Zscaler, Inc.

Raimi Shah

John A Chanak

Shudong Zhou

William Fehring

Rex Shang

Howie Xu

Subramanian Srinivasan

Dianhuan Lin

Arvind Nadendla

Manikya Bardhan

Raj Krishna

Anjul Mishra

Loc Bui

Systems and methods for sing Large Language Models (LLMs) to generate an Artificial Intelligence (AI) report on security risk using the cybersecurity data include obtaining cybersecurity monitoring data for an organization where the cybersecurity monitoring data is from a plurality of sources including from cybersecurity monitoring of a plurality of users associated with the organization; inputting the cybersecurity monitoring data to a first Large Language Model (LLM) to generate an initial output for a security report; inputting the initial output to a second LLM for critiquing the initial output against a set of rules to check for predefined flaws and to check for what was done correctly to generate a critique; resolving the initial output and the critique to generate a final output; and providing the final output for the security report.

Generative AI report on security risk using LLMs

Systems and methods include obtaining log data for a plurality of users of an enterprise where the log data relates to usage of a plurality of applications by the plurality of users; determining i) app-segments that are groupings of application of the plurality of applications and ii) user-groups that are groupings of users of the plurality of users; and providing access policy of the plurality of applications based on the user-groups and the app-segments. The steps can further include monitoring the access policy over time based on ongoing log data, manual verification of the access policy, and incidents where users are prevented from accessing any application; and adjusting the determined based on the monitoring.

Generating zero-trust policy for application access using machine learning

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Austin, TX, United States of America

Raimi Shah