International Business Machines Corporation

Globalfoundries Inc.

Lenovo (singapore) Pte. Ltd.

Joseph W Dain

Gregory Tad Kishi

Nilesh Prabhakar Bhosale

Stefan Lehmann

Osnat Shasha

Christopher Zaremba

Robert Beverley Basham

Theodore Timothy Harris, Jr

Raul Eduardo Saba

Sara Megan Coronado

Dan Melamed

Wayne A Sawdon

Matthew John Fairhurst

Deepavali M Bhagwat

Ralph Thomas Beeston

Scott A Brewer

Abhishek Jain

Nikhil Khandelwal

Anthony J Ciaravella

Gary Anna

Benjamin L Andrews

Renan J Ugalde Amezcua

Sandeep R Patil

Itzhack Goldberg

Shay Haim Akirav

Thomas W Bish

Gil E Paz

James Hewitt

Frank N Lee

Dean Hildebrand

Joseph M Swingler

Edward H W Lin

Shira Ben-Dor

Henry Zheng Liu

Daniel I Tan

Karla J Lehmann

Christopher J Vollmar

Oded Aviyam

Drew Olson

Scott Brewer

Daniel James Winarski

Evangelos Stavros Eleftheriou

Sarvesh S Patel

Jonathan Wayne Peake

Yariv Bachar

James Arthur Fisher

George G Zamora

Thomas D Weigold

Elena Drobchenko

Mudi M Fluman

Takeshi Ishimoto

Slavisa Sarafijanovic

Raymond Anthony James

Lukas Rupprecht

David Luciani

Harold Joseph Roberson, Ii

Oren Bar

Constantine Arnold

Jeffrey Wayne Pilch

Daniel De Souza Casali

Adriana Pellegrini Furnielis

Shawn C Andrews

Ron Riffe

Joel H Chen

Stan Li

Provided are a computer program product, system, and method for determining an encryption technique for a modified data object to backup. Deep data inspection is performed on an object using a natural language processing module to determine facets for the object. The facets provide facet values for instances of sensitive information in the object. The facet values for the object are processed to determine a cumulative facet value. A determination is made of an encryption technique comprising one of a plurality of different encryption techniques to use on the object based on the cumulative facet value. The determined encryption technique is used to encrypt the object to transfer to a backup storage.

Determining facet values for sensitive information in a data object to determine an encryption technique to encrypt the data object for backup

A method and/or system of managing metadata are disclosed that include connecting a source data storage system (DSS) that stores both data and metadata to a metadata management platform (MMP); scanning metadata records onto the MMP from the DSS; storing metadata attributes for at least one of the group consisting of directories and buckets on the DSS in a look-up table on the MMP; and adding updated metadata attributes to the look-up table on the MMP for each subsequent scan of the DSS.

Custom metadata tag inheritance based on a filesystem directory tree or object storage bucket

A method includes scanning, by a device of a plurality of devices, a directory of a file system. The directory includes a plurality of subdirectories, and each device of the plurality of devices corresponds to a different queue of a plurality of queues. The method also includes inserting, by the device, a plurality of scanning tasks into a subset of the plurality of queues in a round robin fashion. The plurality of scanning tasks correspond to the plurality of subdirectories and responding to a scanning task of the plurality of scanning tasks includes scanning a subdirectory of the plurality of subdirectories.

Parallel file system scans with multiple shared queues

Computer-implemented methods for management of data collection devices. Aspects include creating a cluster of data collection devices and a distributed meta-key manager for the cluster and providing an authentication key for each data collection device to access the distributed meta-key manager. Aspects also include collecting and storing data by one or more of the data collection devices and periodically perform a quorum check for each data collection device of the cluster. Aspects further include updating an operational mode of each data collection device based on the quorum check and offloading the stored data from a data collection device based on successful verification of the stored data and the operational mode of the data collection device.

