Automated Bot for Security Access Review Accuracy
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Solution Overview
Problem
Enterprise systems face challenges in efficiently and accurately reviewing and managing security access levels across multiple users, leading to persistent out-of-date access allocations and potential business losses due to the complexity of systems and manual auditing processes.
Innovation Solution
A system and method utilizing a trained bot with a machine learning algorithm to retrieve and analyze user profile information from relational databases, generating recommendations for revoking or approving security access authority, thereby improving the efficiency and accuracy of access reviews and modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual auditing processes are used to review security access levels, then human experts can make decisions based on their judgment, but the process becomes time-consuming, slow, and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical auditing process with an automated bot system that uses machine learning algorithms to retrieve, analyze, and evaluate user profile information from relational databases. This substitution eliminates human error and speed limitations while maintaining decision-making quality through trained AI models that can process access review data rapidly and accurately.
Solution Approach 2:
The bot system performs self-service by autonomously retrieving user profile information, analyzing access patterns, evaluating security requirements, and generating recommendations without requiring continuous human intervention. The system serves itself by automatically navigating the complex landscape of enterprise data to identify and assess security access issues.
2Reliability
If manual methods are used to review critical access in complex enterprise systems, then human judgment can be applied, but the complexity of systems makes thorough review difficult and leads to persistent out-of-date access allocations
Solution Approach 1:
The patent replaces manual review processes with an automated bot that can navigate and process complex enterprise systems. The machine learning algorithm systematically retrieves and analyzes user profile information across multiple tables in relational databases, evaluating access allocations against security requirements without being overwhelmed by system complexity.
Solution Approach 2:
The bot acts as an intermediary between the complex enterprise system and human experts. It retrieves and analyzes user profile information, evaluates access patterns, and generates recommendations that bridge the gap between system complexity and human decision-making capabilities, making the complex data accessible and actionable.
3Reliability
If periodic review of critical access is performed manually, then access can be audited, but the process is slow and errors can lead to high priority issues and business losses
Solution Approach 1:
The patent replaces slow manual periodic reviews with an automated bot system that continuously or frequently evaluates security access. The machine learning algorithm rapidly retrieves and analyzes user profile information, identifying and flagging potential security issues much faster than manual processes, thereby improving both speed and reliability of access control.
Solution Approach 2:
The bot system enables continuous or frequent evaluation of security access rather than relying on slow periodic manual reviews. The automated nature of the system allows for ongoing monitoring and assessment of access allocations, ensuring security issues are identified and addressed promptly without the delays inherent in manual periodic auditing.
Data Source
AI summary
The disclosed system and method provide a way to easily review, audit, and modify multiple users' security access authority. The disclosed system and method may include using a trained bot that is trained using a machine learning algorithm to retrieve user profile information from a complex network of tables stored in a relational database and to analyze the user profile information to generate a recommendation to revoke or approve at least one user's security access authority for at least one transaction type. In some embodiments, the disclosed system and method allow for a human expert to revoke or approve at least one user's security access authority for at least one transaction type based on the recommendation. In some embodiments, the disclosed system and method determine whether to revoke or approve at least one user's security access authority for at least one transaction type and may also revoke or approve the at least one user's security access authority based on the decision.