How do DSPM Tools Compare to Traditional Data Discovery Tools?

Data Security Posture Management (DSPM) is a relatively new area in cybersecurity, designed to account for evolving threats and increasingly large and elaborate attack surfaces. It is a data-focused approach to securing complex and multi-cloud environments, including a wide range of capabilities traditionally covered by standalone tools.

Legacy data discovery tools struggle to keep up with the evolution of the digital landscape as more and more organizations employ large and complex networks of cloud platforms. The sheer volume of data handled and stored by many organizations, dispersed throughout the cloud and constantly changing, is often too much for traditional data discovery tools to find.

Importance of Effective Data Discovery

Many organizations these days, especially those in heavily regulated industries, manage and store vast amounts of highly sensitive data that must be protected. The first step to safeguarding data against attacks and accidents is locating, documenting, and classifying all business data. Everything else, from governance to incident response, depends upon the foundation of data discovery.

Data discovery involves synthesizing and analyzing data from several sources, including internal stores and third-party data, to identify patterns and maintain knowledge and control over your data. An effective data discovery tool will offer organizations visibility and insight into their data landscapes, enabling them to make more informed decisions on data governance, access, and protection.

Insufficient data discovery measures in modern digital landscapes can result in a number of issues for data security. Unknown, undiscovered, and unstructured data, known as shadow data, can negatively impact security, productivity, and compliance, leading to consequences like data breaches, regulatory penalties, and business disruptions.

Challenges of Traditional Data Discovery

Traditional tools for data discovery and classification tend to fall short in modern cloud environments. They are not necessarily designed to discover data stores unknown to the organization, and they have difficulties discovering data across the entire environment, including all cloud platforms. They are also often limited to specific types of data.

Deploying a busy tech stack for data protection can lead to problems as well. Implementing individual legacy tools for each step of data protection, from discovery to remediation, can be a difficult and time-consuming process for organizations. The many different data security tools may also be incompatible or unable to integrate with each other, further complicating the picture.

Legacy data discovery tools also tend not to be developed with a massive and continually shifting digital landscape in mind. They are not built to scale for business growth and vast volumes of data, provide additional context for data classification, or understand changes to data.

How DSPM Measures Up

The purpose of DSPM is to protect data and ensure compliance with data privacy and security regulations as digital environments, and the threat landscape continues to grow more complex. As a data-centric approach to cloud security, DSPM directly addresses many challenges accompanying traditional data protection strategies.

Data discovery is just one of many functions that DSPM tools can cover, and they do so with methods and technology built for modern organizations. While it is important to remember that “DSPM tools were not created equal” and assess which one will be most effective for your organization’s needs, many organizations can benefit from implementing DSPM tools.

Unlike many traditional data discovery tools, DSPM is designed specifically to discover data across all platforms—including IaaS, PaaS, and SaaS—in complex and multi-cloud environments. It focuses on the data itself rather than the systems or platforms in order to provide complete visibility into data stores and their security.

Another significant trend in DSPM that separates it from traditional data discovery tools is the strategic use of artificial intelligence and automation. Instead of depending on rule-based measures and manual processes for identifying and classifying data, DSPM tools use advanced technologies and tactics to make data discovery faster and more adaptable.

DSPM Beyond Data Discovery

Because DSPM is designed to holistically identify, classify, and protect data across diverse cloud environments, it covers more than just data discovery capabilities. DSPM tools can cover functions that include not only data discovery but also data classification, risk assessment and prioritization, configuration and policy management, compliance management, reporting and alerting, and incident remediation and prevention.

In order to effectively protect data, organizations can implement DSPM tools to help them make informed security decisions and develop comprehensive data security strategies. Using DSPM can streamline some of the more difficult and time-consuming processes involved with data protection, like categorizing and analyzing data. This can save security teams time and effort on their data security tasks, allowing them to more efficiently protect sensitive data and work on other projects that require a human touch.

Conclusion

It is crucial for organizations to regularly reevaluate and update their security strategies as threat trends, the digital landscape, and technological advances move quickly. Traditional data discovery tools are not built for today’s rapidly evolving and constantly expanding digital environments, but DSPM is.