Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for extracting and tracing ridges at junctions, including confluences, corners, and terminals. For example, the disclosed systems generate, from a digital image, a pixel ridge using a distance transform and a depth-first traversal of pixels in the digital image. In some embodiments, the disclosed systems determine a junction within the pixel ridge by segmenting the pixel ridge into logical branches. In certain embodiments, the disclosed systems classify the junction as a confluence based on determining that the junction is made up of at least a threshold number of incident branches within the pixel ridge. In some embodiments, the disclosed systems reconstruct the confluence by detecting a region of deformity for the confluence and replacing pixels in the region of deformity with extrapolated pixels connecting incident branches.

Abstract: An information handling system includes a computer readable medium having an artificial intelligence (AI) application security model. The system further includes a processor operably coupled to the computer readable medium to access the AI application security model to gather business information for one or more applications, gather application vulnerability data, flag one or more at risk applications based on the vulnerability data, and assign a business vulnerability score for the one or more at risk application at least partially based on the business information for the one or more at risk application.

Abstract: A system controlling, in real time, user access to resources of an enterprise information technology (IT) infrastructure includes an activity detector configured to collect contextual data from data sources in response to a user attempting to access a resource of the enterprise IT infrastructure. A feature extraction engine coupled with the activity detector is configured to extract features from the contextual data collected by the activity detector. A behavior classifier coupled with the feature extraction engine is configured to implement a machine learning model that is trained to generate a role prediction by classifying behavior of the user based on input of the features. An access controller coupled with the behavior classifier is configured to assign a role priority to the user based on the prediction generated by the machine learning model, the role priority determining which resources of the enterprise IT infrastructure that the user is able to access.

Abstract: An information handling system includes a memory device to store code and a processor to execute code. The processor receives behavior information from a user environment, instantiates a machine learning model to evaluate the behavior information to determine if the behavior information matches authenticated behavior, and authenticates the user environment to utilize a protected element of the information handling system in response to determining that the behavior information matches the authenticated behavior.

Abstract: An information handling system instantiates a first machine learning model to provide a first description of a first content item, instantiates a second machine learning model to receive the first description and information related to a second content item and to provide a third description of based on the first content item and the information related to the second content item, determines whether the second description represents a malicious attempt to alter the first content item, and blocks editing of the first content item in response to determining that the second description represents the malicious attempt. The second content item is an edited version of the first content item.

Abstract: A system for countering ransomware attacks includes a honeypot deployment controller configured to deploy a honeypot within an enterprise environment. The honeypot is generated by a generative artificial intelligence (AI) model trained on a predetermined risk profile. The honeypot is generated to attract ransomware attackers. The system includes a ransomware threat analyzer communicatively coupled with the honeypot deployment controller. The ransomware threat analyzer is configured to respond to a ransomware attack by classifying the ransomware attack and generating a recommendation based on the classifying. The system includes a security management interface communicatively coupling the ransomware threat analyzer with a security management system for the enterprise environment. The security management interface is configured to convey the recommendation to the security management system.

Abstract: A method for managing a data protection system includes monitoring, by an interaction manager, a data protection workload executing on the data protection system, wherein the data protection system comprises a plurality of services, and wherein the data protection workload is executed by the plurality of services communicating with each other using application programming interface (API) calls, obtaining, based on the monitoring, a copy of the API calls, performing an embedding on the copy of the API calls to obtain API embeddings, applying the API embeddings to a failure prediction engine to obtain a failure prediction for the data protection workload, performing a remediation action on the API calls to remediate the data protection workload, and updating the failure prediction engine based on the remediation action.

Abstract: A system can receive a group of data packets originated by at least one edge device. The system can decrypt respective data packets of the group of data packets, to reconstruct a file that is represented by the group of data packets. The system can extract a contextual value of the file. The system can input the respective contextual values and a defined rulebook to a retrieval-augmented generation system, wherein the defined rulebook comprises rules applicable to traffic routing in a human-readable format, and wherein the retrieval-augmented generation system outputs a priority for transmission of the group of data packets. The system can transmit the group of data packets to a destination based on the priority and the defined rulebook.

Abstract: Techniques for granularly classifying and protecting data are disclosed. A service accesses data comprising a first data unit and a second data unit. Based on an analysis of the data, the service assigns the first data unit a first sensitivity level and the second data unit a second sensitivity level. The first sensitivity level is a relatively higher sensitivity level as compared to the second sensitivity level, resulting in the data, as a whole, comprising data with multiple disparate sensitivity levels. The service selects a first encryption algorithm for application against the first data unit and selects a second, different encryption algorithm for application against the second data unit. The service applies the first encryption algorithm to the first data unit and applies the second, different encryption algorithm against the second data unit, resulting in the data being subjected to multiple different encryption algorithms.

Abstract: A system can maintain a group of priority queues for messages, wherein respective existing messages are assigned to respective priority queues of the group of priority queues based on a first policy. The system can change from the first policy to a second policy with respect to the group of priority queues, wherein the changing is performed independently of disrupting delivery of the existing messages. The system can assign respective new messages to the respective priority queues based on the second policy. The system can, based on changing to the second policy, re-order the respective existing messages of the respective priority queues from respective rears of the respective priority queues to respective fronts of the respective priority queues. The system can deliver at least some of the respective existing messages and at least some of the respective new messages from the respective priority queues.

