Accenture Global Services Gmbh

Accenture Global Solutions Limited

Shimon Modi

Amin Hassanzadeh

Shaan Mulchandani

Walid Negm

Elvis Hovor

Shubhashis Sengupta

Roshni Ramesh Ramnani

Annervaz Karukapadath Mohamedrasheed

James J Solderitsch

Song Luo

Steven Tiell

Chetan R Kundavaram

Edy S Liongosari

Stephen A Schall

Systems, methods, and apparatus, including computer programs encoded on computer storage media, for obtaining, processing, and presenting data related to security events, and for implementing courses of action to protect assets in response to the security events. An event management module identifies malicious activity present on a first network domain and/or a second network domain based on received network domain activity. A threat intelligence module receives data identifying the malicious activity in first data constructs of a predefined data structure. The threat intelligence module obtains additional data related to the identified malicious activity and generates second data constructs that include enriched data regarding the malicious activity. The enriched data includes data describing a campaign in which at least a portion of the malicious activity is involved and one or more courses of action. A course of action module receives the second data constructs and implements a given course of action.

Connected security system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating structured data using data received from unstructured textual data sources. One of the methods includes receiving unstructured textual data, identifying one or more keywords in the unstructured textual data, determining one or more patterns included in the unstructured textual data using the identified keywords, identifying one or more intelligence types that correspond with the unstructured textual data using the determined patterns, and associating, for each of the identified intelligence types, a data subset from the unstructured textual data with the respective intelligence type.

Unstructured security threat information analysis

Systems, methods, and apparatus, including computer programs encoded on computer storage media, for facilitating communication in an industrial control network. A system includes an industrial control network, one or more controller devices, one or more emulators, and an encryption relay processor. Each controller device can be operable to control one or more operational devices connected to the industrial control network. Each emulator can be configured to communicate with a respective controller device, and each emulator can be configured to reference a respective profile that includes information about security capabilities of the respective controller device. The encryption relay processor can be operable to facilitate communication to and from each emulator over the industrial control network. The encryption relay processor can execute a cryptographic function for a communication between the emulator and a node on the industrial control network when the respective controller device is incapable of performing the cryptographic function.

Industrial security agent platform

Techniques are described for receiving data generated by multiple platforms of different types, and managing the data in multiple stages of a data lifecycle associated with a super-platform. An end-user (e.g., data discloser) may interact with multiple individual (e.g., siloed) platforms of different types. The individual platforms may generate data describing, and/or resulting from, these interactions with end-user(s). The data from the various individual platforms may be received, ingested, stored, analyzed, aggregated, and/or otherwise processed by a super-platform during various stages of a data lifecycle. In some implementations, the end-user, the super-platform, and/or the individual platform(s) may provide one or more restrictions on how the data may be handled in each of the stages of the data lifecycle.

Platform data lifecycle management

Techniques are described for aggregating data generated by multiple platforms of different types. A particular user (e.g., end-user) may interact with multiple individual (e.g., siloed) platforms of different types and/or that support different business purposes or industries. The individual platforms may generate data describing and/or resulting from these interactions. The data may be received, ingested, and processed by a super-platform. The super-platform may generate aggregate data by aggregating the data received from different individual platforms. Data aggregation may be performed on data that is generated by different individual platforms and that is associated with a particular user or multiple users. Aggregation may also be performed on data that is independent of any particular user, such as sensor data that describes an environment in proximity to the platform.

Platform data aggregation and semantic modeling

Techniques are described for receiving data generated by multiple platforms of different types, and determining recommendations for end-user(s) of the multiple platforms based on an analysis of the received data. An end-user may interact with multiple individual platforms of different types. The individual platforms may generate data describing, and/or resulting from, such interactions. The data may be received, ingested, stored, analyzed, and/or otherwise processed by a super-platform. The data may be aggregated and the data and/or aggregate data may be analyzed by a recommendation engine executing on the super-platform to determine one or more recommendations for a particular end-user based on an analysis of the data and/or aggregate data associated with that end-user. Such recommendation(s) may be provided to the end-user through an end-user interface and/or search engine provided by the super-platform, or through a third-party entity.

Recommendation engine for aggregated platform data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a network security threat response. A data structure that represents communication events between computing devices of two or more network domains is received. The data structure is analyzed and a threat scenario that is based on a chain of communication events that indicates a potential attack path is determined. The chain of communication events include a sequence of communication events between computing devices proceeding from an originating computing device to a destination computing device, wherein the originating computing device and the destination computing device exist on different network domains. Attack pattern data, for the threat scenario and from a threat intelligence data source, that is associated with communications between computing devices that occurred during one or more prior attacks is received. Based on the threat scenario and the attack pattern data, one or more courses of action for responding to the threat scenario is determined, and information associated with the one or more courses of action is provided.

Event correlation across heterogeneous operations

Systems, methods, and apparatus, including computer programs encoded on computer storage media, for facilitating secure communication. A system for facilitating secure communication includes an enterprise network, one or more operational technology networks, and a management server. Each of the operational technology networks can include one or more controller devices operable to control one or more operational devices, and can include a respective site security server and a respective security relay server. The security relay server can be operable to facilitate secure communication between controller devices of the operational technology network and its corresponding site security server. The management server can be a node on the enterprise network and can be operable to communicate with each site security server.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for analyzing data that includes security threat information. One of the methods includes identifying intelligence types that each categorizes a subset of data, associating, for each of the intelligence types, each of the subsets of data, which are categorized by the respective intelligence type, with the respective intelligence type, determining rules for a third party that each indicate that the third party should receive data associated with particular types of potential security threats and priority information for the data, determining, for each of the potential security threats indicated in the rules, a group of the subsets that include information associated with the respective potential security threat, assigning, for each subset in each of the groups, a priority to the respective subset using the priority information, and providing the determined subsets to the third party using the respective priorities.

Security threat information analysis

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correlating domain activity data. First domain activity data from a first network domain and second domain activity data from a second network domain is received. The first domain activity data and the second domain activity data is filtered to remove irrelevant activity data, based on a first set of profile data for devices in the first network domain and a second set of profile data for devices in the second network domain. Unfiltered first and second domain activity data is aggregated. Aggregated unfiltered first and second domain activity data is correlated to determine an attack path for an attack that occurs across the first network domain and the second network domain, based on attack signatures and profiles associated with previously identified attacks. A visualization of the attack path is generated.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for transforming representations of network activity data. A data structure that represents communication events between computing devices of one or more networks is received. The data structure is analyzed and a set of potential attack paths represented in the data structure is determined. A score is assigned to each potential attack path in the set of potential attack paths. Potential attack paths that have scores that do not meet a predetermined threshold are removed from the set of potential attack paths. Potential attack paths that remain in the set of potential attack paths are ranked, based on each score assigned to each potential attack path, and the data structure that includes a ranked set of potential attack paths is provided.

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Washington, DC, United States of America

Shimon Modi