Armorblox, Inc.

Juniper Networks, Inc.

Stackrox, Inc.

Dhananjay Sampath

Arjun Sambamoorthy

Prashanth Arun

Chetan Anand

Salil Kanetkar

Sanjay Singh

Anand Raghavan

Anand Deshpande

Robert Lyons

Ali Golshan

Sameer Bhalotra

Vishwanath Raman

A method includes: accessing a corpus of messages previously sent from a user account; correlating sequences of words, in the corpus of messages, with behavior signals; aggregating the behavior signals into a behavioral model representing combinations of behavior signals characteristic of behavior in messages sent from the user account; later, accessing a message outbound from the user account to a recipient account, the message including a document associated with a document tag; correlating sequences of words, in the message, with behavior signals; retrieving a data access policy including a threshold at which access to a document associated with the document tag is restricted; and in response to detecting a difference between the behavioral signals from the message and the behavioral model exceeding the threshold, restricting access, by the recipient account, to the document in the message.

Method for data protection across sharing platforms

A method includes: accessing a corpus of emails sent from a email account prior to the initial time period; correlating sequences of words, in the corpus of emails, with language signals; aggregating the language signals into a sender model that represents combinations of language signals characteristic of language in emails sent from the email account; later, accessing a email outbound from the email account and directed to a recipient; scanning the email for the set of language signals; correlating sequences of words in the email with language signals; calculating a similarity score for the email based on the subset of language signals detected in the email and the sender model; and, in response to the similarity score falling below a threshold similarity, flagging the email as suspicious and redirecting the email away from the recipient.

Method for electronic impersonation detection and remediation

A method includes: receiving selection of a document; correlating sequences of words, in the document, with a set of language signals; generating a set of document tags representing the set of language signals; and retrieving a first data access policy: associated with a particular document tag in the set of document tags; and including a set of identities permitted to access a document associated with the particular document tag; receiving selection of a recipient account of the document; and in response to detecting the set of identities excluding the recipient account, restricting access to the document by the recipient account.

A cloud security service provides network security. The cloud security service receives, via a computer network, an electronic message sent by a sending user of an enterprise to a receiving user. The cloud security service analyzes the electronic message using a machine-learned user model describing the sending user's electronic messages, the user model generated based at least in part on previous electronic messages sent by the sending user. The cloud security service determines, based on the analysis, that the electronic message violates a security policy of the enterprise. The cloud security service performs a security action based on the determination that the electronic message violates the security policy.

User model-based data loss prevention

A cloud security service receives an electronic message sent by a purported sending user to a receiving user. The cloud security service retrieves a user model and a user identity associated with the purported sending user, the user identity including a set of feature values describing messages from the purported sending user. The cloud security service applies the user model to the received electronic message to identify a set of feature values describing the electronic message. The cloud security service compares the set of feature values describing the electronic message to the set of feature values describing messages from the purported sending user included in the user identity. The cloud security service determines, based on the comparison, whether the received electronic message was sent by the purported sending user. The cloud security service performs a security action based on the determination.

Message validation using machine-learned user models

A server has a processor and a memory connected to the processor. The memory stores instructions executed by the processor to collect operating signals from machines, where each operating signal characterizes the state or a change in operational state of a designated application operating within a designated container, where the designated container is an isolated process in user space designated by an operating system kernel. The operating signals are compared to models that incorporate summarizations of data sets that represent observed benign operating signals and malicious operating signals for the designated application operating within the designated container. Risks associated with the operating signals are characterized.

System and method for providing security in a distributed computation system utilizing containers

A system is configured to receive traffic being transported via a network; obtain, as a result of receiving the traffic, content from one or more packets associated with the traffic; analyze the content to identify one or more attributes associated with the content, where the one or more attributes correspond to at least one of: a network address, information associated with an application with which the traffic is associated, information associated with message content, or information associated with software content; determining that at least one attribute, of the one or more attributes, matches an attribute, of a set of attributes that are stored within a memory, where the set of attributes corresponds to a set of categories of traffic; identify a category, of the set of categories, that corresponds to the attribute; associate the category and the traffic; and process the traffic based on the associated category.

Traffic classification and control on a network node

A system may identify one or more attributes associated with traffic. The system may then determine that at least one attribute, of the one or more attributes, matches an attribute of a set of attributes that correspond to a set of categories of traffic. Based on determining that the at least one attribute matches the attribute of the set of attributes, the system may identify a category, of the set of categories, that corresponds to the attribute. The system may associate the category with the traffic, and process the traffic based on the associated category.

Growing community of inventors

Sunnyvale, CA, United States of America

Dhananjay Sampath