AI Compliance Training System Using Recurrent Neural Networks
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Solution Overview
Problem
Current methods for training employees on cybersecurity compliance protocols are inefficient and costly, as they often require on-site training, which is expensive and limited in scope, or off-site training that results in lost work time and additional expenses, while also struggling to determine the optimal training trajectory for each employee due to the complexity of compliance standards and overlapping protocols.
Innovation Solution
An AI-based system comprising an AI instruction module and an AI audit/evaluate module that uses a feedforward recurrent neural network model to determine an employee's training status, predict an optimal training trajectory, and assess compliance with standards like HIPAA, NIST, and ISO, by analyzing metrics such as educational background and job-specific certifications, and predicting the outcome of compliance audits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If on-site on the job training is provided, then training quality and relevance are improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates virtual copies of training scenarios, compliance requirements, and assessment environments through software simulations. These digital replicas allow employees to practice compliance procedures in realistic virtual settings without the costs associated with physical on-site training facilities, instructors, and materials.
Solution Approach 2:
The patent replaces the mechanical system of physical on-site training (instructors, classrooms, physical materials) with an automated software-based training system that uses AI algorithms, virtual simulations, and digital assessments to deliver training content and evaluate employee compliance knowledge.
2Ease of operation
If off-site training is provided, then training accessibility is improved, but lost work time and additional expenses increase
Solution Approach 1:
The patent implements a dynamic training system that adapts to employee schedules and learning paces. The software allows employees to access training modules at any time, progress at their own speed, and receive immediate feedback, eliminating the fixed schedules of traditional off-site training while maintaining accessibility.
Solution Approach 2:
The patent enables employees to self-manage their compliance training through an automated software platform that provides training content, tracks progress, and conducts assessments without requiring external instructors or organized training sessions, allowing employees to complete training during available work time rather than losing dedicated training time.
3Adaptability or versatility
If comprehensive compliance training is provided, then compliance coverage is improved, but training complexity and difficulty to assess increase
Solution Approach 1:
The patent divides comprehensive compliance training into modular segments organized by compliance domain, job role, and difficulty level. Each module covers specific compliance requirements and can be independently completed, allowing employees to progress through structured units while the system tracks overall compliance coverage across all segments.
Solution Approach 2:
The patent implements automated feedback mechanisms that provide employees with immediate results on compliance assessments, identify knowledge gaps, and recommend specific training modules for improvement. The system continuously monitors training progress and compliance status, providing real-time feedback to both employees and organizations without requiring manual assessment of complex compliance data.
Data Source
AI summary
In some embodiments a server having one or more processors and one or more non-transitory computer readable media storing instructions executable by one or more processors to perform operations, the server determines based on aggregated data, a plurality of metrics associated with an employee, and also determines a training status of an employee, and predicts by one or more artificial intelligence modules executed on the server an optimal training trajectory and the outcome of a compliance audit cycle, the server sends by the one more processors assignments and milestones associated with the optimal training trajectory to the employee, and to the employees' manager.