Palo Alto Networks, Inc.

Nandini Ramanan

William Redington Hewlett, Ii

Brody James Kutt

Min Du

Sheng Yang

Sujit Rokka Chhetri

Mrunmayi Bharat Nandgaonkar

Sreejith Rajkumar

Manish Mradul

Shan Huang

Anurag Mukund Phadke

An automated software-as-a-service (SaaS) security posture management (SSPM) system disclosed herein detects and maintains security posture for SaaS applications according to correct implementation of configuration settings. Based on detecting a previously unseen SaaS application with unknown implementation of configuration settings, the SSPM system scrapes the Internet for web content for the SaaS application and preprocesses/inputs the web content into a machine learning model to obtain predictions of correct/incorrect implementation of configuration settings as output. Based on the predictions not having sufficiently high confidence, the SSPM system obtains additional application content by logging into the SaaS application and scraping locally rendered pages therein. The application content is preprocessed/input to the machine learning model to obtain additional high confidence predictions.

Automated machine learning-based detection of correct implementations for configuration settings of software-as-a-service applications

The present application discloses a method, system, and computer system for classifying stream data at an edge device. The method includes obtaining a stream of a file at the edge device, aligning a predetermined amount of data in chunks associated with the stream of the file, processing a plurality of aligned chunks associated with the stream of the file using a machine learning model, and classifying, at the edge device, the file based at least in part on a classification of the plurality of aligned chunks.

Machine learning architecture for detecting malicious files using stream of data

Automated attribute scraping for security feature implementation with a single trained machine model across security features improves prediction quality and efficiency of predictions. A security feature implementation prediction system (system) generates search engine queries for each security feature based on high importance tokens for the security feature. The system ranks URLs returned from each search engine query for relevance, then preprocess and inputs content for top-ranked URLs into the trained machine learning models. The system identifies implemented security features output based on confidence values output by the trained machine learning model and identifies sentences that describe the implementations in corresponding content for top-ranked URLs.

Automated attribute scraping for security feature implementation prediction

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Sunnyvale, CA, United States of America

Nandini Ramanan