AI Intra-System Packet Authentication Under Dynamic System Load
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Solution Overview
Problem
Existing data security protocols fail to verify data exiting an enterprise system for malware or threats, and do not adapt to real-time system load demands, compromising efficiency and productivity.
Innovation Solution
Implementing generative AI algorithms and smart contract logic for intelligent intra-system authentication, using an AI engine to validate data packets against dynamic data path parameters, adapt authentication protocols based on real-time system resources, and perform multi-stage validations at various points within the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intra-system authentication protocols are implemented to verify data packets, then data security is improved, but system processing overhead and complexity increase
Solution Approach 1:
The authentication process is divided into multiple discrete stages: initial authentication at JVM initiation, intermediate authentication during data processing, and final authentication at data exit. Each stage performs specific verification tasks independently, making the complex authentication process manageable and maintainable while ensuring comprehensive security coverage.
Solution Approach 2:
Authentication checks are performed in advance at multiple points before data exits the system. The initial authentication at JVM startup validates the execution environment beforehand, and intermediate authentications verify data integrity during processing, preventing unauthorized modifications before final exit authentication.
2Reliability
If multi-stage authentication is performed at multiple system points, then authentication thoroughness is improved, but processing time and system productivity decrease
Solution Approach 1:
Authentication is performed periodically at predetermined intervals and system points rather than continuously. The three-stage authentication occurs at specific milestones (JVM initiation, data processing completion, data exit), providing thorough verification while allowing uninterrupted data flow between authentication points, thus maintaining productivity.
3Adaptability or versatility
If authentication protocols are dynamically adapted based on real-time system resources, then system adaptability is improved, but computational overhead and processing complexity increase
Solution Approach 1:
The authentication protocol dynamically adjusts its behavior based on real-time system resource conditions. When system resources are sufficient, more thorough authentication checks are performed. When resources are constrained, the protocol adapts by reducing verification intensity or skipping non-critical checks, maintaining security while preventing system overload.
Solution Approach 2:
The system continuously monitors resource utilization and feeds this information back to the authentication protocol engine. Based on this feedback, the protocol automatically adjusts authentication parameters such as verification depth, check frequency, and validation strictness, enabling adaptive security that responds to current system conditions without manual intervention.
Data Source
AI summary
Systems, methods, and apparatus are provided for intelligent intra-system data authentication. An AI engine, using generative AI algorithms, may screen network data and obtain telemetry and TCP data associated with a data packet. The AI engine may output a set of data path parameters associated with the data packet. The AI engine may adapt the data path parameters based on smart contract rules and other factors. The data packet may be processed using one or more intra-system applications. The AI engine may authenticate the data packet against the data path parameters in a multi-stage intra-system authentication. When one stage of authentication fails, the AI engine may restrict the data packet, preventing transmission past a point of restriction.