Zscaler, Inc.

Narinder Paul

Srikanth Devarajan

Amit Sinha

Kailash Kailash

Shashank Gupta

Howie Xu

Rex Shang

Arun Bhallamudi

Changsha Ma

Parnit Sainion

Deepen Desai

Balakrishna Bhat Bayar

Dianhuan Lin

Arcady V Schekochikhin

Douglas A Koch

James Tan

Huan Chen

Sonal Choudhary

Parth Shah

Shankar Vivekanandan

Pooja Deshmukh

Jay Chaudhry

Julien Sobrier

Michael Andrew William Sutton

Sivaprasad Udupa

Tarun Dewan

Sriram Natarajan

Karthikeyan Thamilarasu

Rakshitha Hedge

Santhosh Kumar

Nirmal Singh

Naveen Selvan

Pratibha Nayak

Uday Pratap Singh

Bharath Meesala

Bharath Kumar

Visvanathan Thothathri

Jose Raphel

Jeff Forristal

Vijay Bulusu

Naresh Kumar

Frank Zhang

Vairavan Subramanian

Ankit Kumar

Davinder Pal Singh

Rahul Kumar

Sakshi Agrawal

Sravani Manukonda

Balakrishna Bayar

Systems and methods of sandboxing a file include responsive to receiving a file associated with a user, obtaining policy for the user; analyzing the file with a machine learning model; and based on a combination of the policy for the user and a verdict of the machine learning model, one of quarantining the file for analysis in a sandbox and allowing the file to the user. The present disclosure presents a smart quarantine with a goal of minimizing the number of files quarantined, the number of malicious files passed through to an end user, and a number of files scanned by a sandbox.

Utilizing machine learning for smart quarantining of potentially malicious files

Systems and methods include, responsive to starting a plurality of listener modules, receiving a Uniform Resource Locator (URL) for a site on the Internet into a database; loading the URL; receiving artifacts based on the loading; using the plurality of listener modules to run rules based on the received artifacts; scoring the URL based on the rules and the received artifacts; and determining whether the URL is one of benign, suspicious, or malicious based on the scoring. The steps can include any of blocking the URL, allowing the URL, further analyzing the URL, adding the URL to a whitelist or blacklist, and providing a notification, based on whether the URL is benign, suspicious, or malicious.

URL risk analysis using heuristics and scanning

Cloud-based data loss prevention (DLP) systems and methods include monitoring a file to be checked for sensitive data from a user associated with a tenant; obtaining one or more dictionaries for the tenant; identifying a DLP match based on any of identifying exact document matches between the file and files in the one or more dictionaries, identifying same text in the file as in an indexed document in the one or more dictionaries, identifying content in the file that contains a subset of text in an indexed document in the one or more dictionaries, and identifying content that is similar but not exact as the text in an indexed document in the one or more dictionaries; and, responsive to the DLP match, blocking the file in the cloud-based system.

Data loss prevention via dual mode indexed document matching

A cloud node in a cloud-based system includes one or more processors and memory storing instructions that, when executed, cause the one or more processors to: communicate with a user associated with a tenant of a plurality of tenants; obtain policy and configuration for the user based on the tenant, from a central authority in the cloud-based system; provide the one or more cloud services to the user, based on the policy and configuration; and crawl one or more cloud providers having a plurality of files for the user, based on the policy and configuration. The cloud node is inline between a user device of the user and the Internet, as well as connected to the one or more cloud providers.

Cloud access security broker systems and methods via a distributed worker pool

Systems and methods include causing a scan by Cloud Access Security Broker (CASB) system of a plurality of users associated with a tenant in a Software-as-a-Service (SaaS) application where the scan includes any of identifying malware in content in the SaaS application and identifying confidential data in the content in the SaaS application; during the scan which is covering historical data in the SaaS application, receiving notifications of the content being actively modified by any of the plurality of users; and including the content being actively modified in the scan with the historical data. The systems and methods can further include maintaining geolocation of the any of the plurality of users; and causing the content being actively modified in the scan to be processed by the CASB system based on the geolocation.

