Data discovery, data classification, data security posture management (DSPM) and data governance are all important elements for effective data management and security. 

In this post, we’ll look at each of these data management functions in turn, explain what they do, why they are important and how they complement each other to form a powerful and effective framework for identifying and managing sensitive information. 

Definitions

What is data discovery?

Data discovery is the process of finding and identifying sensitive data across an organization’s digital environment, including endpoints, databases, file shares, on-premises systems, cloud environments and, increasingly, SaaS and AI applications. Data discovery tells you what data you have and where it's located.

You need data discovery when:

  • You don’t have a clear view of where your sensitive data resides – 42% of businesses do not know what sensitive data they have and where it’s stored

  • You’re preparing for audits or compliance work – security standards like ISO27001, PCI DSS, Cyber Essentials and NIST Cybersecurity Framework require sensitive data assets to be identified to establish scope for compliance 

  • Investigating data breaches – identifying whether sensitive data was present on compromised systems is easier with data discovery tools

  • You’re planning cloud migration, M&A, AI or other technology initiatives that may impact data security

Discovery is the starting point; it gives you visibility across your digital landscape

What is data classification?

Data classification is the process of categorizing and labeling data based on its sensitivity, business value and/or risk so that the right technical and operational controls can be applied. Data classification labeling – sometimes called ‘sensitivity labeling’ – is a prerequisite for technical controls like data loss prevention (DLP) to function effectively. Data classification tells you how sensitive data is so it can be handled appropriately.

You need data classification when:

  • You need to be able to apply different controls to different types of data – regulated data and personally identifiable information (PII) requires a different level of control to publicly available information

  • You use DLP, policy-based encryption and automated retention processes

  • You need users to be able to identify which data is confidential or restricted, so they can treat it with extra care – this includes ensuring that new documents are created using a template that enforces visible labeling as well as metadata tagging

Classification gives meaning to the data you have

What is data security posture management (DSPM)?

Data security posture management is an ongoing monitoring process. DSPM continuously monitors sensitive data and its environment to identify potential risks and exposures, providing an overview of data security posture to prioritize where remediation efforts are needed. DSPM highlights where sensitive data may be exposed and what needs to be fixed first.

You need DSPM when:

  • You process large amounts of sensitive data and need to ensure it is adequately protected – 74% of enterprise organizations store at least 5PB of unstructured data

  • You are responsible for data risk across your organization’s SaaS, cloud and on-premises environments – you can’t secure what you can’t see

  • You need to be able to prioritize remediation efforts and costs where it is most important, based on exposure and risk

  • You need to be able to provide ongoing insights into your data security posture to senior management and the executive team

DSPM builds on discovery and classification and provides visibility of real-time data risk

What is data governance?

Data governance is the framework that defines how data should be managed across the organization based on its sensitivity, business value or risk. It defines ownership and accountability for data security and data risk management, and establishes the policies and rules that determine how the business uses, manages and protects data. It also monitors the effectiveness of controls and adherence to its rules and policies as they are applied by the business. Data governance establishes ownership and accountability for data assets and the rules that apply to it, as well as monitoring adherence to them.

You need data governance when:

  • You need clear accountability for data across the business – 85% of companies have reported increasing customer demand for transparency of data use, and 46% now rank ‘providing clear information about how data is collected and used’ as the most effective action to build customer confidence

  • Your security and compliance obligations require standardized data handling across teams and systems

  • You want security and compliance controls to be sustainable, rather than ad hoc – however, this can be challenging to achieve. Between 2015 and 2023, year-to-year compliance PCI DSS v3.0 to v3.2.1 fell from 48% to 14.3%.

Data governance sets the rule and provides accountability and oversight for data security

How the four work together

Data discovery, data classification, DSPM and data governance serve distinct functions for data security and data management. However, they also complement each other. Data discovery and data classification are foundational to effective DSPM, and data governance establishes the overarching responsibility, accountability and organizational policies for data and its use.

The table compares data discovery, data classification, DSPM and data governance, showing both their differences and complementary roles.

  Main function Typical outputs What it doesn't do (on its own)
Data discovery Find the data (for visibility) - Categorized data asset lists/inventories
- Locations of data stores		 - Apply classification labeling
- Apply policy rules
- Enforce governance requirements
Data classification Label the data (for context and control) - Sensitivity labeling of data assets
- Category labels, tags and other functional identifiers		 - Identification and labeling of unknown data assets
- Enforce governance requirements
DSPM Monitors risk and exposure (for prioritization of fixes) - Data mapping
- Risk insights
- Exposure alerts
- Posture analysis
- Automated remediation and policy enforcement		 - Reliant on discovery, categorization and classification labeling
- Establish governance requirements
Data governance Defines rules and ownership of data (establishing policy, accountability and oversight) - Policies, standards and procedures
- Data stewardship model
- Retention rules
- Oversight reports		 - Functional data discovery, classification or DSPM

 

“These capabilities are most effective when they work together: data discovery provides visibility; classification adds context; DSPM helps prioritize risk; and governance provides the framework for control and accountability.”

Common buying mistakes

It’s important that organizations have mechanisms in place to support the four areas of data discovery, data classification, DSPM and data governance. Aspects of these requirements can be achieved procedurally, others are better suited to purpose-built solutions. Many organizations have incumbent solutions that can provide classification, or help enforce data management policies and controls. 

However, there are some common buying mistakes we frequently see that limit the value of downstream investments with a knock-on effect on data security.

These include:

  • Buying classification without discovery – classification can only be applied to known data assets; classification without discovery leaves businesses exposed to unmanaged and unmanageable data blind spots

  • Treating DSPM as a replacement for discovery – many DSPM solutions include discovery-like features, but few embed discovery as a foundational component. DSPM depends on visibility of sensitive data, assets, access and environmental context. Without these, the insights it offers are incomplete and unreliable. 

  • Assuming governance tools alone addresses security problems – Governance establishes the framework for data security and data management. It sets the ground rules for the secure handling, retention and management of data and monitors their execution by the business.

  • Selecting solutions based on a single use case – Many products and services are originally procured to deliver a targeted program, such as PCI DSS compliance, ISO27001 certification or digital migration. The problem with this approach is that it risks narrowing selection criteria, discounting solutions with features and capabilities that would be beneficial to support wider organizational goals, objectives and obligations but at little-to-no additional cost. 

  • Failing to consider all company environments and data formats – Related to the project-based nature of many procurement decisions, organizations often overlook the range of platforms and environments that their data management solutions – from discovery and classification, to DSPM and governance – need to support. The most commonly overlooked include legacy information systems, hybrid environments and unstructured data stores.

  • Focusing on superficial features over accuracy – Accurate discovery is the single fundamental component that underpins effective data security and data management. However, many businesses focus on polished dashboards and superficial features that add little value, particularly when the underlying detection is poor quality with high false positive rates. 

How Ground Labs Enterprise Recon supports data discovery, classification, security and governance

Understanding the differences between data discovery, data classification, DSPM and data governance is important for organizations selecting solutions to enhance existing data security and management capabilities. However, the real value comes when they are used together. 

Ground Labs Enterprise Recon helps organizations take the first step with accurate discovery, empowering the discovery of sensitive data across complex environments - across structured and unstructured data, on-premises and in the cloud. It offers Purview integration for data classification, alongside risk scoring and inbuilt remediation for prioritized mitigation of data exposures. 

“Enterprise Recon gives organizations a stronger foundation for security, compliance and ongoing data management based on clear, accurate insight into their sensitive data.”

Read more to learn why data discovery is the foundation of data security posture management