International Business Machines Corporation

Malarvizhi Kandasamy

Sudheesh S Kairali

Mali Hansraj

Mohammad N Nazmi

Sunil Chelani

Sarbajit Kumar Rakshit

Binoy Thomas

Amitabha Biswas

James K Wong

A computer-implemented method performs dynamic filtering within a service mesh. While transferring data from one microservice to another microservice, each microservice evaluates a level of trust with each upstream and downstream microservice based on the level of trust score. Each microservice evaluates which data should be filtered while transferring data to downstream microservice and which data should be filtered while receiving data from upstream microservice.

Dynamic data filtering based on trust score within service mesh

A method, computer system, and a computer program product are provided for establishing security measures for a content. In one embodiment, the method comprises identifying at least a microservice chain in a network computer. Each microservice chain has more than one microservice linked to one another and each microservice includes a plurality of applications bundled together. Any use profiles associated with the microservice chain are identified and all data entry points into each microservice are determined. Each microservice is analyzed to predict when each microservice in the chain will provide a confidential output. Once any microservice chain is deemed to be providing a confidential output, any predicted exits in the microservice deemed to be generating an output content are upgraded so that all predicted exists provide a confidential output.

Providing dynamic network security based on importance of proprietary content

An example method for determining domain name system (DNS) forwarding rules is provided. The method includes training a machine learning model to generate recommended DNS forwarding rules and determine whether a DNS resolver is experiencing an DNS resolver issue. The method further includes receiving a DNS query at the DNS resolver which is one of a plurality of DNS resolvers forming a network of DNS resolvers. The method further includes identifying a chain of DNS resolvers of the network of DNS resolvers based at least in part on the DNS query and a DNS response to the DNS query. The method further includes analyzing the DNS query and the DNS response using the machine learning model to generate a DNS forwarding rule and to identify whether any of the DNS resolvers of the network of DNS resolvers is experiencing the DNS resolver issue.

Determining domain name system forwarding rules in a multi-cloud environment

Detecting resource errors in a heterogeneous computing environment is provided. A plurality of individual resource dependency graphs corresponding to a plurality of computing systems that comprise the heterogeneous computing environment is consolidated to form a consolidated resource dependency graph. An analysis of respective nodes representing respective resources of the heterogeneous computing environment in the consolidated resource dependency graph is performed to identify a resource error caused by a new resource being added to a computing system of the plurality of computing systems based on defined rule sets. It is determined whether the new resource causes an error to sibling resources at a same level under a parent resource in the consolidated resource dependency graph based on the analysis. In response to determining that the new resource does not cause an error to the sibling resources, the new resource is deployed in the computing system of the heterogeneous computing environment.

Detecting resource redundancy and conflicts in a heterogeneous computing environment

An approach is provided for changing a language for a replay of a session of a user. Request and response data in the session is captured. If a preferred language for the replay does not match an initial language of content presented to the user in the session, the captured data is translated into the preferred language and the content is presented in the preferred language and not in the initial language. If the preferred language matches the initial language, the content is presented in the initial language, without translating the captured data. An event in the captured data indicates a change in a displayed image. Responsive to searching Hypertext Markup Language (HTML) documents generated from a resource file, the HTML documents are determined to not include an HTML document that specifies the change. The HTML document is generated and employed in the replay of the session.

Changing a language for a user session replay

An approach is provided for changing a language for a replay of a session of a user. If a preferred language for the replay does not match an initial language of content presented to the user, data captured from requests and responses is translated from the initial into the preferred language, and the session replay presents the content in the preferred language; otherwise, the content is presented in the initial language. If a next event in the data indicates a change in an image displayed on a computing device utilized by the user during the session, a Hypertext Markup Language (HTML) document specifying the change is found in a search of HTML documents generated from a resource file from which image data for the session is reproduced, and the session replay employs the HTML document; otherwise, scripts that apply the next event to the HTML documents are generated.

An approach is provided for changing a language for a replay of a session of a user. Data from requests and responses of the session is captured. Based on the captured data, an initial language of content presented to the user in the session is determined. A selection by an analyst of a preferred language for the replay of the session is obtained. It is determined whether the preferred language matches the initial language. If the preferred language does not match the initial language, the captured data is translated from the initial language into the preferred language and the session is replayed by presenting the content in the preferred language and not in the initial language. The replayed session is viewed by the analyst. If the preferred language matches the initial language, the session is replayed by presenting the content in the initial language, without translating the captured data.

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Bengaluru, India

Malarvizhi Kandasamy