Abstract: An example methodology includes, by a computing device, receiving information regarding a new application from another computing device and determining one or more relevant features from the information regarding the new application, the one or more relevant features influencing predictions of any potential performance issue and any potential security issue. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of any potential performance issue for the new application and a second prediction of any potential security issue for the new application based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.

Abstract: Methods, apparatus, and processor-readable storage media for artificial intelligence query response systems with anomaly detection are provided herein. An example computer-implemented method includes obtaining at least one user query; performing a comparison of the at least one user query to one or more previous user queries contained within at least portions of one or more data structures; performing, based on results of the comparison, anomaly detection analysis on the at least one user query by processing the at least one user query against the one or more previous user queries contained within the at least portions of the data structure(s) using one or more anomaly detection algorithms; and generating, based on results of the anomaly detection analysis, at least one response to the at least one user query by processing, using an artificial intelligence system, the at least one user query and context information related thereto.

Abstract: One example method includes automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model. For the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information. For the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data. For the classified data, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to generate masked data.

Abstract: A method comprises receiving a large language model request, analyzing the large language model request using one or more machine learning algorithms, and predicting, based at least in part on the analyzing: (i) a large language model of a plurality of large language models to process and to respond to the large language model request; and (ii) at least one database from which data is to be used to generate a prompt for the large language model. The method further comprises interfacing with the large language model and the at least one database to enable the large language model to process and to respond to the large language model request.

Abstract: Architectures and techniques are described that can automate container security elements in the context of applications being deployed on a container orchestration platform. Techniques detailed herein can serve to increase awareness of container product security in an automated manner, can automate processes on detecting security vulnerabilities and bringing down insecure workspaces, can automate processes for mitigating the security vulnerabilities, notifying, verifying, and bringing the associated containers back online.

Abstract: A method comprises identifying a request for data, and identifying one or more data elements that are responsive to the request for data. The one or more data elements are analyzed to classify whether the one or more data elements comprise personally identifiable information, wherein the analyzing is performed using one or more machine learning models. The method further comprises interfacing with one or more cloud platforms of a plurality of cloud platforms to transfer the one or more data elements that have been classified as comprising personally identifiable information to the one or more cloud platforms.

Abstract: A method comprises receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed. Using the one or more machine learning models, the at least one machine learning algorithm and the configuration of the one or more workspaces are predicted in response to the request. The one or more workspaces are configured based, at least in part, on the predicted configuration.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model, predicting, by the workspace size predicting engine, a size of a workspace that corresponds to the workspace provisioning request, receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, and predicting, by the datacenter host prediction engine, a datacenter and/or host that is able to support requirements of the workspace.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request including resource requirement information that specifies one or more features that are to be included when a workspace is provisioned. The one or more features include at least a machine learning (ML) model that is to be run in the workspace. The method also includes predicting, by the workspace size predicting engine, the one or more resources for provisioning the workspace that corresponds to the workspace provisioning request.

Abstract: One example method includes automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model. For the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information. For the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data. For the classified data, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to generate masked data.

Abstract: One example method includes pre-processing a dataset, wherein the dataset includes data and/or metadata that indicates a software configuration of an internet of things (IoT) device, and/or indicates a history of any performance issues and/or security issues experienced by the IoT device, after the dataset is pre-processed, providing the dataset as an input to a machine learning model, using the machine learning model to generate, based on the input, respective target variable value predictions for each target variable in a group of target variables, and a first one of the target variables corresponds to the software configuration, and a second one of the target variables corresponds to the history, and when the target variable value predictions indicate a potential security issue and/or a potential performance issue, with the IoT device, taking a remedial action to resolve the potential security issue and/or the potential performance issue.

Abstract: An example methodology includes, by a computing device, receiving information regarding a new application from another computing device and determining one or more relevant features from the information regarding the new application, the one or more relevant features influencing predictions of any potential performance issue and any potential security issue. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of any potential performance issue for the new application and a second prediction of any potential security issue for the new application based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a data access event, wherein the data access event relates to a data element and determining whether the data element is a personally identifiable information (PII) data element. The method also includes, responsive to a determination that the data element is a PII data element, by the computing device, predicting, using a machine learning (ML) model, a PII protection policy appropriate for the PII data element, and applying the PII protection policy to the PII data element. The method further includes, by the computing device, returning the data access event including the PII data element with the PII protection policy applied.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.

Abstract: Architectures and techniques are described that can automate container security elements in the context of applications being deployed on a container orchestration platform. Techniques detailed herein can serve to increase awareness of container product security in an automated manner, can automate processes on detecting security vulnerabilities and bringing down insecure workspaces, can automate processes for mitigating the security vulnerabilities, notifying, verifying, and bringing the associated containers back online.

Abstract: A method comprises receiving event-based data, extracting one or more attributes from the event-based data, and analyzing the one or more attributes to classify whether the one or more attributes comprise personally identifiable information. The analyzing is performed using one or more machine learning models. The event-based data corresponds to one or more events where the one or more attributes are added to at least one of a database and an application.