CrowdStrike Accelerates Real-Time Data Classification with On-Device AI

Modern data security depends on understanding sensitive data as it is created, accessed, and moved in real time directly on the endpoint.

In addition to identifying predefined patterns such as credit card numbers or Social Security numbers, organizations must protect unstructured information including documents, chat logs, support tickets, AI prompts, medical records, and free-text fields. When protecting this data, understanding the meaning and context of the content is just as important as recognizing patterns.

"Hey, I set up the environment for you - the login is j.harrison and the passphrase we agreed on is Sunrise@2026"

There is no structured pattern to match; no username field, password label, or predefined format. A rule-based system would need to anticipate every possible way a person might share credentials in natural language, which would be impossible. Yet the meaning of this message is unambiguous: Someone is sharing account credentials in plain conversation.

Language models understand the meaning and context of content, enabling accurate classification across a broader range of sensitive data. However, delivering accurate AI-powered classification locally introduces a difficult challenge: balancing model efficacy and computational feasibility.

To address this challenge, CrowdStrike worked closely with Intel to introduce a new capability in CrowdStrike Falcon® Data Security that classifies sensitive data using language models that run on-device using dedicated hardware for AI. This is the first step in a broader strategy to extend our existing rule-based engines with AI-based classifications across a range of AI acceleration hardware, starting with Intel’s NPU.

The Challenge: Powerful AI Running on the Device