A Technical Explainer on Data Security Posture Management (DSPM)

Note: this article has been updated as of 6/2/2024

For the modern organization, cloud computing is like a double-edged sword: while it promises substantial cost savings, enhanced business agility, and a remarkable boost to productivity, it simultaneously introduces numerous data challenges. 

From a data protection standpoint, perhaps the most difficult challenge to address 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.

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.

So organizations must take a proactive approach by deploying security strategies and solutions that address these concerns. If they’re not proactive, and simply fall back on outdated or on-premises security technology, they face elevated risks of data leakage and deployment complications.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) and cloud security posture management (CSPM) to assess their cloud security posture and gain a consolidated view into data risks across the entire environment.

What is DSPM?

According to Gartner, who coined the term in 2022, “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 essentially 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.

We discuss how the DSPM process works at the end of this article. 

What problem it solves

Today, enterprises are struggling with three key data challenges:

• Massive growth in data, often exponentially from year to year
• Massive migration of data to the Cloud
• Diverse types of data (such as intellectual property, financial, business confidential, and regulated PII/PCI/PHI data) in increasingly complex environments

Traditionally, matching user access against specific datasets is rather complicated, especially since most IAM and data security tools operate in silos. Not only that, but tracking the evolution of that data across various formats, data locations, and shadow data is crucial for effective posture management.

Like most data security tools, DSPM addresses the need to protect data against exposure for a variety of scenarios. Data security posture management offers extensive data observability to identify these types of security gaps, including real-time visibility into data flows and matching risk and compliance with data security controls. 

DSPM tools also enable organizations to adhere to regulations that require a data risk assessment.

How DSPM compares to CSPM

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. 

There are many tools that offer CSPM, and some leverage feature sets that include AI or machine learning algorithms that can predict or correlate specific vulnerabilities. In the end, the more advanced tools provide a more in-depth analysis of threats. 

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.

DSPM focuses squarely on the data layer, from identifying sensitive data to monitoring and identifying risk to business-critical data such as inappropriate entitlements or access. Modern 

DSPM tools can identify risks and remediate those issues by fixing permissions/entitlements/sharing.

While CSPM focuses on infrastructure-level vulnerabilities that can place networks and infrastructure at risk, DSPM focuses on data layer risk that can cause a data breach or loss.

So, the most significant difference between the two types of posture management comes down to context. Instead of being data agnostic like CSPM, DSPM operates on the assumption that not all data is equal nor should it require a similar security posture. 

Not only does DSPM offer data discovery and classification, it typically leverages AI or Machine Learning to “learn” what security posture it should maintain.

How AI is driving DSPM

In this rapidly evolving landscape of data security, recent advancements in DSPM and CSPM have been significant. As 2024 continues to see the emergence of new threats, the marketplace for AI-based technologies designed to counteract these risks is skyrocketing. Particularly noteworthy is the integration of advanced AI algorithms in DSPM tools, offering more robust and predictive analytics for data security.

As the role of AI and machine learning in DSPM expands, these technologies are enabling more sophisticated data analysis, predictive threat modeling, and automated responses to security incidents. Today, AI has become a core component of effective DSPM strategies, offering unprecedented insights into an organization’s data security posture.

Can DSPM be matured?

The journey to mature DSPM begins with acknowledging existing gaps in an organization’s understanding of data risks. Let’s face it: too many organizations lack insight into the risky use, storage, or movement of their data. 

Recognizing these blind spots is the first step toward developing a more secure data environment. As this awareness grows, the risk to data begins to decrease, paving the way for more targeted and effective data security strategies.

To mature your DSPM, there are six key steps:

• Project start and organization insight 
• Data discovery and categorization
• Risk assessment and management 
• Remediation 
• Prevention 
• Mature and enhance

How the process of DSPM works

With Cloud, every file or data element can be easily shared with anyone around the globe. But this data can also be easily copied, duplicated, modified and shared. Imagine 100 variations of a redlined sensitive contract that needs to be protected, with each version containing different access privileges.

This presents some unique security challenges, which DSPM can effortlessly address with the right tools.

Here’s are the steps DSPM takes to improve an organization’s data security posture:

1. First, it must identify all the sensitive cloud data, from intellectual property to financial to PII/PCI/PHI.
2. Then, it gathers all the information about what data is being shared with whom, and tracks data lineage as it moves across the environment. Identifying where the data may be at risk is a crucial step, as it provides visibility into which data is being shared in accordance with corporate security guidelines and where violations are happening. Typically, the DSPM will alert SOC analysts to provide actionable insights.
3. Finally, and perhaps most importantly, DSPM can remediate those issues as they are happening. For example, it might fix access control issues or permissions. Or, it may disable sharing a sensitive file with a third party that should not be shared.

DSPM with Concentric AI

Concentric AI Semantic Intelligence is a SaaS-based solution that leverages artificial intelligence and deep learning to automatically discover and protect the most sensitive and confidential information contained within your file sharing repositories.

Our technology automatically delivers a content-based, categorized view of your data, including a risk rating for all exposed data. This allows your data security, privacy, and compliance teams to easily find and correct inappropriate sharing of important data files.

Concentric AI is securing the future of work with an agentless platform that is easy to deploy and capable of delivering value in days without any upfront work for you. We offer a free-of-charge pilot program which requires very little time and effort from your teams.

Can DSPM be deployed as a managed service? 

Operationalizing data security can be a significant challenge, even with a full-time security team. From the complexities of diverse data sources and evolving threats to the pressures of compliance and resource constraints, the need for a holistic, autonomous and managed solution is real.  

Concentric AI’s managed DSPM offers a unique blend of autonomous technology and human expertise. Our solution autonomously handles most security tasks, while our dedicated team addresses more intricate challenges. This two-pronged approach ensures comprehensive data protection, reducing potential liabilities and providing 24/7 security assurance. 

Book a demo today to see firsthand — with your own data — how Concentric AI can simplify the complex challenges of identifying and protecting your data.