Abstract: Systems and methods for connecting a medical device to a wireless network are provided. The operating room may include an operating room communication device and/or a hub device configured to transmit wireless network information for connecting wirelessly with the wireless network. The medical device may connect with the wireless network based on the wireless network information.

Abstract: Methods, apparatus, and processor-readable storage media for automated virtual machine categorization using artificial intelligence techniques are provided herein. An example computer-implemented method includes categorizing one or more virtual machines associated with at least one computing environment by processing performance data corresponding to the one or more virtual machines using at least one neural network; generating at least one explanation of the categorization of the one or more virtual machines by processing one or more outputs of the at least one neural network and context-based information pertaining to the one or more virtual machines using one or more RAG techniques; and performing one or more automated actions related to one or more data backup operations within the at least one computing environment based at least in part on the at least one generated explanation.

Abstract: Metadata about files present at clients is collected. The metadata includes values for a set of features associated with the files. The values are normalized and centroids are calculated. For each file, a comparison is performed of a normalized value of a feature associated with a file against a respective centroid calculated for the feature. Based on the comparison, the file is or is not backed up. A machine learning model is applied to files that have been backed up to tier the backed up files.

Abstract: Values corresponding to a set of features associated with each file of a set of files are collected. The features include an access frequency, modification time, sensitivity level, retention priority, and file type. The values are normalized and centroids are calculated. Normalized values and corresponding centroids of features of the files are compared. The files are tiered based on the comparison.

Abstract: A method for managing a data protection system includes monitoring, by an interaction manager, a data protection workload executing on the data protection system, wherein the data protection system comprises a plurality of services, and wherein the data protection workload is executed by the plurality of services communicating with each other using application programming interface (API) calls, obtaining, based on the monitoring, a copy of the API calls, performing an embedding on the copy of the API calls to obtain API embeddings, applying the API embeddings to a failure prediction engine to obtain a failure prediction for the data protection workload, performing a remediation action on the API calls to remediate the data protection workload, and updating the failure prediction engine based on the remediation action.

Abstract: A method for managing a data protection system includes obtaining, by an interaction manager, a failure notification for a data protection workload executing on the data protection system, wherein the data protection system comprises a plurality of services, and wherein the data protection workload is executed by the plurality of services communicating with each other using application programming interface (API) calls, based on the failure notification, identifying a portion of the plurality of services used for the data protection workload, performing a recreation of the data protection workload to identify a failure point associated with the failure notification, and performing a remediation action of the data protection workload based on the recreation.

Abstract: A method for managing a data protection system includes monitoring, by an interaction manager, a data protection workload executing on the data protection system, wherein the data protection workload is executed by a plurality of services communicating with each other using application programming interface (API) calls, obtaining a copy of the API calls, detecting a failure associated with the data protection workload using the copy of the API calls, performing a root cause analysis on the failure using historical API calls of the data protection system and based on a random forest machine learning algorithm and a support vector machine algorithm, performing a remediation action based on the root cause analysis and using a failure prediction engine, and updating an action-reward associated with the remediation action and applying a reinforcement learning on the failure prediction engine based on the action-reward to obtain an updated failure prediction engine.

Abstract: The present disclosure relates to a system (300) for providing route guidance using augmented reality, the system includes a processor (502) to obtain a set of orientation parameters of a subject (304) based on one or more sensors accommodated in a computing device (302) associated with the subject and determine one or more motion parameters associated with the subject to reach the destination from a target location. The processor generates a set of arrows indicating at least one direction of the path from the target location of the subject to the destination, wherein when the set of orientation parameters deviates from the one or more motion parameters, the processor configured to calculate a degree of rotation to align the set of arrows indicating the at least one direction of the path along the desired direction to reach the destination to facilitate route guidance.

Abstract: A method for managing a data protection system includes obtaining, by an interaction manager, a historical dataset of a plurality of application programming interface (API) calls from the data protection system for a plurality of data protection workloads, wherein each of the plurality of data protection workloads is executed by the plurality of services communicating with each other using the API calls, in response to obtaining the historical datasets, identifying a portion of the plurality of services used for each of the plurality of data protection workloads, generating a set of synthetic workloads based on the historical dataset and based on the identifying, wherein the set of synthetic workloads are generated using generative adversarial networks (GANs), performing a simulation of the set of synthetic workloads to obtain traffic statistics, and performing a preventive action of the data protection system based on the simulation and using the traffic statistics.

Abstract: A method for managing a data protection system includes obtaining, by an interaction manager, a set of historical application programming interface (API) calls between a plurality of services for a data protection workload in the data protection system, wherein the data protection workload is executed by a plurality of services communicating with each other using the historical API calls, identifying a portion of the plurality of services used for the data protection workload, performing a subsystem health evaluation on each of the portion of the plurality of services based on the set of historical API calls to obtain a reliability score for each service, performing a system evaluation of the data protection system based on the reliability score of each of the portion of the plurality of services, and performing a preventive action on the data protection system based on the system evaluation.