Abstract: A method, computer system, and computer program product are provided for performing automated eavesdropping detection and remediation. A distance between a first and second wireless client device is determined with reference to a wireless access point based on wireless signals transmitted by the devices. A first and second signal loss are determined with reference to the first and second wireless client devices and the wireless access point. Material properties for any physical structures between the first and second wireless client devices are derived based on the signal losses. A sound attenuation is determined between the first and second wireless client devices based on the material properties. It is determined whether a first user of the first wireless client device can hear audio of a second user of the second wireless client device based on the sound attenuation determined between the first wireless client device and the second wireless client device.

Abstract: Methods are provided for generating hierarchical summaries with actionable recommendations having various granularities. Specifically, the methods involve obtaining notifications related to network issues and generating meta-semantic data that includes a summary of each of the notifications. The methods further involve obtaining inventory data of network devices in a plurality of domains of a network. The inventory data includes configuration information of the network devices. The methods further involve generating a multi-level hierarchical summary specific to the network based on the inventory data and the meta-semantic data. The multi-level hierarchical summary includes a first level specific to one or more affected network devices and a second level specific to a group of network devices. The methods further involve providing the multi-level hierarchical summary for performing one or more actions associated with the network.

Abstract: A method comprises: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an AI model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

Abstract: A method for learning potential correlation of data structures and fields across multiple disparate data sources. The method automatically identifies relationships that exist in multiple data sources to facilitate a data broker that can return the “shortest-path-to-data”. The method includes communicating with a data lake that integrates access to data stored in a plurality of different data sources. The method next includes correlating, via the data lake, data fields in data sets across the plurality of different data sources to identify relationships across the plurality of different data sources. A request to access data is obtained, and the method determines that data for the request is stored in two or more data sources of the plurality of different data sources, selects a particular data source of the two or more data sources and retrieves the data for the request from the particular data source.

Abstract: This disclosure describes techniques for authenticating a user. For instance, a system may initially generate and then store authentication data for later authenticating the user. The authentication data may include biometrics data (e.g., facial recognition data), credentials data (e.g., a username, password, etc.), and/or environmental data (e.g., object(s) located within an environment). Later, the system may receive image data generated by a user device of the user. In some examples, the system may then analyze the image data using one or more facial recognition techniques in order to identify the user. The system may then use the authentication data, such as the environmental data, to determine that the image data further represents the object(s). Based on determining that the image data further represents the object(s), the system may then verify the user for access to a resource.

Abstract: A system and method for an artificial intelligence (AI) confidentiality proxy that can protect a network from the transferal of sensitive AI data sets to outside AI systems. A data contract is established between the proxy and outside AI systems could function as well as user personas for additional protection. This system can also integrate with the network and provide security policy content and inspection rules.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: Methods are provided for generating hierarchical summaries with actionable recommendations having various granularities. Specifically, the methods involve obtaining notifications related to network issues and generating meta-semantic data that includes a summary of each of the notifications. The methods further involve obtaining inventory data of network devices in a plurality of domains of a network. The inventory data includes configuration information of the network devices. The methods further involve generating a multi-level hierarchical summary specific to the network based on the inventory data and the meta-semantic data. The multi-level hierarchical summary includes a first level specific to one or more affected network devices and a second level specific to a group of network devices. The methods further involve providing the multi-level hierarchical summary for performing one or more actions associated with the network.

Abstract: This disclosure describes techniques for authenticating a user. For instance, a system may initially generate and then store authentication data for later authenticating the user. The authentication data may include biometrics data (e.g., facial recognition data), credentials data (e.g., a username, password, etc.), and/or environmental data (e.g., object(s) located within an environment). Later, the system may receive image data generated by a user device of the user. In some examples, the system may then analyze the image data using one or more facial recognition techniques in order to identify the user. The system may then use the authentication data, such as the environmental data, to determine that the image data further represents the object(s). Based on determining that the image data further represents the object(s), the system may then verify the user for access to a resource.

Abstract: In one embodiment, an illustrative method herein comprises: determining, by a process, for each group of policies configured across a plurality of network devices in a computer network, an information set having a list of all policy components used for each group and which policies within each group have which particular policy components of the list of all policy components used for that group; performing, by the process, a comparative analysis of similarity and component variance on policies within each group based on the information set; deriving, by the process, an overall complexity indicator for each group based on the comparative analysis; and providing, from the process to an assessment interface, a ranking of each group as compared to other groups of policies within the computer network based on their respective overall complexity indicator.

Abstract: This disclosure describes techniques for authenticating a user. For instance, a system may initially generate and then store authentication data for later authenticating the user. The authentication data may include biometrics data (e.g., facial recognition data), credentials data (e.g., a username, password, etc.), and/or environmental data (e.g., object(s) located within an environment). Later, the system may receive image data generated by a user device of the user. In some examples, the system may then analyze the image data using one or more facial recognition techniques in order to identify the user. The system may then use the authentication data, such as the environmental data, to determine that the image data further represents the object(s). Based on determining that the image data further represents the object(s), the system may then verify the user for access to a resource.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: Techniques and architecture for determining compliance of a network with respect to a network design solution. The techniques may include determining a role for each network device of a network that comprises multiple network devices and determining one or more policies related to each network device with respect to other network devices of the network. The techniques may further include based at least in part on the roles for each network device and the one or more policies, determining a level of compliance of a configuration of the network with respect to a network design solution. Based at least in part on the level of compliance, a remedial action may be performed. In configurations, a machine learning model may be used.

Abstract: In one example, a logical representation of a first graph is generated. The first graph indicates a configuration of a network device in a network at a first time. The first graph includes a first node representative of a first configuration block of the network device, a second node representative of a second configuration block of the network device, and a first link that indicates, by connecting the first node and the second node, that the first configuration block is associated with the second configuration block. The logical representation of the first graph is compared to a logical representation of a second graph that indicates an actual or planned configuration of the network device at a second time subsequent to the first time. In response, one or more changes in the configuration of the network device from the first time to the second time are identified.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: In one example, a logical representation of a first graph is generated. The first graph indicates a configuration of a network device in a network at a first time. The first graph includes a first node representative of a first configuration block of the network device, a second node representative of a second configuration block of the network device, and a first link that indicates, by connecting the first node and the second node, that the first configuration block is associated with the second configuration block. The logical representation of the first graph is compared to a logical representation of a second graph that indicates an actual or planned configuration of the network device at a second time subsequent to the first time. In response, one or more changes in the configuration of the network device from the first time to the second time are identified.

Abstract: Techniques and architecture for determining compliance of a network with respect to a network design solution. The techniques may include determining a role for each network device of a network that comprises multiple network devices and determining one or more policies related to each network device with respect to other network devices of the network. The techniques may further include based at least in part on the roles for each network device and the one or more policies, determining a level of compliance of a configuration of the network with respect to a network design solution. Based at least in part on the level of compliance, a remedial action may be performed. In configurations, a machine learning model may be used.