Crafting an Effective Data Classification Policy for Enhanced Security and Compliance

Published On: February 21, 2024Categories: Blog

Today’s enterprises are generating more data than ever from a broad range of sources. The rise of big data and artificial intelligence (AI) is creating more valuable targets for bad actors. Adding to that, evolving regulations continue to add to the challenge of how data is handled throughout its lifecycle.

Building and refining an effective data classification policy is now critical to the operations of any organization. Even a relatively minor cyber attack can have long-lasting consequences, and so can falling out of compliance with a range of regulations.

Data classification policies aim to better understand a company’s data and how specific data types should be handled. When done well, organizations can bolster security and remain compliant simultaneously.

In this post, we’ll break down why businesses across every sector need a robust data classification and management policy, the benefits of developing one, and how to craft an effective policy.

Data Classification Policy


The Growing Need for Data Classification

From a bird’s eye view, data classification is a set of policies and procedures that define how a company’s data should be categorized, handled, and protected. But why has data classification become so crucial?

Data has become valuable, and not just for the company. Malicious actors are actively trying to access sensitive data due to its value, with one study indicating there were 3,205 publicly reported U.S. data breaches occurring in 2023. At the same time, end users have become increasingly concerned about privacy and how their data is captured and used.

Compliance regulations have emerged largely in response to these changes, further enhancing the need for robust data classification procedures and policies. GDPR, for example, imposes strict and public fines for any entity that mishandles EU citizens’ sensitive data. Since enacted, the EU has imposed approximately €4 billion in GDPR fines. Classifying and protecting sensitive data is clearly critical.

Effectively classifying, processing, and protecting data is no longer optional. Lackluster policies can result in costly breaches, fines, and irreparable damage to your company’s reputation. Businesses need to enact robust data classification policies to remain operational.


Business Benefits of Data Classification

Having an in-depth understanding of your data lays the foundation for an effective protection strategy and helps determine where to apply security controls.

Organizations have a vast amount of data, and accurate classification is crucial to making sense of it. Without proper classification, all data would essentially be treated the same. Instead of trying to protect all of your data, which is resource-intensive, data classification helps you prioritize the most important sensitive, regulated, confidential, and IP data.

How exactly can a comprehensive data classification policy and framework help your organization? Let’s drill deeper into how effective data classification can benefit any organization.

Security Frameworks Requiring Classification

Cybersecurity frameworks aim to help organizations protect their IT assets from bad actors, including sensitive and regulated data. The following frameworks require accurate data classification at a foundational level to enable advanced security controls:

  • NIST 800-53: The National Institute of Standards and Technology (NIST) created NIST 800-53 to help organizations identify any data they’re collecting that requires protection. The standard specifies data classification processes and how categories should be protected. Compliance demonstrates your processes to partners and customers.
  • ISO 27001: The International Organization for Standardization (ISO) 27001 dictates a strict IT security management system, including data classification and management processes. Compliance is challenging, and once achieved, organizations receive an industry-respected certification.

Regulatory Compliance Requiring Classification

One of the most sought-after benefits is remaining in full compliance with applicable regulations, and for good reason — non-compliance is costly and may cause severe reputation damage.

Most compliance standards involve data classification. Some data privacy laws are geographically focused, such as the California’s Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR), which affect a significant amount of businesses as it applies to any organization with customers in the EU. The rights of all data related to EU citizens must meet strict control and protection standards for many types of data, which are detailed in Article 4. Other regulations are industry-specific, such as:

  • HIPAA: All healthcare organizations in the United States must meet stringent data protection controls regulated by the Health Insurance Portability and Accountability Act (HIPAA). Basic patient information, along with sensitive records, must be classified and protected.
  • PCI DSS: The Payment Card Industry Data Security Standard (PCI DSS) requires businesses to identify and secure all user financial information that’s used during credit card transactions. Properly classifying regulated data and secure handling is necessary to maintain compliance.
  • SOX: Publicly traded companies in the United States must comply with the Sarbanes-Oxley Act (SOX). This regulation focuses on protecting investors by improving the accuracy of disclosures and financial statements. SOX mandates strict controls over financial data, which requires data classification to identify, protect, and ensure the accuracy of regulated data.
  • FERPA: The Family Educational Rights and Privacy Act (FERPA) applies to all U.S. educational institutions receiving any funding from the U.S. Department of Education. The goal of FERPA is to protect student records while specifying parent’s rights to their children’s records. Similar to HIPAA and GDPR, data classification is critical for identifying regulated data and implementing effective controls.

Each of these regulations imposes different fines and penalties that create immediate financial repercussions and reputational damage for organizations. 

Minimize Data Breach Risks and Impact

Compliance and risk mitigation go hand in hand, as most compliance standards aim to protect a company’s data. Cyber attackers are continuously exploring new ways to compromise a target’s infrastructure and gain access to valuable, sensitive data

The pervasive nature of third-party supply chains has created a dominant attack vector by allowing malicious actors to exploit vulnerabilities in your partners to compromise their access to your systems. Additionally, the rise of AI-generated ransomware and sophisticated phishing attacks is a growing threat to every organization.

If you don’t understand the wealth of data your company controls, treating every byte as highly sensitive is inefficient and ineffective. 

However, data classification allows for enacting appropriate data protection controls for these categories, which is a critical step in mitigating the impact of data breaches. Additionally, making data, such as access credentials, as secure as possible helps minimize the risk of a successful data breach. 

