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US 12,764,121 B2Granted
Presenting high-level descriptions of access privileges within an organization
AssigneeOracle International Corporation
InventorsKenneth Joseph Meltsner, Kent Arthur Spaulding
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Abstract
Techniques are described herein that provide high-level views of roles, access permissions, and responsibilities for individuals within an organization. Organizational data, including detailed, low-level information on access permissions and attributes of individuals, are used to generate human-comprehensible role names and descriptions that individuals may be classified into. A list of outlier individuals is determined and presented, where outlier individuals have access permissions that do not align with the responsibilities of their assigned role.
Claims20
A method comprising:
- retrieving a set of organizational data for an organization, the set of organizational data comprising user data for each individual within the organization, the user data comprising at least access permissions and attributes of the individuals;
- training and deploying a first machine learning (ML) model using the retrieved set of organizational data to output responsibilities of each individual within the organization, wherein the first ML model infers the responsibilities of each individual as a function of the user data of the individuals, wherein the responsibilities inferred by the first ML model represent tasks that each individual undertakes within the organization;
- based on the responsibilities output by the first ML model, training and deploying a second ML model to identify and classify individuals with similar responsibilities and establish a plurality of distinct roles within the organization associated with the individuals within the organization, wherein the second ML model is trained as a function of the responsibilities output by the first ML model;
- for each individual within the organization, comparing the access permissions of the individual with the roles and responsibilities of the individual to determine a list of one or more outlier individuals whose access permissions do not align with said roles and responsibilities;
- generating, using a generative artificial intelligence (AI) language model based on the responsibilities output by the first ML model and the plurality of distinct roles classified by the second ML model, human-comprehensible role descriptions and names for roles within the organization, wherein the generative AI model generates the human-comprehensible role descriptions within the organization based on access patterns associated with said roles and adapts the role descriptions responsive to learning new access patterns;
- and
- presenting the list of one or more outliers individuals, wherein presenting the list of one or more outlier individuals comprises generating an interface that identifies (a) the human-comprehensible role descriptions and role names output by the generative AI language model for each outlier individual, (b) names for each outlier individual, and (c) the access permissions that do not align with said roles and responsibilities.
4 more elements
A system comprising:
- at least one device including a hardware processor;
- the system being configured to perform operations comprising:
- retrieving a set of organizational data for an organization, the set of organizational data comprising user data for each individual within the organization, the user data comprising at least access permissions and attributes of the individuals;
- training and deploying a first machine learning (ML) model using the retrieved set of organizational data to output responsibilities of each individual within the organization, wherein the first ML model infers the responsibilities of each individual as a function of the user data of the individuals, wherein the responsibilities inferred by the first ML model represent tasks that each individual undertakes within the organization;
- based on the responsibilities output by the first ML model, training and deploying a second ML model to identify and classify individuals with similar responsibilities and establish a plurality of distinct roles within the organization associated with the individuals within the organization, wherein the second ML model is trained as a function of the responsibilities output by the first ML model;
- for each individual within the organization, comparing the access permissions of the individual with the roles and responsibilities of the individual to determine a list of one or more outlier individuals whose access permissions do not align with said roles and responsibilities;
- generating, using a generative artificial intelligence (AI) language model based on the responsibilities output by the first ML model and the plurality of distinct roles classified by the second ML model, human-comprehensible role descriptions and names for roles within the organization, wherein the generative AI model generates the human-comprehensible role descriptions within the organization based on access patterns associated with said roles and adapts the role descriptions responsive to learning new access patterns;
- and
- presenting the list of one or more outliers individuals, wherein presenting the list of one or more outlier individuals comprises generating an interface that identifies (a) the human-comprehensible role descriptions and role names output by the generative AI language model for each outlier individual, (b) names for each outlier individual, and (c) the access permissions that do not align with said roles and responsibilities.
6 more elements
A non-transitory computer-readable medium containing instructions comprising:
- retrieving a set of organizational data for an organization, the set of organizational data comprising user data for each individual within the organization, the user data comprising at least access permissions and attributes of the individuals;
- training and deploying a first machine learning (ML) model using the retrieved set of organizational data to output responsibilities of each individual within the organization, wherein the first ML model infers the responsibilities of each individual as a function of the user data of the individuals, wherein the responsibilities inferred by the first ML model represent tasks that each individual undertakes within the organization;
- based on the responsibilities output by the first ML model, training and deploying a second ML model to identify and classify individuals with similar responsibilities and establish a plurality of distinct roles within the organization associated with the individuals within the organization, wherein the second ML model is trained as a function of the responsibilities output by the first ML model;
- for each individual within the organization, comparing the access permissions of the individual with the roles and responsibilities of the individual to determine a list of one or more outlier individuals whose access permissions do not align with said roles and responsibilities;
- generating, using a generative artificial intelligence (AI) language model based on the responsibilities output by the first ML model and the plurality of distinct roles classified by the second ML model, human-comprehensible role descriptions and names for roles within the organization, wherein the generative AI model generates the human-comprehensible role descriptions within the organization based on access patterns associated with said roles and adapts the role descriptions responsive to learning new access patterns;
- and
- presenting the list of one or more outliers individuals, wherein presenting the list of one or more outlier individuals comprises generating an interface that identifies (a) the human-comprehensible role descriptions and role names output by the generative AI language model for each outlier individual, (b) names for each outlier individual, and (c) the access permissions that do not align with said roles and responsibilities.
4 more elements
Dependencies are inferred from the claim text.