Evidentiary AI Architecture With Secure Enclaves and Temporal RAG
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Solution Overview
Problem
Existing artificial intelligence systems fail to provide a unified architecture that enables auditable, context-aware, temporally-accurate, and privacy-preserving data analysis, particularly in high-stakes, regulated environments, lacking integration of hardware-secured enclaves, versioned data repositories, and stateful management for forensic auditing.
Innovation Solution
A synergistic integration of hardware-secured enclaves, versioned data repositories, and a Retrieval-Augmented Generation (RAG) system with Temporal Block Sparse Attention (TBSA) mechanism for synchronic correlation and an immutable Evidentiary Analyze State, ensuring secure, accurate, and auditable analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are deployed in regulated industries for data analysis, then analytical capability is improved, but auditability and privacy preservation deteriorate
Solution Approach 1:
The system segments the LLM execution environment into isolated hardware-secured enclaves, separating the analytical processing from the external system. This segmentation allows the LLM to perform sophisticated analysis while the enclave's cryptographic boundaries ensure that internal states remain private yet auditable through controlled verification mechanisms.
Solution Approach 2:
Hardware-secured enclaves act as intermediaries between the LLM analytical engine and the external regulated environment. The enclave mediates by providing a trusted execution environment that preserves privacy internally while offering verifiable audit trails externally, thus reconciling the conflict between analytical versatility and auditability.
2Measurement precision
If versioned data repositories are implemented for historical analysis, then temporal accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-versioning data repositories with time-stamped snapshots before analysis occurs. This advance preparation enables precise temporal querying without adding complexity to the analysis engine itself, as the versioning infrastructure is already in place to handle historical data retrieval efficiently.
Solution Approach 2:
The patent adds a temporal dimension to data storage through versioning, transforming a static repository into a time-aware structure. This dimensional enhancement allows precise temporal accuracy without proportionally increasing complexity, as the versioning layer operates as an additional dimension rather than complicating the core analysis pathways.
3Reliability
If hardware-secured enclaves are used for secure processing, then privacy preservation is improved, but processing speed deteriorates
Solution Approach 1:
The system extracts only the essential cryptographic verification functions from the enclave to external validators, while keeping the computationally intensive LLM processing inside the secure enclave. This extraction minimizes the overhead impact on processing speed by reducing the frequency and complexity of enclave-to-external communications.
Solution Approach 2:
The patent optimizes the balance between privacy and speed by changing parameters such as the frequency of attestation requests, the granularity of state verification, and the use of incremental vs. full verification. These parameter adjustments allow the system to maintain strong privacy guarantees while adapting processing speed to operational requirements.
4Reliability
If comprehensive state management for forensic auditing is implemented, then auditability is improved, but computational overhead increases
Solution Approach 1:
The system implements partial state capture by recording only the essential elements needed for forensic auditing (prompts, responses, confidence scores, data lineage) rather than the complete internal state of the LLM. This selective partial action provides sufficient auditability while dramatically reducing the computational overhead of state management and storage.
Solution Approach 2:
The patent creates simplified cryptographic copies (hashes) of the LLM's internal state for auditing purposes, rather than managing and verifying the complete original state. These cryptographic copies provide tamper-evident audit trails with minimal computational overhead, as hashing is far less resource-intensive than full state verification.
Data Source
AI summary
A system and method for providing a unified, evidentiary artificial intelligence architecture integrates a local Large Language Model (LLM) executed within a hardware-secured enclave with a versioned data repository to perform synchronic, point-in-time correlation of data against historical operational rules. The LLM includes a novel Temporal Block Sparse Attention (TBSA) mechanism for computationally efficient analysis of long-context time-series data. The system captures a persistent, immutable ‘Evidentiary Analyze State’ using cryptographic hashing, creating a verifiable audit trail of the AI's reasoning process. A Retrieval-Augmented Generation (RAG) framework enables this correlation and drives a closed-loop proactive feedback mechanism, generating recommendations to update operational procedures based on real-time risk analysis. This unified architecture provides a specific technological improvement, yielding quantifiable gains in prediction accuracy and latency while ensuring privacy and auditability for high-stakes applications in domains such as finance, healthcare, and cybersecurity.