Is our organization’s data resilient against cyberattacks, even when the identity is compromised? Are we certain? These were already essential questions at a time when the broad adoption of digital transformation means businesses are generating almost incomprehensible amounts of data. And now, artificial intelligence (AI) has upped the ante even further. As AI adoption grows, so does organizations’ appetite for the vast data from disparate sources needed to train AI models. Because of this, companies are grappling with how to safeguard a surging amount of fragmented data wherever it lives.

“As the use of AI intensifies, particularly with the advancement of generative AI models, it becomes the catalyst for a data deluge,” analyst firm IDC says. As a result, “systems will adapt (and) infrastructure will evolve.”

This evolution explains the buzz around DSPM, or data security posture management, a holistic approach to assessing and managing an organization’s cybersecurity readiness and effectiveness in protecting its data assets.

The Cloud Security Alliance, a non-profit tech industry group, calls DSPM “a crucial piece of your cloud security puzzle” that has started to dominate the conversation around protecting mission-critical data. They define DSPM as “a set of practices and technologies used to assess, monitor, and reduce the risk related to data residing in cloud data stores – with a focus on multi-cloud environments. DSPM is data-centric, in that it looks at the context and content of the data being protected, placing the focus on sensitive records such as PII or medical records.”

AI and DSPM go hand in hand because traditional security methods like CNAPP (Cloud-Native Application Protection Platform) alone don’t adequately address an organization’s overall cyber resilience related to data, as they focus on keeping attackers out. Legacy data loss prevention (DLP) tools address the right problem but with the wrong solution. To protect data, being reactive is not enough. You have to be proactive. The rise of digital transformation has led to shadow data, with more businesses realizing they can’t protect against what they can’t see — leaving them vulnerable to cyberattacks.

DSPM solves one of the most irksome aspects of data security: Knowing where all the data is and how to secure it.

In earlier approaches, it was always the organization’s burden to locate these assets manually, determine which should be scanned, and then configure connectors for them. This process is time-consuming, labor-intensive, and complex. Worse, it was ineffective in the cloud, leaving large amounts of sensitive data unaccounted for.

Unlike other alternatives, DSPM is not limited to specific cloud platforms and data asset types, or even to the cloud itself. Many DSPM alternatives require data to be removed from the organization’s environment for scanning, which increases risk. These approaches are also prone to blind spots regarding “shadow data”.

Secret Sauce of DSPM

The secret sauce of DSPM is that it uses intelligent automation to continuously discover, scan and analyze data, classify it by data type and sensitivity level, and protect it against misuse or theft. It does so across multiple cloud, SaaS, and on-premises environments. It also eliminates the need for multiple data scans by various security solutions – with DSPM, it’s one scan, one centralized data snapshot.

Another strength of DSPM is its definition of the attack surface. Traditionally, businesses have viewed this strictly in terms of threats to data from external hackers. However, in today’s world of AI and cloud-native development, data is created, moved, copied, and shared freely by fast-moving developers, data scientists and others.

While this data democratization has helped foster innovation in many companies, it also has raised new worries for security teams. As this democratization happens, it is common for sensitive, proprietary, and highly regulated data to get copied, modified, shared and moved across multiple cloud data stores without any oversight by data security teams.

Risks include accidentally making a file widely accessible in a cloud storage location, copying a database to a test environment without proper protection and forgetting to delete it afterward, and deleting sensitive data from a managed datastore without realizing that versioning is enabled, allowing deleted data to still be accessed through a restore process.

These practices can run rampant throughout an organization, undetected by even the most diligent security teams, and have led to a new problem: Shadow data.

DSPM addresses the issue by providing a unified view of all sensitive data, including each asset’s location, ownership, access control, status and usage.

It also finds and remediates misplaced, redundant, and obsolete data by identifying and preventing data exposure and unnecessary duplication, purging outdated or irrelevant data, and ensuring that policies are in place to monitor data hygiene continuously.

It tackles data access governance by identifying all internal and external users, roles, and resources with access to confidential data. Then, it monitors and controls each user’s access based on their roles and responsibilities, ensuring that only authorized people have access to sensitive assets.

DSPM deals with privacy and compliance obligations by detecting and fixing violations of data privacy regulations and industry standards such as GDPR and PCI DSS, and then generating audit-ready compliance reports.

All of these capabilities have become vital as AI adds more fuel to the world’s data explosion. Enabling AI has become DSPM’s main use case.

The last year has shown that the secret is out: DSPM is the best practice for protecting sensitive data stored in many different environments and types of cloud storage technologies.