Abstract: 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.

Abstract: Systems and techniques that facilitate compliance enforcement via service discovery analytics are provided. In various embodiments, a system can comprise a receiver component that can access one or more declarative deployment manifests associated with a computing application. In various instances, the system can comprise a dependency component that can build a dependency topology based on the one or more declarative deployment manifests. In various cases, the dependency topology can indicate dependencies among one or more computing objects that are declared by the one or more declarative deployment manifests. In various aspects, the system can comprise a compliance component that can determine, based on the dependency topology, whether the computing application satisfies one or more compliance standards.

Abstract: A method, system, and computer program product for software package update handling are provided. The method installs an initial set of software packages in a virtual environment. A package dependency graph is generated representing independent software packages and dependent software packages of the initial set of software packages. One or more software packages are updated with one or more updated software packages to generate a subsequent set of software packages. A compatibility check is performed on the subsequent set of software packages. The method generates an update prerequisite package based on the compatibility check.

Abstract: Techniques regarding pipeline security compliance are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a certification component that certifies a result from an execution of a pipeline for a computer application based on a comparison of an implemented security control profile associated with the pipeline and an applicable security control profile identified from a source artifact associated with the computer application.

Abstract: Techniques regarding managing one or more software application build processes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a control component that can execute a freeze algorithm that modifies an incorporation of a software artifact within a software application build set. The freeze algorithm can prevent implementation of a change to the software artifact by a version control program.

Abstract: Systems and techniques that facilitate compliance enforcement via service discovery analytics are provided. In various embodiments, a system can comprise a receiver component that can access one or more declarative deployment manifests associated with a computing application. In various instances, the system can comprise a dependency component that can build a dependency topology based on the one or more declarative deployment manifests. In various cases, the dependency topology can indicate dependencies among one or more computing objects that are declared by the one or more declarative deployment manifests. In various aspects, the system can comprise a compliance component that can determine, based on the dependency topology, whether the computing application satisfies one or more compliance standards.

Abstract: Embodiments relate to a computer system, computer program product, and computer-implemented method to train a machine learning (ML) model using artificial intelligence to learn an association between (regulatory) compliance requirements and features of micro-service training datasets. The trained ML model is leveraged to determine the compliance requirements of a micro-service requiring classification. In an exemplary embodiment, once the micro-service has been classified with respect to applicable compliance requirements, the classified micro-service may be used as an additional micro-service training dataset to further train the ML model and thereby improve its performance.

Abstract: An approach is disclosed that selects a current processing element from a set of processing elements included in a software pipeline. A selected input data to the current processing element was an output data from a previously executed processing element. The input data is verified by computing a current fingerprint of the selected input data and comparing the computed fingerprint to an expected fingerprint. The expected fingerprint was previously computed after the output data was generated by the previously executed processing element. In response to the comparing revealing that the current fingerprint fails to match the expected fingerprint, a verification error is indicated to a user of the process.

Abstract: A method provides for collecting data source images from multiple repositories. Application dependencies are discovered from the data source images. Status results are determined based on vulnerability and compliance scanning of all dependent sources for each data source image. The status results are aggregated across all data source images for each of the multiple repositories. Remediations are determined for violations indicated by the aggregated status results. Each of the remediations is aggregated and ordered to define a single global remediation solution.

Abstract: A computer implemented method for compliance profiling, the method comprising creating an application security profile indicating a set of permissions enabled for a corresponding application, associating one or more source files corresponding to the application to a running workload, executing the running workload, capturing a workload security profile with respect to one or more operations executed by the running workload, wherein the workload security profile indicates a set of permissions utilized by the running workload, comparing the workload security profile and the application security profile to identify one or more differences, and recommending a change to the application security profile according to the identified one or more differences.

