Check Point Software Technologies Ltd.

Checkpoint Mobile Security Ltd

Barak Kfir

Dan Karpati

Eliyahu Hanokh Sandler

Nofar Bardugo

Pavel Berengoltz

Tamir Zegman

Yotam Ahrak

Elena Root

Yuval Raban

Moisey Sidgiyayev

A computer device (referred to as a processing engine), system, and method are provided for refactoring an original security policy using an artificial intelligence (AI) engine including a large language model (LLM). The processing engine parses policy data converts the original security policy into a code representation and sends the converted code representation to the AI engine. The AI engine analyzes the original security policy by applying the LLM to the code representation and identifies policy insights that are sent to the processing engine.

Security policy refactoring using AI

A device, method, and system are provided for improving user interactions with large language models (LLMs) by enhancing user queries. User queries are enhanced by retrieving if/then pairs from a database that are related to the user query. The query is matched to related if statements stored in the database and the then statements of the matched if statements are included with the query to generate improved answers from the LLM.

Dynamic AI tester with feedback-driven learning

Methods and systems are provided for protecting DNS traffic locally on an electronic device (e.g., a smart phone) by capturing DNS traffic from network traffic transmitted from the device and ensuring the DNS traffic is routed to a trusted DNS server via a prescribed transmission protocol.

On-device protected DNS

Methods performed by a system on a computer device, such as a smart phone, i.e., locally, for protecting against network-based attacks. These methods inspect all traffic to every application and web browser on the device.

On-device network protection

Growing community of inventors

Giv′at-Shmuel, Israel

Barak Kfir