International Business Machines Corporation

Manus Kevin McHugh

Schuyler Bruce Matthews

Kirk Woods

Lisa Seacat DeLuca

Shikhar Kwatra

Barry Michael Graham

Sarika Budhiraja

Balakumaran Vaithyalingam

Lee Thomas

An embodiment includes receiving, at database connectivity (DBC) layer, a request to access data of a data source, wherein the data source is registered with a data catalog that includes data governance artifacts, and wherein the request is made via a connection that bypasses the data catalog. The embodiment also includes comparing, by a governance manager at the DBC layer, an access privilege level for a credential associated with the request to access requirements of requested data included in data protection rules of the governance artifacts. The embodiment also includes masking, by the governance manager, a portion of the data provided in response to the request, wherein the portion of the data has an access requirement that is not met by the access privilege level for the credential associated with the request.

Governed database connectivity (GDBC) through and around data catalog to registered data sources

A method, computer system, and a computer program product for deep tagging a recorded review session is provided. The present invention may include identifying a recording segment referencing an artifact in a recorded review session. The present invention may also include generating a bi-directional link between the identified recording segment and the referenced artifact.

Deep tagging artifact review session

In an approach to predicting software security exploits by monitoring software events, sets of design events are received from a compliance monitor, where the sets of design events are captured by the compliance monitor during execution of a software application. A pattern of the sets of design events performed by the software application is detected, where the pattern is a specific sequence of the sets of design events. The pattern of the sets of design events performed by the software application is compared to a database of learned patterns using a deep learning model. A security exploit is predicted based on the comparison of the pattern of the sets of design events performed by the software application to the learned patterns.

Predicting software security exploits by monitoring software events

In capturing software element state changes during software application runtime, a computing device executes a software application that a set of software sensors integrated within executable code of the software application. The software sensors correspond to software elements in the software application. During runtime, a given software sensor corresponding to a given software element continuously determines whether the given software element changes state. In response to a change in state, the given software sensor captures and stores the current state of the given software element. An application control module associated with the software application obtains state data stored by the software sensors. The application control module determines commands for modifying a behavior of the software application based on an analysis of the state data and executes the commands.

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Colorado Springs, CO, United States of America

Manus Kevin McHugh