Kyndryl, Inc.

International Business Machines Corporation

Joseph Reyes

Hamza Yaswi

Bernhard Julius Klingenberg

Andrew S Kronstadt

Marci Devorah Formato

Christopher C Bode

Naina Singh

Mark A Lindsay

Thaddious L Goodman, Jr

Venkidupathy Ramasamy

Daniela K M Trevisan

Technology for storing legal hold data in the cloud (for example, in containers), where the source of the data stored in the cloud is independent backup copy(ies) that are maintained by an enterprise subject to a legal hold order. Changing the legal hold data as it is stored in the set of backup copy(ies) in a manner will not change the legal hold data as it is stored in the cloud. Mechanism may also be provided for auditing chain of custody and authenticating parties who request legal hold data to be delivered from cloud storage.

Storing legal hold data in cloud data storage units

A method, system, and program product for controlling power associated with connectivity between devices is provided. The method includes scheduling a copy function associated with copying data from a production hardware device to a secure hardware device at a specified time period. A first hardware connection between the production hardware device and a production network associated with the production hardware device is disabled during the specified time period and a second hardware connection between the production hardware device and the secure hardware device is enabled. A subsequent copy function is enabled for copying the data from the production hardware device to the secure hardware device. The second hardware connection between the production hardware device and the secure hardware device is disabled after the copy function has completed. In response, the first hardware connection between the production hardware device and the production network is enabled.

Device connectivity power control

An AI firewall engine dynamically manages firewall ports of an enterprise network to increase security. The AI firewall engine may begin with a baseline port profile configuration and then add firewall rules derived from natural language processing and machine learning from vendor recommended port requirements for an application. The AI firewall engine builds a dynamic port profile with rules that are applied to the firewalls. The AI firewall engine may then monitor system changes and dynamically updates the port profile and configurations on the firewalls.

Dynamically managing firewall ports of an enterprise network

Provided is a method, computer program product, and system for performing automated failover and/or failback recovery analysis using predictive analytics. A processor may monitor a disaster recovery (DR) life cycle during a DR scenario. The processor may monitor failover process activities in a DR production environment over a predetermined time period. Based on data collected during monitoring of the DR life cycle and the failover process activities in the DR production environment over the predetermined time period, the processor may generate, using machine learning, a failback blueprint plan to move production to a new production environment.

Disaster recovery failback advisor

A user can wear a device which emits a visual and/or audible output. The output changes over time. A system is capable of predicting the output. Thus, the system can analyze a video and determine, based on observed output of the device, whether the video has been modified. The output can be particularly difficult for humans to modify, detect, understand, or recreate, further impeding attempts to disguise edits to the video.

Wearable watermarks

Systems, methods, and computer program products providing network security leveraging analytics and physical separation between computer systems and a network to prevent threats from infecting network devices. A specialized pluggable dongle like security device is inserted between ports of computer system(s) connecting to the network and port(s) of network hardware facilitating connections between the computer system and computer network. The security device uses a combination of onboard analytics and cloud-based analytic services to detect incoming threats from network traffic and whether to allow network traffic to pass through the security device and/or prevent network traffic from entering the computer system. In response to detected network threats, an out of band management network communicating with the security device can open or close a physical gate onboard the security device, which, when opened introduces an air gap between the network and computer system, preventing harmful network traffic from entering the computer system.

In-line cognitive network security plugin device

An approach is provided for providing disaster recovery, active/active and active/standby workload allocation in a networked computing environment according to aspects of the present invention. Risk factor data is obtained for each component of a plurality of components in a global data center. A resiliency score is calculated for each component of the plurality of components based on a set of risk factors that are applicable to the component gathered from the risk factor data. A set of group resiliency scores are computed by aggregating the resiliency scores for each of the plurality of components included in a component group. In response to a determination that an application's performance can be improved, a datacenter is selected for failover protection based on a group resiliency score corresponding to the datacenter. Moreover, the overall enterprise resiliency score can be improved by moving an application between sites in the enterprise.

Workload allocation utilizing real-time enterprise resiliency scoring

A machine-learning mechanism of a disaster-avoidance system trains a knowledgebase to associate characteristics of a data-center component with corresponding degrees of vulnerability to failure and with remedial steps that may be undertaken to avoid failure or to reduce adverse effects of a failure. This training is performed as a function of inferences derived from historical records and from extrinsic information sources. The historical records identify past failures of similar components, component characteristics associated with past failures, and results of remedial procedures undertaken in response to past failures or to previous occurrences of the characteristics. The extrinsic sources identify the current existence of external conditions known to be associated with past failures. When a component's total degree of vulnerability exceeds a predefined threshold value, the system assembles a subset of that component's remedial steps into a remedial procedure and directs downstream modules or administrators to implement the procedure.

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Pelham, NY, United States of America

Joseph Reyes