Data classification: The fundamental requirement of successful DLP programs

The way we store data has undergone a dramatic shift. From good ol' floppy disks   to today's cloud storage solutions, advancements in storage have slashed costs and allowed organizations to hold exponentially more data. And we are all taking advantage of this. In fact, data is proliferating at a staggering 25% annually. This presents a double-edged sword. On the one hand, this vast data lake holds immense potential for valuable insights. On the other hand, monitoring data across endpoints, servers, and the cloud can overwhelm security teams. Security analysts are finding it hard to overcome this challenge. The 2023 TechStrong PulseMeter Report reaffirms this by stating that 47% of organizations aren't completely aware of where their sensitive data is stored. Since knowing the data you store and classifying it based on sensitivity form the core of a good data loss prevention (DLP) program, lacking this information could lead to a rise in data breaches. The Identity Theft Resource Program (ITRP) confirms this, with its report showing a 78% increase in data breaches from 2022 to 2023—and the ITRP says that stat is poised to increase and evolve in 2024. This makes having an understanding of what data you store, where you store it, and knowing how sensitive it is all the more important for efficient DLP. You cannot apply DLP policies without knowing how restrictive they need to be or what data they need to be applied to. Therefore, it's important to discover and classify sensitive data before attempting to apply policies to secure it.

 Why do we need to start with data classification?

Data classification is a proactive measure towards data security. It involves classifying data into categories based on various parameters, such as data type, owner, or department, but most critically, its sensitivity and vulnerability. Classifying your data based on sensitivity can help you respond to incidents faster and prevent the misuse and loss of sensitive data. In this blog, you'll discover the importance of data classification in preventing data loss and how you can effectively classify data.

The importance of data classification for DLP

Forrester states that data classification is the cornerstone of data security and preventing data loss. An efficient data classification strategy serves as a compass to IT administrators, assisting them in directing their DLP efforts and focus towards safeguarding critical data assets, along with:

  • Prioritizing and allocating resources: A one-size-fits-all approach to DLP could be ineffective. Classifying your files into sensitivity levels, such as public, internal, restricted, and confidential, can provide insights into the types of data contained in each file. This enables IT administrators to prioritize their efforts and allocate resources towards securing their most sensitive data first and least sensitive data last.

  • Managing data flow: With cross-functional teams collaborating across different regions, your data is always on the move. Managing data flow becomes challenging when you are uncertain of the data you possess. This is where data classification comes into play—categorizing your data helps you stay informed about its whereabouts. By doing so, you can regulate its flow and ensure your data doesn't fall into the wrong hands.

  • Enforcing DLP policies: Efficient classification of data can help in implementing appropriate security controls based on the sensitivity of the data. For instance, access to sensitive data, such as PII, PHI, or financial information, must be restricted to authorized personnel only. Classifying files containing this data as sensitive can help in targeting them with the right DLP policies, preventing unauthorized access and mitigating the risk of data loss.

  • Adhering to regulatory requirements: Regulations like the GDPR, HIPAA, or the PCI DSS require organizations to adhere to specific rules regarding data discovery and classification. For example, the GDPR stipulates that organizations must maintain a record of all activities performed with or on sensitive data, including details on sensitive data processed and the technical measures employed to protect it. Identifying and classifying your sensitive data helps with adhering to such regulations.

  • Responding to incidents: In the event of a data breach or a cyber incident, classifying your data as sensitive or critical allows you to execute a targeted response. Security teams can then prioritize recovery efforts, focusing on the most valuable information first. This minimizes the impact of the breach and streamlines the recovery process, saving time and resources.

Factors to consider while classifying data to prevent data loss

Now that we've established that data classification is essential for DLP, it's important to understand how it can be achieved. Classifying data can be tricky because you want it to be easily identifiable for users without putting all your eggs in one basket. Let's explore how factors of a classification strategy can impact data loss:

  • Levels of classification: Getting the number of classification levels right is vital. Having too few can result in inconsistent categorization of sensitive data, while having too many can complicate incident response and DLP efforts. According to Capterra's 2023 Data Management Survey, 61% of organizations with three levels of classification experienced a data breach, 75% with four levels faced a breach, and 67% with five levels faced a breach.

  • Method of classification: With the increase in the volume of data generated across organizations, classifying data manually can become tedious and ineffective, with the chance of human error also being a challenge. The same report highlights how 86% of organizations with manual classification faced a data breach, while 55% of organizations with automated classification were impacted.

  • Intent of classification: Classifying your data with the right intentions makes all the difference. The same report states that organizations that classify data to stay compliant with regulations, rather than security, were more prone to data breaches. The best attitude is to improve overall data security so that compliance can follow naturally.

 These are just a few indicators of why getting the strategy right for data classification can be tricky and important. Therefore, it's crucial to equip yourself with the right solutions to classify data.

 Overcoming the classification conundrum for better DLP

Considering the cyberthreat landscape, classifying your sensitive data has become a no brainer. However, the conundrum of choosing the right strategy could daunt the best of security analysts. This is where our data classification solution comes in. With this, you can choose from the right mix of content-, context-, and user-based classification to identify sensitive data, avoid false positives, and focus on critical assets.

Learn more about the importance of data classification for DLP in our on-demand webinar: Data classification: The cornerstone of DLP: [embed]https://www.youtube.com/watch?v=83fPPdjnoYk[/embed]