International Business Machines Corporation

Kyndryl, Inc.

Jinho Hwang

Maja Vukovic

Muhammed Fatih Bulut

Larisa Shwartz

Anup Kalia

Sai Zeng

Michael Elton Nidd

John Joseph Rofrano

Jin Xiao

Nikolaos Anerousis

Kun Bai

Brian L Peterson

Shripad Nadgowda

Frank Bagehorn

Clifford A Pickover

Srinivasan Parthasarathy

Raghav Batta

Qing Wang

Ota Sandr

Altynbek Orumbayev

Vugranam Chakravarthy Sreedhar

Ali Kanso

Lisa M Chavez

Jakub Krchák

Constantin Mircea Adam

Milton H Hernandez

Christopher Charles Young

Chen Lin

Jill L Jermyn

Michal Mýlek

Michael S Gordon

Jie Ma

Ya Bin Dang

Frederick Y Wu

Shriram Rajagopalan

Rohit Madhukar Khandekar

Tomás Ondrej

Virginia Mayo

Virginia Mayo Policarpio

Valentina Salapura

Itzhack Goldberg

Hubertus Franke

Jose Eduardo Moreira

Harigovind Venkatraj Ramasamy

Roxana Monge Nunez

Rafal Bigaj

Pooja Aggarwal

Lijun Mei

Dennis Anthony Perpetua, Jr

Yunwu Huang

Amol Bhaskar Mahamuni

Ryan T Gordon

Marcos Vinicius L Paraiso

Vidhya Shankar Venkatesan

Dorothea W Wiesmann Rothuizen

Victoria Lee Guerra

Cindy J Mullen

In Kee Kim

Ahmed Amokrane

Michal Mylek

Malik Jackson

Li Feng Zhang

Rick A Hamilton, Ii

Seema Nagar

Komminist S Weldemariam

Ruchi Mahindru

Neil Sondhi

Minkyong Kim

Li Jun Mei

Ajay Gupta

Olympia Gluck

Alaa Salah Youssef

Hai Huang

Orna Raz

Gargi Banerjee Dasgupta

Senthil Kumar Kumarasamy Mani

Agueda Martinez Hernandez Magro

Karin Murthy

George E Stark

Harshit Kumar

Amar Prakash Azad

Christopher R Dotson

Qi Ming Teng

Daniel S Riley

Tengfei Ma

Michael Tacci

Prateeti Mohapatra

Frederick Y-f Wu

Ioana Giurgiu

Yelena Zilberstein

Byungchul Tak

Randall M George

John Robert Davis

Tonghoon Suk

Nikhil Verma

Zemei Zeng

Xinli Wang

Alexandre Francisco Da Silva

Rodrigo Otavio Castrillon

Hagen Völzer

Several aspects are provided for dynamically updating an alert-management system that uses a master ruleset to match alerts in a data processing system with automata for handling the alerts. A method comprises training a machine learning model to correlate the alerts with the automata using a training dataset comprising alerts which were successfully handled by the automata. The machine learning model is then applied to correlate unmatched alerts with the automata, wherein the unmatched alerts were not matched to the automata by the master ruleset. The method further comprises analyzing operation of the machine learning model in relation to correlation of the unmatched alerts to define a new ruleset for matching the unmatched alerts with the automata and outputting the new ruleset for auditing of each rule in the new ruleset. In response to approval of an audited rule, the audited rule is added to the master ruleset.

Alert management in data processing systems

A method, a computer system, and a computer program product for a shiftleft topology construction is provided. Embodiments of the present invention may include collecting datasets. Embodiments of the present invention may include extracting topological entities from the datasets. Embodiments of the present invention may include correlating a plurality of data from the topological entities. Embodiments of the present invention may include mapping the topological entities. Embodiments of the present invention may include marking entry points for a plurality of subgraphs of the topological entities. Embodiments of the present invention may include constructing a topology graph.

Shiftleft topology construction and information augmentation using machine learning

Systems and methods are provided that integrate a machine-learning model, and more specifically, utilizing a platform as a service (PaaS) cloud to predict probability of success for an operator in an environment. An embodiment comprises a system having: a processor that executes computer executable components stored in memory, trained machine-learning model that predicts probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud, and a deployment component that receives a first operator and a first namespace and employs the trained machine-learning model to predict success of deployment of the first operator in a first environment.

Machine-learning model to predict probability of success of an operator in a PaaS cloud environment

Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can dynamically determine one or more endpoints to fulfill a user request. Embodiments of the present invention can select the dynamically determined one or more endpoints as the one or more endpoints that fulfill the user request. Embodiments of the present invention can execute the selected one or more endpoints to fulfill the user request.

Just in time assembly of transactions

A method, a computer system, and a computer program product for security risk analysis is provided. Embodiments of the present invention may include collecting operational data. Embodiments of the present invention may include building pipelines. Embodiments of the present invention may include localizing security issues using the operational data on an unsupervised model. Embodiments of the present invention may include constructing a semantic graph using shift-left data. Embodiments of the present invention may include constructing a mapping between the operational data and the shift-left data. Embodiments of the present invention may include clustering collected datasets. Embodiments of the present invention may include creating an active learning cycle using ground truth.

Shift-left security risk analysis

According to an embodiment, a computer-implemented method can comprise: inspecting, using a processor, a set of container images respectively associated with pods; identifying, using the processor, a first subset of the pods that contain a vulnerability; classifying, using the processor, the first subset of the pods as primary-infected pods; generating, using the processor, a first list of namespaces in which the primary-infected pods are deployed within a network; checking, using the processor, network policies in connection with the first list of namespaces to determine secondary-suspect pods that have ability to communicate with the primary-infected pods; generating, using the processor, a list of secondary-suspect namespaces in which the secondary-suspect pods are deployed within the network; identifying, using the processor, one or more secondary-suspect pods that communicated with one or more primary-infected pods; and generating, using the processor, a list of secondary-infected pods.

Context based risk assessment of a computing resource vulnerability

Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Refinement of a predicted event based on explainability data

Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

Feedback loop learning between artificial intelligence systems

A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

Estimating performance and required resources from shift-left analysis

A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.

Automatic mapping of records without configuration information

Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.

Dynamic re-composition of patch groups using stream clustering

Systems, computer-implemented methods, and computer program products that can facilitate computing environment migration plan recommendation based on one or more latent entity computing property preferences are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment. The computer executable components can further comprise a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. In some embodiments, the recommendation component recommends discovered latent computing property preferences of the entity to construct the computing environment migration plan.

Latent computing property preference discovery and computing environment migration plan recommendation

A system, program product, and method for use with an artificial intelligence (AI) platform to dynamically amend a knowledge base responsive to query evaluating and processing. A received or detected query is subject to natural language processing to identify, annotate, and map one or more query tokens against a knowledge base. The query tokens are evaluated against the knowledge base to identify one or more query tokens absent from the knowledge base and leverage a neural network to predict a probability relationship between the query tokens absent from the knowledge base and one or more tokens populated in the knowledge base. The natural language (NL) query is translated to a structured query language (SQL) and the SQL query is executed and evaluated, and the knowledge base is selectively and dynamically amended subject to the SQL evaluation.

Automated validity evaluation for dynamic amendment

An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: detecting that the system is in an unhealthy state; determining a set of one or more possible actions to remedy the unhealthy state of the system; selecting at least one action of the set of one or more possible actions; and constructing a service request implementing the selected at least one action; wherein at least one of the detecting, determining, selecting, and constructing is based at least in part on applying a reinforcement learning algorithm.

Cognitive service request construction

Growing community of inventors

Ossining, NY, United States of America

Jinho Hwang