AI Security Escrow for Real-Time Third-Party Access Compliance
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Solution Overview
Problem
Large organizations face challenges in efficiently, effectively, and securely managing access to their computer systems, particularly when exchanging information with external systems, due to the complexity of their operations and the difficulty in identifying and preventing security issues before they occur.
Innovation Solution
A third-party security escrow system utilizing machine learning and artificial intelligence to dynamically update and enforce network security policies in real-time, ensuring compliance with enterprise standards, providing visual indicators of compliance, and enabling/disabling access through API interfaces based on dynamic pattern analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual security management methods are used to manage access to computer systems, then security policies can be enforced, but the system becomes difficult to manage efficiently and effectively due to complexity
Solution Approach 1:
The system enables automated self-service through AI-powered security agents that autonomously monitor, assess, and enforce security compliance across third-party systems. The agents continuously evaluate security controls, automatically update compliance status, and trigger access permissions without manual intervention, allowing the system to manage itself despite increasing complexity
Solution Approach 2:
The patent applies accelerated security assessment by using AI agents to continuously and rapidly evaluate security compliance in real-time. The system accelerates the traditional manual security audit process by automatically analyzing security controls, policies, and configurations across multiple third-party systems simultaneously, enabling fast detection and response to compliance issues
2Reliability
If manual monitoring of third-party security compliance is implemented, then security issues can be detected, but issues are difficult to identify and localize in real-time
Solution Approach 1:
The system implements continuous feedback loops where AI security agents monitor third-party systems, automatically report compliance status changes, and trigger immediate notifications to relevant stakeholders. The feedback mechanism provides real-time visibility into security compliance across the enterprise, enabling rapid identification and localization of issues as they occur
Solution Approach 2:
The patent replaces manual mechanical security monitoring with AI-powered automated agents that use machine learning to detect, analyze, and localize security compliance issues. The AI agents substitute human analysts by automatically evaluating security controls, interpreting compliance data, and pinpointing specific issues across complex third-party systems in real-time
3Productivity
If real-time automated security compliance monitoring is implemented using AI agents, then security issues can be identified quickly, but the system requires complex AI/ML infrastructure
Solution Approach 1:
The system segments the complex AI/ML monitoring infrastructure into modular, distributed security agents deployed across individual third-party systems. Each agent independently performs local security assessments and compliance evaluations, reducing the complexity burden on any single system while collectively providing enterprise-wide real-time monitoring capability
Solution Approach 2:
The patent introduces AI security agents as intermediary components between third-party systems and the central security management platform. These agents mediate by locally processing security compliance data, filtering and preprocessing information before transmission to central systems, thereby reducing the computational complexity and data transmission burden on the core AI/ML infrastructure
Data Source
AI summary
Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and/or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and/or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable/disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.