Data Classification in DSPM: Why does data classification matter

Data Security Posture Management (DSPM) makes a lot of sense as a cybersecurity strategy. Rather than try and second-guess where an attack might come from and implement defenses against these hypothetical threats, instead start with protecting what really matters. At the heart of DSPM lies Data Classification, the vital phase where an organization identifies and categorizes its data according to sensitivity and risk. Not all data is created equally and therefore it makes sense to focus protection on the most sensitive data.

Protecting data properly can be expensive. You may have a safe at home for valuables but typically it will only be large enough to hold the most precious belongings so not everything benefits from the highest level of safety.

Similarly, data classification systematically organizes data into defined categories based on risk and confidentiality, typically structured around levels such as:

• Public: Information available without restriction.
• Internal: Information for internal use only, not for public disclosure.
• Confidential: Sensitive information that would cause harm if leaked.
• Restricted: Highly sensitive data requiring stringent protection, often regulated (e.g., medical records, financial information).

This structured approach ensures each data type receives appropriate protection, aligning resources to actual risk and avoiding costly missteps.

Classic cybersecurity best practice prioritizes perimeter defenses—firewalls, intrusion detection systems – and platform security – configuration hardening, AV and vulnerability management. Yet breaches persist, often because companies don't fully understand their data landscape. With the rapid growth of cloud computing, remote work, and collaboration platforms (Slack, Google Drive, Microsoft Teams), data spreads across numerous locations, complicating security management. Accurate classification is fundamental to effectively manage and secure data wherever it resides.

DSPM technology typically follows a three-step process:

1. Discovery

The first step is automated discovery—locating data across cloud services, endpoints, databases, file shares, and even hidden within user laptops or legacy systems. DSPM platforms scan continuously, identifying data repositories to ensure no critical data remains undetected.

2. Classification

Once discovered, DSPM tools will apply a variety of Pattern Matching/Regex, machine learning (ML) and advanced natural language processing (NLP) algorithms to classify data accurately.

The best DSPM tools will utilize context-aware AI trained on your real-world datastores, distinguishing clearly between sensitive and non-sensitive data. For example, automatically classifying medical records as 'Restricted', employee records as 'Confidential', and press releases as 'Public', significantly reducing human intervention and error.

3. Tagging and Policy Enforcement

Following classification, data can then be systematically tagged and aligned with security policies. Accurate tagging allows security teams and technologies (like Data Loss Prevention (DLP) tools) to act based on clear contextual rules. For instance, data tagged as ‘Restricted’ is automatically encrypted and strictly access-controlled, while less-sensitive data might be managed with standard protections, optimizing resources effectively.

DSPM is one of the priority cyber security best practices - in fact Control 3 of the CIS Controls is dedicated to Data Protection, with the need for Asset and Software Inventories being the only two higher priority Controls. But being such a dynamic moving target, data location and classification is a continuous and complex process to manage. Which is why modern DSPM tools must be AI-driven by design, not just dressed up in analytics. You don’t need more dashboards. You need decisions: clear, guided actions based on continuous discovery.