Cloud access security broker systems and methods for active user identification and load balancing

Systems and methods include performing inline monitoring of production traffic between users, the Internet, and cloud services via a cloud-based system; utilizing a trained machine learning model to inspect static properties of files in the production traffic; and classifying the traffic as one of malicious or benign based on the trained machine learning model.

Utilizing machine learning to detect malicious executable files efficiently and effectively

Systems and methods include identifying a cloud application; performing one or more automated scripts to determine a first set of attributes of the cloud application; obtaining a second set of attributes of the cloud application based on a manual analysis; obtaining weighting factors for the first set of attributes and the second set of attributes; determining a risk score of the cloud application based on the first set of attributes and the second set of attributes and the associated weighting factors; and displaying the risk score of the cloud application. The steps can further include enforcing security policies for the cloud application based on the risk score, such as via one of a cloud-based system and a Cloud Access Security Broker (CASB) system.

Cloud access security broker determining risk score of cloud applications based on security attributes

Systems and methods include obtaining a profile for an application, wherein the profile includes one or more tenants, rules for use of the application by the one or more tenants, and users for the rules; monitoring a user of a tenant of the one or more tenants inline via a node in a cloud-based system; identifying an application of the one or more applications based on the monitoring and associated rules for the user; and enforcing the associated rules for the user for the application.

Granular SaaS tenant restriction systems and methods

Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.

Machine learning model abstraction layer for runtime efficiency

Cloud-based data loss prevention

Systems and methods for Data Loss Prevention (DLP) on images include detecting an image in monitored user traffic; scanning the image to identify any text and extracting any identified text therein; responsive to the extracting, scanning the extracted text with a plurality of DLP techniques including one or more DLP engines where the extracted text is checked to trigger the one or more DLP engines, Exact Data Matching (EDM) where the extracted text is matched to see if it matches specific content, and Indexed Data Matching (IDM) where the extracted text is matched to some part of a document from a repository of documents; and performing one or more actions based on results of the plurality of DLP techniques.

Data loss prevention on images

Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.

Utilizing Machine Learning to detect malicious executable files efficiently and effectively

Systems and methods include obtaining a file associated with a user for processing; utilizing a combination of policy for the user and machine learning to determine whether to i) quarantine the file and scan the file in a sandbox, ii) allow the file to the user and scan the file in the sandbox, and iii) allow the file to the user without the scan; responsive to the quarantine of the file and the sandbox determining the file is malicious, blocking the file; and, responsive to the quarantine of the file and the sandbox determining the file is benign, allowing the file.

Mobile device security, device management, and policy enforcement are described in a cloud-based system where the 'cloud' is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one or more mobile profiles for one or more mobile devices each associated with a user from an enterprise; responsive to enrollment of a mobile device of the one or more mobile devices, communicating to the mobile device; determining an associated mobile profile of the one or more mobile profiles for the mobile device; and configuring the mobile device based on the associated mobile profile.

Mobile device security, device management, and policy enforcement in a cloud-based system

A cloud-based security system includes a plurality of enforcement nodes connected to one another; a central authority connected to the plurality of enforcement nodes; and a Data Loss Prevention (DLP) service executed between the plurality of enforcement nodes, wherein the DLP service includes one or more DLP rules based on one or more DLP engines for a tenant, and wherein, for the DLP service, a first enforcement node is configured to monitor traffic of a user of the tenant, detect a DLP rule violation based on the one or more DLP rules, and forward DLP incident information to a second enforcement node, and the second enforcement node is configured to transmit the DLP incident information to a server for the tenant, including both DLP triggering content that cause the DLP rule violation and DLP scan metadata.

Data loss prevention incident forwarding

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Sunnyvale, CA, United States of America

Narinder Paul