US 12,277,233 B1
Intelligent AI risk management framework
Sanjay Agrawal, Westford, MA (US); Anant Agrawal, Westford, MA (US); and Samarth Agrawal, Westford, MA (US)
Assigned to CIMCON Software, LLC, Westford, MA (US)
Filed by CIMCON Software, LLC, Westford, MA (US)
Filed on Jun. 27, 2024, as Appl. No. 18/757,198.
Claims priority of provisional application 63/614,478, filed on Dec. 22, 2023.
Int. Cl. G06F 21/00 (2013.01); G06F 21/57 (2013.01)
CPC G06F 21/577 (2013.01) [G06F 2221/033 (2013.01)] 42 Claims
OG exemplary drawing
 
1. A computer-implemented method, the method comprising:
accessing, by one or more computing devices, a repository of files stored on one or more computer-readable storage devices in accordance with a set of one or more scanning parameters, wherein at least one of the one or more scanning parameters is selected from a group consisting of target folders, target drives, file types, file age, file compression technology, scan depth, and keywords; and
analyzing, by the one or more computing devices, files stored in the repository to identify candidates that are potentially generated at least in part using one or more artificial intelligence (AI) processes, wherein analyzing the files stored in the repository further comprises:
identifying one or more attributes specific to the file type, wherein the one or more attributes include at least one of: a file extension associated with an AI tool, a reference to a generative AI tool, algorithm, keyword or folder associated with an AI tool, or outputs of an AI file; and
identifying one or more keywords within the files;
wherein each of the identified candidates includes at least one attribute among the identified attributes or at least one keyword among the identified keywords that is associated with AI usage;
evaluating associated input and output dependencies of each of the identified candidates, wherein the associated dependencies comprise one or more of libraries, files, or import modules;
generating a measure of risk for each of the identified candidates based on the corresponding attributes of the identified attributes or the corresponding keywords of the identified one or more keywords;
determining one or more subsets of the identified candidates based on the respective measure of risk; and
identifying or performing one or more automated tests or actions on each of the one or more subsets, wherein the one or more automated tests or actions comprises at least one of:
performing one or more tests to evaluate one or more AI models associated with the subset;
retaining the subset for further assessment;
deleting the subset from the repository;
moving the subset to a new location in the repository;
adding the subset to a file inventory;
starting a workflow using a pre-built workflow template;
generating a visual representation of the input and output dependencies for each of the identified candidates in the subset and displaying the visual representation on a graphical user interface, wherein the visual representation comprises an interconnected AI map of one or more objects representing a sequence of inputs to outputs in accordance with the dependencies at a specified-hierarchy level; or
grouping copies of particular files within the subset.