Data Integrity and Availability

Security and compliance are certainly two valuable benefits of data classification, but they aren’t the only ones. Having processes to classify data and understand how it should be managed ensures data integrity. 

Consistent methods that dictate how data should be captured, stored, and transmitted help maintain the quality and trustworthiness of all data. All functions across an enterprise require data to be accurate to carry out a variety of roles, and inaccurate data can have far-reaching effects.

Similarly, knowing where to look for specific information and how to access it greatly enhances productivity across the entire company.

Time to Market and Prioritized Decisions

With advanced generative AI projects, rapid and secure time to market is essential. Having an accurate and up-to-date data classification policy is not just a protective measure; it’s a strategic tool for fast, informed decision-making. By effectively categorizing data, businesses can prioritize resources for high-value AI initiatives, ensuring that sensitive and regulated data are appropriately identified and protected.

This approach minimizes risk and aligns with compliance needs, enabling a faster, more efficient path to market. It’s about making smart, strategic choices on data utilization, streamlining processes, and leveraging data as a key asset. In essence, a solid data classification framework isn’t just about protection – it’s about enabling your business to act quickly and with confidence, driving growth and maintaining a competitive edge.

Critical Components of an Effective Data Classification Policy

There are many vital components that add up to an effective data classification and data management policy. Some of these components are:

  • Data discovery – it all starts with accurate data discovery. Once you know where all your data is, you need to classify it by assigning a level of sensitivity to each piece of information, which makes it easier to manage and protect. 
  • Data categorization: A core function of data classification is understanding its level of sensitivity. The four common types of data categories are public, internal, confidential, and restricted. Each category is subject to different handling, protection, and deletion policies.
  • Tags, labels, and metadata: Classification involves labeling or tagging data so it can be effectively managed and safeguarded. This component also helps develop other systems that need to use specific types of data.
  • Compliance requirements: We’ve explored how classification is necessary for compliance, but specific requirements should also dictate how information is classified and managed. Creating or refining classification policies should follow any applicable regulations rather than working backward.
  • Documentation: All policies and frameworks related to data classification should be thoroughly documented and made available to those who need them. Readily available documentation ensures adherence to procedures and may also be required for compliance.
  • Business context: How does data fit into various business processes, impact operations, and contribute to current objectives? Data classification schemes should understand the role specific types of data play in the organization and label them appropriately. From there, they can be used to enable benefits such as data-driven insights in specific use cases.

Creating a Data Classification Framework

A framework for data classification hones in on how data is evaluated based on factors like sensitivity, importance, and compliance. It’s one facet of an overall data classification policy.

Developing and continuously refining your data classification framework is of the utmost importance. A high-level overview of creating your framework involves:

  1. Creating data categories: Begin by defining the categories into which data will be classified. The standard categories discussed above are likely relevant to your organization, but you may need to modify them or create additional categories. 
  2. Assigning sensitivity levels to each category: The public data category is typically the least sensitive level, while restricted is the highest. Determine the correct sensitivity levels for each category you create, which will pave the way for other aspects of your data classification policy. 
  3. Determine access controls and logs: Who should be able to access each category of data? Should they be able to modify data or only read it? Access controls should stipulate which user identities can use types of data and then log all access and modify them to create an audit trail.
  4. Create data protection policies: How should data be protected based on classification levels? These protection measures should also have the capability to be monitored and enforced. Ensure the right tools are in place and these policies are thoroughly documented.
  5. Enacting compliance processes: How do your categories and sensitivity levels relate to applicable regulations? Before implementing your framework, make sure your decisions are mapped to specific regulations to stay compliant.

Implementing the Data Classification Policy

A data classification policy is a set of guidelines detailing how data should be handled, protected, and shared based on its classification. 

A complete data classification policy combines many of the components we’ve explored so far, bringing them all together into a robust set of tools and systems to create potent benefits. Implementing or refining a data classification involves the following steps:

  1. Creating a data classification framework to categorize data effectively, as explored above.
  2. Define data handling guidelines for each data category, including which user identities can access specific categories. Ensure an audit log is created to understand when data is accessed or modified.
  3. Develop the right processes to comply with all relevant regulations, which vary based on your jurisdiction and industry. Understand how data must be handled and your reporting requirements when creating these processes.
  4. Enact appropriate security measures to protect data as necessary based on its categorization. Compliance standards or industry best practices often inform how to secure each data category.
  5. Implement a data retention policy that regularly reviews data and secures deletion when necessary. Understand when data must be deleted per compliance standards or internal processes, and make dates readily available for administrators.
  6. Regularly review data classification policies and make changes as necessary to meet evolving compliance regulations. Schedule periodic review periods and be ready for ad hoc changes if compliance requirements shift suddenly.

Leverage Inventa for Automated, Accurate, and Contextual Data Classification

Classifying data is the cornerstone of compliance, security, and extracting the most value from your data. 

However, starting from scratch can be challenging, even for enterprises with skilled in-house teams. Relying on manual data classification is time-consuming, error-prone, and simply unable to keep pace with the volume and velocity of today’s data.

Inventa from 1touch is a comprehensive platform developed to help organizations rapidly and continuously classify data to remain fully compliant and boost risk mitigation. Our advanced platform offers:

  • Rich automation and continuous monitoring to classify data as it’s created.
  • Industry-leading accuracy by leveraging advanced AI/ML techniques with keywords, dictionaries, and regex to classify sensitive data accurately.
  • Powerful contextual classification capabilities to understand the underlying business context of every byte so it can be leveraged to add value to the organization.