In-Flight SAN Shield With AI-Generated Sector Encryption
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Solution Overview
Problem
Storage Area Networks (SANs) are susceptible to network snooping, exposing sensitive data due to uniform cryptographic logic across storage devices, and zoning during critical periods can be challenging, posing security concerns.
Innovation Solution
Implement Generative Artificial Intelligence (Gen AI) models to dynamically categorize data into confidentiality sectors based on sensitivity, generating unique cryptographic logic for each sector, and exchange smart contracts with embedded user authenticity to ensure secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform cryptographic logic is used across all storage devices, then implementation simplicity is maintained, but security against network snooping is weakened
Solution Approach 1:
The patent applies local quality by assigning different cryptographic logic to different storage devices based on their specific security requirements. Each storage device receives customized cryptographic parameters rather than uniform encryption, allowing security to be tailored to individual device risk profiles and data sensitivity levels while maintaining overall system coherence
Solution Approach 2:
The patent segments the cryptographic protection approach by dividing storage devices into different security zones or groups. Each segment receives appropriate cryptographic logic based on its security classification, enabling differentiated security protection across the storage network while maintaining manageable complexity through structured segmentation
2Reliability
If zoning is implemented to protect sensitive data, then data security is improved, but system complexity and operational difficulty increase during critical periods
Solution Approach 1:
The patent implements self-service by enabling storage devices to automatically receive and apply appropriate cryptographic logic without manual zoning configuration. The system autonomously manages security assignments, eliminating the need for administrators to manually create and maintain security zones, thereby reducing operational complexity while maintaining strong security protections
Solution Approach 2:
The patent applies preliminary action by pre-configuring cryptographic logic and security parameters before data transmission or storage operations begin. Security measures are established in advance through automated policies rather than requiring real-time zoning decisions during critical operations, reducing operational burden while ensuring security is always in place
3Reliability
If encryption is applied to all data, then confidentiality is maximized, but data transfer performance and processing speed decrease
Solution Approach 1:
The patent applies local quality by selectively applying cryptographic logic only to specific storage devices handling sensitive data, rather than encrypting all data uniformly. This targeted approach maintains high confidentiality for sensitive information while avoiding unnecessary encryption overhead for non-sensitive data transfers, thereby preserving overall system productivity
Solution Approach 2:
The patent implements partial action by applying encryption selectively based on data sensitivity classification and device security requirements. Rather than encrypting every data block regardless of content, the system applies cryptographic protection only where necessary, reducing processing overhead and maintaining optimal data transfer performance for non-sensitive operations while still protecting confidential information
Data Source
AI summary
Data blocks that are being transmitted via a Storage Area Network are provided inflight security, in the form of cryptography that is dynamically generated and applied while the data is being transferred to the SAN. The complexity/type of cryptography that is generated and applied is based, at least, on the level of confidentiality/sensitivity of individual data elements in a data block. Gen AI models are implemented that have been trained to identify confidential data elements and, in response, categorize the confidential data elements into confidentiality sectors that are based on the level of transmission security required. Once the confidential data elements have been categorized/sectored, the Gen AI generates (i) cryptographic logic for each confidentiality sector, which is subsequently applied and (ii) a smart contract that includes the generated cryptographic logic and is exchanged between the source and destination, which is enabled through authenticity of user information embedded in the smart contract.