Zero trust management for data collection devices

Systems and methods for management of event metadata. The methods may include maintaining a plurality of data storage systems in communication with an external metadata management system. The methods may also include operating the metadata management system to store tagged event metadata corresponding to the plurality of data storage systems. Event metadata corresponding to a data storage system includes information associated with at least one data operation event executed on data residing in that data storage system.

Methods and systems for event based tagging of metadata

Systems and methods for cognitive encryption of data are disclosed. The methods may include maintaining a plurality of data storage systems in communication with an external metadata management system, operating the metadata management system to store metadata corresponding to data residing on the plurality of data storage systems, identifying a candidate data set residing on at least one of the plurality of data storage systems on which at least one security action should be performed using information included in the metadata management system, and in response to identifying the candidate data set, identifying the at least one security action.

Methods and systems for encryption based on intelligent data classification

Provided are techniques for data search with automated selection of one or more AI inference models and inference label indexing. Metadata for a data object that is stored in a data source is retrieved. The metadata is matched to filtering criteria. The filtering criteria is used to identify an Artificial Intelligence (AI) inference model. The data object is sent to an inference engine for the AI inference model. An inference result is received from the inference engine. The inference result is stored in a database to associate the inference result with the data object. Then, in response to receiving a search request with one or more terms, the one or more terms are matched to the inference result in the database. and the data object and the inference result are returned.

Data search with automated selection of artificial intelligence inference models and inference label indexing

A computer-implemented method includes receiving custom metadata for several data items, the data items having system metadata associated therewith, selecting, based on the custom metadata, some of the data items to move between a primary storage system and a secondary storage system, and moving the selected data item(s) between the primary storage system and the secondary storage system. A computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method. A system includes a processor and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Event driven migration, recall, and data placement optimization based on custom metadata

A dataset formatted in a first format is generated, wherein the dataset formatted in the first format includes at least a plurality of data elements that comprise data. A selection is made of machine learning framework, wherein the machine learning framework operates on datasets formatted in a second format that is different from the first format. The dataset is converted from the first format to the second format and links are generated from the converted dataset formatted in the second format to the plurality of data elements of the dataset formatted in the first format to avoid creating additional copies of the plurality of data elements.

Generation of a dataset in the format of a machine learning framework

A computer-implemented method according to an aspect includes training a cognitive network, utilizing metadata associated with historic data threats, inputting metadata associated with a current data threat into the trained cognitive network, identifying, by the trained cognitive network, one or more stored instances of data determined to be vulnerable to the current data threat, and adjusting one or more security aspects of the one or more stored instances of data determined to be vulnerable to the current data threat.

Protecting data based on a sensitivity level for the data

A system, according to one embodiment, includes: a processor, as well as logic that is integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to: send, by the processor, point in time copies of data to an object storage system. A directive for manipulating the point in time copies of the data are also set to the object storage system by the processor. Moreover, the point in time copies of the data are manipulated by a storlet on the object storage system according to the directive.

Efficient management of point in time copies of data in object storage

A set of metafiles that stores at least metadata information and provenance information of an artificial intelligence (AI) process is generated, where the AI process is trained with a source data. The set of metafiles is accessed via an application programming interface (API) to the set of metafiles. In response to accessing the set of metafiles, the source data in the set of metafiles is transferred to a cache for processing by the AI process.

Generation of a metadata-driven artificial intelligence platform

Embodiments for storage resource utilization analytics using metadata tags by a processor. Storage capacity utilization in the plurality of heterogeneous storage systems may be determined using one or more events indexed into a centralized search index.

Storage resource utilization analytics in a heterogeneous storage system environment using metadata tags

A method includes generating a table in a relational database, the table comprising an empty column reserved for a custom tag. The column has a default column name. The method also includes, in response to an instruction to create the custom tag, generating a column mapping that maps a name of the custom tag to the column name. The column mapping is stored outside the relational database and is used by a parser to translate between the name of the custom tag and the column name.

Custom tag definition and translation in a database

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Vail, AZ, United States of America

Joseph W Dain