AIRBOX Secure AI Framework with Segregated, Reversible Session Tracking

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Solution Overview

Problem

Current Generative AI systems lack transparency, security, and accountability, leading to unreliable outputs, data privacy breaches, and legal and regulatory challenges due to unattributed and unregulated use of training data.

Innovation Solution

Implementing an AIRBOX ecosystem with a secure, time-stamped, permission-based, segregated, and reversible AI framework that ensures data privacy, accountability, and compliance through logging, analytics, and software updating, using a Universal Prompt Descriptor (UPD) to reconstruct sessions and maintain data ownership.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If Generative AI systems use large language models trained on public data lakes, then AI output capability is improved, but data security and privacy are worsened due to confidential customer data being churned back into updated LLMs

Engineering Contradiction:

ImproveAI output capabilityVSAvoiddata security breach

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent segments the AI system into separate data environments: a public data lake for general training and a private data lake for confidential customer data. This segmentation prevents confidential data from being ingested into the LLM while maintaining AI output capability through the private data lake's isolation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary layer (the private data lake with controlled access) between the public data lake and the LLM. This intermediary mediates data flow to allow AI processing while blocking harmful data backflow that would compromise security and privacy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If Generative AI systems operate without documentation and audit trails, then system complexity is reduced, but accountability and reliability are worsened due to inability to monitor behavior or provide audit trails

Engineering Contradiction:

Improvesystem documentation requirementsVSAvoidaccountability for AI output

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent implements preliminary actions by automatically logging and timestamping all AI processing activities before they occur. This includes recording data inputs, processing operations, and outputs in advance, creating an audit trail that ensures accountability without requiring complex post-event documentation systems.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent establishes feedback mechanisms through comprehensive logging systems that monitor AI behavior in real-time. These logs provide feedback on data flow, processing operations, and outputs, enabling accountability and reliability while maintaining manageable system complexity through automated tracking.

Inventive Principle:

Principle #23Feedback

3Speed

If AI systems process data without time-stamping and attribution, then processing speed is improved, but measurement precision and reliability are worsened due to inability to track when and where data was used

Engineering Contradiction:

Improvedata processing speedVSAvoiddata usage attribution

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The patent applies preliminary action by automatically timestamping and attributing data usage at the point of processing. This metadata is attached immediately to data flows, enabling precise measurement of when and where data was used without slowing down the processing speed, as the attribution occurs concurrently with processing rather than afterward.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If Generative AI systems allow unrestricted data access, then adaptability and versatility are improved, but security and reliability are worsened due to susceptibility to data errors and pollution from unknown sources

Engineering Contradiction:

Improvedata access flexibilityVSAvoiddata quality and accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent applies local quality by creating different data access environments with distinct quality characteristics. The public data lake provides broad access for versatility, while the private data lake provides controlled, verified access for reliability. Each environment has its own quality attributes, allowing the system to maintain both adaptability and reliability through localized data management.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250259069A1Systems and methods for secure, segregated reversible machine learning ai

Publication Date: 2025.08.14 FIRST DRAFT LAW INC

AI summary

A replicable and attributable AI framework ecosystem is presented. In embodiments, a set of processes, methods and apparatuses for secure, time-stamped, permission-based, segregated, reversible, private and community, machine learning artificial intelligence implementations, platforms and frameworks may be provided. In embodiments, a given process may include a series of discrete sessions, each labelled with a unique Universal Prompt Descriptor (UPD). The UPD allows each session to be reconstituted to its exact state at any subsequent time point. In embodiments, the AI framework ecosystem may be further configured to include timestamped logging, and, through AIRBOX analytics, determine authorship, and assign or limit responsibility.