Bank of America Corporation

Jennifer Lynn Greenwald

Rajesh M Gopinathapai

David Pritchard

David Patrick Harte

Tinku Thomas

Aspects of the disclosure relate to identifying entitlement rules based on a frequent pattern tree. A computing platform may retrieve entitlement data associated with a relational database, where the entitlement data is indicative of user entitlements to computing resources in an enterprise network. Then, the computing platform may generate, for the entitlement data, a frequent pattern tree. Then, the computing platform may compare a pair of branches and may detect a pattern associated with a pair of entitlements. Then, the computing platform may determine, based on the frequent pattern tree, a frequency of occurrence of the pattern. Then, the computing platform may identify, based on the frequency of occurrence, a rule associated with the pattern. Subsequently, the computing platform may trigger, via the computing device and based on the rule, an action related to one or more of the entitlements of the pair of entitlements.

Identifying entitlement rules based on a frequent pattern tree

Methods, apparatuses, and systems are described for provisioning access rights in a computing system. A data structure may be created that corresponds to the access rights of a computing system. The data structure may be traversed to identify candidate bundles of access rights that correspond to patterns of access rights in the computing system. The candidate bundles of access rights may be evaluated to select one or more bundles to define as one or more roles in the computing system. The defined roles may then be provisioned to the users of the computing system as a replacement for the individual access rights. Various constraints may be applied to reduce the number of candidate bundles of access rights to evaluate.

Role discovery for identity and access management in a computing system

Artificial intelligence ('AI') systems and methods are provided for bundling software permissions across an enterprise organization. An AI engine may be deployed to maximize wellness of a software bundle. Maximizing the wellness may include adding a software permission to the bundle. Maximizing the wellness may include associating additional members with the bundle. Maximizing the wellness may include removing a software permission from the bundle. Maximizing the wellness may include disassociating a member from the bundle. The AI engine may streamline allocation of software permissions in an enterprise organization that would otherwise be unmanageable by human role engineers. For example, the AI engine may be deployed in enterprise organizations that utilize over 4,000 different software application across over 750,000 members. Utilizing the AI engine to streamline software permissions may limit exposure of the organization to an increased risk of a cyberattack on its information systems.

Artificial intelligence software access bundling

Embodiments of the present invention provide a system for assessing role defining parameters associated with access to resources in a network. The system is configured for extracting information associated with one or more resources, one or more users, and one or more authorizations, determining one or more metrics associated with one or more roles within an entity based on the extracted information, calculating a role defining parameter that defines a condition of the one or more roles based on the one or more metrics associated with the one or more roles, wherein the role defining parameter is associated with access to the one or more resources, determining if the parameter is below a predefined threshold, and performing one or more actions.

Growing community of inventors

Chicago, IL, United States of America

Jennifer Lynn Greenwald