Abstract: An approach for detecting non-compliant methodologies in a repository. The approach can generate an abstract model of an Infrastructure as Code (IaC) repository based on security requirements of an IaC. The approach can compare the abstract model to one or more validated abstract models associated with other repositories of a hybrid multi-cloud system. The approach can generate an alert notifying the IaC repository of one or more non-compliant methodology measures. The approach can send the alert to the IaC repository.

Abstract: 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.

Abstract: 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.

Abstract: A sandbox component, operatively coupled to a host and a guest container, the sandbox component securely extends systems data collection software with potentially untrusted third-party code. A secure environment is enabled where plugins will run inside a sidecar container that is separate from a guest container. A container consists of an entire runtime environment: an application, plus its dependencies, libraries and other binaries, and configuration files needed to run it, bundled into one package. A sidecar service is not necessarily part of the application but is connected to the guest container and follows the parent application. A sidecar is independent from its primary application in terms of runtime environment and programming language. The sidecar plugin will be given a sparse/limited set of privileges required to simply perform its intended function and the Linux kernel constructs will control data access and transfer.

Abstract: A computer system, computer program product, and computer-implemented method to identify one or more re-factoring operations directed at micro-service identification for source code. A genetic algorithm is leveraged to produce an offspring population of re-factoring operations from a parent set. The offspring population is subject to an assessment utilizing one or more objective measures. Responsive to the assessment, one or more identified re-factoring operations are selectively applied to the source code to produce one or more corresponding micro-service candidates.

Abstract: Embodiments relate to a system, program product, and method for evaluating and controlling configuration of a build manifest. An application build manifest is discovered and is subjected to parsing process in which one or more components that comprise the application are identified. The build manifest is monitored for changes to the identified components, and a change notification is generated in response to a change in an identified component. Each generated change notification is assigned a classification. The change notifications are applied selectively to update the manifest, wherein the selective update is based on the classification of the change notification.

Abstract: Techniques facilitating representing and analyzing cloud computing data as pseudo systems are provided. A system comprises a memory that stores, and a processor that executes, computer executable components. The computer executable components comprise a framework component and a generation component. The framework component can recreate a system state of a computing device as a pseudo system state for the computing device. The pseudo system state can be decoupled from an original operating state of the computing device and can comprise data abstracted from the original operating state. The data abstracted can mimic an operation of the computing device. The generation component can create the pseudo system state and can facilitate black-box execution of software over the pseudo system state. The black-box execution of software can comprise running applications in the pseudo system state as if the applications were executing in the original operating state of the computing device.

Abstract: An artificial intelligence (AI) platform to support a continuous integration and deployment pipeline for software development and operations (DevOps). One or more running processes are subject to monitoring to identify presence of vulnerabilities. An automated rebuild of the monitored processes is initiated, which includes constructing a map representing a relationship of test code elements corresponding to different portions of source code. The identified vulnerable source code reflected in a new container image is subject to an automatic verification to ascertain if the source code is covered by at least one of the represented test code elements. A risk assessment is employed as part of the verification. A new container image is selectively deployed responsive to the risk assessment.

Abstract: An artificial intelligence (AI) platform to support a continuous integration and deployment (CI/CD) pipeline for software development and operations (DevOps). One or more dependency graphs are generated based on application artifacts. A machine learning (ML) model is leveraged to capture a relationship between components in the dependency graph(s) and one or more pipeline artifacts. Responsive a change of an application artifact, the captured relationship is leveraged to identify an impact of the detected change on the pipeline artifact(s). The CI/CD pipeline is selectively optimized and executed based on the identified impact to improve the efficiency of the pipeline and the deployment time.

Abstract: Embodiments relate to a computer system, computer program product, and computer-implemented method to train a machine learning (ML) model using artificial intelligence to learn an association between (regulatory) compliance requirements and features of micro-service training datasets. The trained ML model is leveraged to determine the compliance requirements of a micro-service requiring classification. In an exemplary embodiment, once the micro-service has been classified with respect to applicable compliance requirements, the classified micro-service may be used as an additional micro-service training dataset to further train the ML model and thereby improve its performance.