Data loss prevention (DLP) tools and processes help ensure that critical data is not accessed by or tampered with by unauthorized users. The underlying technology that can make or break your success in data loss prevention is data classification.
This article explains how data classification affects the success of your data loss prevention measures.
What is data classification?
Data classification is the process of organizing data into relevant categories. These categories can be general, such as Top Secret, Confidential and Public, or quite specific, such as categories aligned with particular regulatory compliance mandates like GDPR and HIPAA.
Data classification helps you improve information security and ensure data privacy by enabling you to assign appropriate access permissions and implement appropriate protection measures for different types of data, such as regularly identifying sensitive data that is overexposed.
What is data loss prevention (DLP)?
Data loss prevention involves protecting sensitive and critical information against inappropriate access or tampering. Data loss prevention tools and processes can reduce data leakage, data loss, data exfiltration, and other risks to critical data.
How does data classification help with data loss prevention?
Data classification helps with DLP in several ways:
Helps establish a firm foundation of strong data governance
Companies often try to put all their eggs in one basket when it comes to DLP, hoping that adopting a single comprehensive DLP product will cover all their data protection needs. But while DLP products do provide security measures, such as lowering the risk of a file on the network being delivered into the wrong hands, they’re not a complete solution.
Rather than focusing only on protecting data from loss with DLP solutions, you need the broad foundation of strong data governance throughout the entire data lifecycle. Data governance requires you to know:
- What kinds of data you have
- Where your data resides
- Who is allowed to access your data
- Who is actually accessing your data
Data classification helps by identifying and labeling sensitive and business-critical information, so you can ensure it is stored only in secure locations and enforce least-privilege and other access policies to reduce the risk of a data breach.
Reduces the number of false positive and false negative results
Accurate data classification is everything when it comes to successful implementation of DLP tools and processes. Inaccurate classification can lead to the following outcomes:
- The DLP tool could restrict access to non-sensitive data that was incorrectly tagged as sensitive, hurting productivity and interfering with critical business processes.
- The DLP could fail to flag unauthorized operations on sensitive data that was misclassified as non-sensitive, increasing the risk of a breach.
Automates the data classification process
Some DLP tools rely on manual classification — users must specify which category their files and other data falls into. This process puts you at risk of both omissions and errors: Users may fail to classify data at all, tag it inconsistently, or simply pick the first or easiest classification type to save time.
An automated data classification solution will provide reliable and consistent classification results across your company and ensure your DLP tool is working with accurate tags.
DLP and Netwrix Data Classification
Netwrix’s data classification software comes with key features that help ensure accuracy and consistency.
- Reusable index — Eliminate the need for lengthy data-recollection every time a new file appears or a classification rule gets changed, so accurate classification results are always available.
- Flexible taxonomy manager — Empower employees to easily create and modify taxonomies to meet your organization’s needs. Eliminate the need to purchase professional services whenever taxonomies need to be added or updated.
- Transparent classification results — See precisely why files were classified the way they were so you can analyze and modify your rules to improve accuracy.
- Remediation workflows — Create automated processes to quarantine sensitive data, revoke excessive permissions and redact data inside files.
Conclusion
The success of any DLP strategy depends upon proper data governance and accurate data classification. Knowing exactly what types of sensitive information you have will enable your DLP solutions to work better, maximizing the value of your investment.
