International Business Machines Corporation

Kyndryl, Inc.

International Business Machines Corpora ! Ion

Vugranam Chakravarthy Sreedhar

Sai Zeng

Milton H Hernandez

Jinho Hwang

Muhammed Fatih Bulut

Yu Zhang

Mark N Wegman

Lin Luo

Vadakkedathu Thomas Rajan

Tova Roth

Douglas Neil Kimelman

Shun Xiang Yang

Tara Lynn Astigarraga

Peng Ji

Saurabh Sinha

Jong-Deok Choi

Manish Satyapal Gupta

Anup Kalia

Mauricio J Serrano

Charanjit Singh Jutla

Constantin Mircea Adam

Brian L Peterson

Sreekrishnan Venkateswaran

Lisa M Chavez

Prema Vivekanandan

Samuel Pratt Midkiff

Nerla Jean-Louis

Virginia Mayo

Maja Vukovic

Mukesh Kumar Mohania

Yaoping Ruan

Alexei A Karve

Jim Alain Laredo

Lenin Mehedy

Amitkumar Manoharrao Paradkar

Shu Tao

Daniel Brand

Karin Murthy

Richard Baxter Hull

Linh Hue Lam

Michael George Burke

Neeraj Asthana

Kenneth David Christiance

Jun Duan

Emi K Olsson

Cindy J Mullen

Kevin Wahlmeier

Paul G Easton

Supreet R Mandala

Marcio Buss

Michael Shane Goode

John Jeffrey Dillon

Xinli Wang

Aaron T Corette

An example operation may include one or more of capturing a current version of sensitive data by a data processor node, hashing, by the data processor node, the current version of the sensitive data, storing, by the data processor node, a hash of the current version of the sensitive data on a first blockchain, encrypting, by the data processor node, the current version of the sensitive data using a secret key, and storing the encrypted current version of the sensitive data on a second blockchain.

Protecting sensitive data

Systems, computer-implemented methods and/or computer program products that facilitate generating code for template specification in a quote to cash (Q2C) solution are provided. In one embodiment, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, a data model by specifying from a universal template which fields and attributes to include in the data model; mapping, by the system, external data into the data model; and generating, by the system, a code for template specification based on the data model, one or more business rules and a first code template.

Generating a quote to cash solution

An assessment component that facilitates assessment and enforcement of policies within a computer environment can comprise a compliance component that determines whether a policy, that defines one or more requirements associated with usage of one or more enterprise components of an enterprise computing system, is in compliance with a plurality of standardized policies that govern operation of the one or more enterprise components of the enterprise computing system. The assessment component can also comprise a policy optimization component that determines one or more changes to the policy that achieve the compliance with the plurality of standardized polices based on a determination that the policy complies with a first standardized policy of the plurality of standardized policies and fails to comply with a second standardized policy of the plurality of standardized policies.

Computer environment compliance

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

Techniques for secure electronic collaboration among parties to provide dynamic cyber insurance are disclosed. A profile describing information technology (IT) infrastructure for an entity is retrieved from an electronic distributed ledger. At least a portion of the profile is provided to a risk evaluator using the electronic distributed ledger. A cyber security risk evaluation associated with the profile is retrieved from the electronic distributed ledger. A request for cyber insurance from the entity is received. Cyber insurance parameters for the entity are determined automatically, using a computer processor, and based on the cyber security risk evaluation. The entity acquires cyber insurance based on the determined cyber insurance parameters.

Dynamic cyber insurance using a distributed ledger

A system includes a memory that stores computer-executable components and a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components include a deployment generator component that analyzes current component versions of application services and determines differences with previous deployment versions of the application services deployed to a target computing environment. A service deployment output component generates instructions to selectively update the application services to the target computing environment based on the determined differences between the previous deployment versions and the current component versions of the application services.

Model-based differencing to selectively generate and deploy images in a target computing environment

An example operation may include one or more of connecting, by a disposition node, to a blockchain comprised of a plurality of user nodes connected to a plurality of device nodes that store user data of the plurality of the user nodes, receiving, by the disposition node, a request from a user node of the plurality of the user nodes to dispose of user data (D) on at least one of the device nodes of the plurality of the device nodes, the request contains a disposal policy (P) and a disposal method (M) of the D, executing, by the disposition node, a consensus algorithm to validate the request based on the D, P and M, in response to a validation of the request, accessing, by the disposition node, the D on the at least one of the device nodes of the plurality of the device nodes, generating, by the disposition node, a location sensitive hash of the D (LSH(D)) and a crypto hash of the D (SHA256(D)), storing, by the disposition node, the LSH(D), the SHA256(D), the P and the M on the blockchain, executing, by the disposition node, a smart contract to dispose of the D on the at least one of the device nodes of the plurality of the device nodes by the M based on the P, and recording an execution of the smart contract on the blockchain.

Defensible disposition of data

A contract generation system that automatically generates cost models for new business contracts is provided. The system receives a new contract comprising a set of key-value pairs, each key-value pair comprising a question and an answer that correspond to a field of information in a document for the new contract. The system computes a similarity measure between the new contract and an existing contract based on a set of variance definitions for determining differences between the set of key-value pairs of the new contract and a corresponding set of key-value pairs of the existing contract. The system generates a cost model of the new contract based on the similarity measure and a cost model of the existing contract.

Creating cost models using standard templates and key-value pair differential analysis

Techniques facilitating service management for the infrastructure of blockchain networks are provided. A system comprises a memory and a processor that executes computer executable components stored in the memory. The computer executable components can comprise an allocation component, a grouping component, and an implementation component. The allocation component can assign, within a blockchain network, a first group of nodes of a first node type to a first set of operation slots and a second group of nodes of a second node type, different than the first node type, to a second set of operation slots. The grouping component can aggregate the second group of nodes assigned to the second set of operation slots with the first group of nodes within the first set of operation slots. The implementation component can execute a service management operation. A consensus algorithm can be satisfied during an execution of the service management operation.

Service management for the infrastructure of blockchain networks

A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.

Method and system to manage risk of vulnerabilities and corresponding change actions to address malware threats

Techniques that facilitate group patching recommendation and/or remediation with risk assessment are provided. In one example, a system includes a vertical stack component, a horizontal stack component and a risk classification component. The vertical stack component identifies a first patch profile from a software system associated with a computer system environment. The horizontal stack component identifies a second patch profile from a hardware system associated with network nodes of the computer system environment. The system learns over time to identify repetitive patterns using machine learning techniques. Then, the risk classification component performs a machine learning process to determine a risk classification for the computer system environment based on the first patch profile and the second patch profile.

Group patching recommendation and/or remediation with risk assessment

Aspects generate costs for a business plan from normalized cost metric values by mapping (via a processor implementing an automated process) text artifacts of a business plan to different tags that each include a cost as a function of a time period for delivery of a service that is associated with a computer service element. Aspects also generate a summary view cost estimation as a function of the time period, costs of the mapped tags and a geographic region productive factor that is assigned to a region identified as providing delivery of the service that is associated with the computer service element. The summary view cost estimation estimates a total cost of delivering the computing services of the computer service element over time as a function of the normalized, mapped tag cost metrics.

Deriving comparable planned and actual service delivery cost metrics

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Yorktown Heights, NY, United States of America

Vugranam Chakravarthy Sreedhar