform should go beyond classifying data by understanding its context — such as permissions, users, and applications.
Data lineage and permissions: The solution should track how data is used and who has access, providing a clear view of potential risks.
Autonomous risk Identification: The ability to identify risks like inappropriate permissions, abnormal activity, or sensitive data in the wrong location is critical.
Risk remediation: Finally, a robust DSPM should provide actionable insights that allow your security team to automatically remediate issues like access misconfigurations or data misplacement with minimal manual intervention.
What are the DSPM vendor’s strengths and weaknesses?
When choosing the right DSPM solution, it’s important to approach things knowing that some vendors prioritize fast deployment while others focus on deep integration or compliance focused risk management.
Here are a few of the leading vendors in the marketplace.
Spirion
Spirion offers quick deployment and easy access to data insights and classification, with a user-friendly interface. But Spirion relies on pattern-based regex discovery that can miss subtle risks that AI-driven solutions are better equipped to handle. Its solution may also not be suitable for larger enterprise deployments.
Microsoft Purview
Microsoft Purview excels in its native integration with the Microsoft ecosystem, making it an easy choice for businesses already using Microsoft tools. Its key strength is compliance-driven data risk management. However, it may not offer the same level of nuanced, advanced AI insights as competitors — with a focus primarily on structured data and regulatory requirements.
Netwrix
For organizations that prioritize audit and compliance controls, especially for structured data environments, Netwrix can be a great fit. Like Purview, handling unstructured data may be a problem and visibility can be limited compared to other solutions.
Varonis
Varonis brings a powerful set of tools for data discovery and classification, particularly for structured data environments like databases and file systems. While its rule-based approach is highly effective in stable environments, it may struggle to adapt to hybrid or evolving data infrastructures. Deployment can also be resource intensive.
Concentric AI: leading with automation
Concentric AI stands out in the marketplace by leveraging advanced AI for accurate data discovery and classification, with autonomous risk detection across unstructured data environments. With Concentric AI, the ability to work without rules or pattern-based approaches gives organizations the visibility and adaptability they need to protect their data.
