Abstract: Systems and methods of Exact Data Matching (EDM) for identifying related tokens in data content using structured signature data implemented in a cloud-based system receiving data sets and customer configuration from a customer, wherein the data sets include customer specific sensitive data from a structured data source with each token represented by a hash value and the customer configuration includes one or more primary keys for a plurality of records in the data sets; distributing the data sets and the customer configuration to a plurality of nodes in the cloud-based system; performing monitoring of content between a client of the customer and an external network; detecting a presence of a plurality of tokens associated with a record in the customer specific sensitive data based on the monitoring; and performing a policy-based action in the cloud-based system based on the detecting.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods include obtaining a file to be checked for Data Loss Prevention (DLP); determining a cryptographic hash of the file and comparing the cryptographic hash to corresponding cryptographic hashes of indexed files; responsive to a match between the cryptographic hash and one of the corresponding cryptographic hashes, determining a DLP match and performing an action based thereon; responsive to no match, extracting text from the file and creating an ordered sequence of hashes of variable length chunks of the extracted text; and determining the DLP match with one of the indexed files based on comparing the ordered sequence of hashes with corresponding ordered sequence of hashes of the indexed files.

Abstract: 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.

Abstract: Systems and methods include obtaining a file to be checked for Data Loss Prevention (DLP); determining a cryptographic hash of the file and comparing the cryptographic hash to corresponding cryptographic hashes of indexed files; responsive to a match between the cryptographic hash and one of the corresponding cryptographic hashes, determining a DLP match and performing an action based thereon; responsive to no match, extracting text from the file and creating an ordered sequence of hashes of variable length chunks of the extracted text; and determining the DLP match with one of the indexed files based on comparing the ordered sequence of hashes with corresponding ordered sequence of hashes of the indexed files.

Abstract: Systems and methods of Exact Data Matching (EDM) for identifying related tokens in data content using structured signature data implemented in a cloud-based system receiving data sets and customer configuration from a customer, wherein the data sets include customer specific sensitive data from a structured data source with each token represented by a hash value and the customer configuration includes one or more primary keys for a plurality of records in the data sets; distributing the data sets and the customer configuration to a plurality of nodes in the cloud-based system; performing monitoring of content between a client of the customer and an external network; detecting a presence of a plurality of tokens associated with a record in the customer specific sensitive data based on the monitoring; and performing a policy-based action in the cloud-based system based on the detecting.

Abstract: Systems and methods of Exact Data Matching (EDM) for identifying related tokens in data content using structured signature data implemented in a cloud-based system receiving data sets and customer configuration from a customer, wherein the data sets include customer specific sensitive data from a structured data source with each token represented by a hash value and the customer configuration includes one or more primary keys for a plurality of records in the data sets; distributing the data sets and the customer configuration to a plurality of nodes in the cloud-based system; performing monitoring of content between a client of the customer and an external network; detecting a presence of a plurality of tokens associated with a record in the customer specific sensitive data based on the monitoring; and performing a policy-based action in the cloud-based system based on the detecting.

Abstract: Systems and methods of Exact Data Matching (EDM) for identifying related tokens in data content using structured signature data implemented in a cloud-based system receiving data sets and customer configuration from a customer, wherein the data sets include customer specific sensitive data from a structured data source with each token represented by a hash value and the customer configuration includes one or more primary keys for a plurality of records in the data sets; distributing the data sets and the customer configuration to a plurality of nodes in the cloud-based system; performing monitoring of content between a client of the customer and an external network; detecting a presence of a plurality of tokens associated with a record in the customer specific sensitive data based on the monitoring; and performing a policy-based action in the cloud-based system based on the detecting.

Abstract: A cloud-based method, a behavioral analysis system, and a cloud-based security system can include a plurality of nodes communicatively coupled to one or more users, wherein the plurality of nodes each perform inline monitoring for one of the one or more users for security comprising malware detection and preclusion; and a behavioral analysis system communicatively coupled to the plurality of nodes, wherein the behavioral analysis system performs offline analysis for any suspicious content from the one or more users which is flagged by the plurality of nodes; wherein the plurality of nodes each comprise a set of known malware signatures for the inline monitoring that is periodically updated by the behavioral analysis system based on the offline analysis for the suspicious content.

Abstract: A cloud-based method, a behavioral analysis system, and a cloud-based security system can include a plurality of nodes communicatively coupled to one or more users, wherein the plurality of nodes each perform inline monitoring for one of the one or more users for security comprising malware detection and preclusion; and a behavioral analysis system communicatively coupled to the plurality of nodes, wherein the behavioral analysis system performs offline analysis for any suspicious content from the one or more users which is flagged by the plurality of nodes; wherein the plurality of nodes each comprise a set of known malware signatures for the inline monitoring that is periodically updated by the behavioral analysis system based on the offline analysis for the suspicious content.

Abstract: A cloud-based method, a behavioral analysis system, and a cloud-based security system can include a plurality of nodes communicatively coupled to one or more users, wherein the plurality of nodes each perform inline monitoring for one of the one or more users for security comprising malware detection and preclusion; and a behavioral analysis system communicatively coupled to the plurality of nodes, wherein the behavioral analysis system performs offline analysis for any suspicious content from the one or more users which is flagged by the plurality of nodes; wherein the plurality of nodes each comprise a set of known malware signatures for the inline monitoring that is periodically updated by the behavioral analysis system based on the offline analysis for the suspicious content.

Abstract: A cloud-based method, a behavioral analysis system, and a cloud-based security system can include a plurality of nodes communicatively coupled to one or more users, wherein the plurality of nodes each perform inline monitoring for one of the one or more users for security comprising malware detection and preclusion; and a behavioral analysis system communicatively coupled to the plurality of nodes, wherein the behavioral analysis system performs offline analysis for any suspicious content from the one or more users which is flagged by the plurality of nodes; wherein the plurality of nodes each comprise a set of known malware signatures for the inline monitoring that is periodically updated by the behavioral analysis system based on the offline analysis for the suspicious content.