Transforming Data Security: How AI and ML is Shaping the Next Generation of Data Security Tools

Originally published by Cyera.

Written by Yana Fesh.

Learn how AI-powered classification is transforming legacy Data Security Posture Management (DSPM) and providing accurate, autonomous insight into your data risk. Understand the limitations of traditional rules-based DSPM and why organizations need to embrace next-gen platforms that leverage AI and ML to stay ahead of modern data threats.

For many years, organizations have relied on legacy data security solutions. These solutions encompassed strict rules-based, manual classifications, and rigid policies that struggled to rise to enterprises' evolving needs. Innovative artificial intelligence (AI) techniques are delivering unmatched speed and accuracy. Below you can find 5 comparisons between newer and legacy platforms.

Intelligent Data Classification

Legacy DSPM systems typically rely on rigid human-managed pattern matching such as regular expressions (RegEx) to classify data. While RegEx is a useful technique to label data, it is prone to false positives. RegEx requires a manual validation and tuning process, significantly slowing down the classification. Even when manually tuned, RegEx is prone to noisy false positives resulting in outputs that cannot be trusted. For example, legacy tools often mistake any 9-digit number for a confidential social security number (SSN).

Modern DSPM systems, powered by AI and machine learning (ML), can analyze complex data patterns and context to classify information more accurately. For example, Named Entity Recognition (NER) can identify which 9-digit numbers are SSNs, Tax IDs, Bank Routing Numbers, and which are less-sensitive 9-digit employee IDs. Modern DSPM systems deliver enhanced accuracy and efficiency in data classification, lowering operational costs and increasing your organization’s trust in the tool’s output.

File Level Classifications

Legacy DSPM tools cannot classify files and identify the business context. File-level context is critical for implementing robust data protection and records management processes. For example, a physician’s name found in a patient’s diagnostic report should be treated with a higher level of confidentiality when compared to a physician's name found in a healthcare marketing pamphlet.

Modern DSPMs have incorporated artificial intelligence techniques to accurately identify the file’s classification, such as meeting minutes, diagnostic reports, resumes, and employee pay stubs. This allows your organization to apply more granular and contextually appropriate security measures, ensuring that sensitive information is handled and protected.

Learned Data Environment

Most legacy DSPM systems take a one-size-fits-all approach to classification. RegEx must be written for each specific customer use case, significantly slowing down implementation. Additionally, patterns must be kept up-to-date as the organization’s data evolves.

Modern solutions use AI techniques to deeply understand each organization's unique and dynamic data landscape. For example, modern tools can automatically learn and classify data that is unique to your environment such as internal ID numbers and document types, all without manual tuning. Learned data classes ensure that all relevant data types are accounted for and properly secured.

Context-Aware Classification

Traditional DSPM systems handle data with the same classification in a uniform manner, without taking into account the context of their discovery. For example, a legacy tool might treat all health information the same, whether it pertains to customers or employees, or whether it is identifiable or anonymized.

Newer AI-powered platforms help security teams understand what their data represents and streamline response and remediation workflows. This allows more efficient and targeted data security efforts. For example, if a folder containing customer data suddenly becomes widely accessible to Human Resource employees who would not normally have access to customer data, modern DSPM will tag it as high-risk and trigger added controls. By identifying such contextual threats, these solutions proactively reduce risk before incidents occur.

A Clear And Concise Overview

To give a clear overview, we’ve created a table comparing key aspects of AI-powered and legacy DSPM systems: