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Chapters

• Tech Stack
• How It Works

Tech Stack

Proposed text stack for building the model architecture

Table of Contents

• Deep Learning Framework
• Programming Language
• Secure Hardware/Execution
• Data Processing and Tokenisation
• Text Segmentation
• Model Architecture Specifics
  • ISO Documents
  • High Acc NLU
• Database Management

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Deep Learning Framework

• Multi-Transformer Arch (Contextual Embedding)
• Convolutional Neural Networks (CNN) and Contextual Neural Memory (CNM) (ReLU)

Programming Language

• Python (Backend) (Standard for LLMs)
  • PyTorch (Deep Learning Framework)
  • HuggingFace Transformer
  • vLLM (Interface Engine)
• React TypeScript (Frontend)

Secure Hardware/Execution

To establish TEEs, the hardware would require one of the following:

• Intel SGX
• AMD
• Arm TrustZone

Data Processing and Tokenisation

• NLTK (Text Processing and Tokenisation)
• HuggingFace Dataset (Apache Arrow)
  • Memory mapped storage (stored in secure folders)

Text Segmentation

• GraphSeg (Unsupervised Algorithm)

Model Architecture Specifics

ISO Documents:

• Clinical-Longformer
  • 4096 tokens
  • More Clinical Tuned
• BigBird
  • 4096 tokens
  • Ideal for understanding long texts
  • 128 million parameters

Hig Acc NLU:

• DeBERTa-v3 (Outpreforms BERT, RoBERTa and DeBERTa versions)
  • Base Model :~: 184 million (parameters)
  • Large :~: 435 million
  • XLarge :~: 750 million
  • XXLarge :~: 1.5 billion

Database Management

• Pinecone/Milvus/pgvector (Vector)
  • Zilliz Cloud (Milvus Serverless option - reduces maintenance)
• SQLite/PostgreSQL (Relational)
• LangChain/LlamaIndex (Orchestrator)

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How it works

How the model will operate, align itself, and component expansion

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Table of Contents

• Front-End
  • Query Module
  • Class Comparison Engine
  • Interpretability Dashboard
• Back-End
  • Data Layer Management
  • Machine Learning Layer Management
  • Risk and Security Management

Front-End

Query Module

• Structure Queries (Incoming)
• Natural Language Queries (Incoming)
• User Interaction (Gateway)

Class Comparison Engine

• Matches ISO control attributes against user query (Model Prediction Classes)
• Returns relevant policy segments (User Specific)

Interpretability Dashboard

• Attention Heatmap (Display)
• Highlights flagged words/tokens (Display)
• Compliance vs Non-compliance (Number x Report)

Back-End

Data Layer Management

• Policy Extraction
  • Crawler
  • Document Parser
  • Removing Irrelevant metadata
• List Aggregation and Handling
  • Lists are merged into paragraphs
  • Long lists are split but include context statements and labels
• Semantic Segmentation
  • Algo to break doc into segments
  • Ensure context is kept with cliques of related sentences

Machine Learning Layer Management

• Domain-Specific Embeddings
  • Subword Embeddings
  • Train on a massive number of specific security policies
• Hierarchical Multi-Label Classification
  • 2 stage model: Top and Bottom
  • Predict High-Level ISO domain
  • Predict Low-Level classifiers for fine-grain attributes
• Class Imbalance Handling
  • Data Augmentation
  • Class Weighting

Risk and Security Management

• NIST AI Risk Management Framework
  • Development and Deployment Life Cycle (Governed)
  • Must incorporate trustworthiness, transparency, and risk mitigation (Continuously)
• Trusted Execution Environments
  • Hardware-enforced isolation
• Threat Modeling
  • Threat Model Testing (Before Deployment)
    • OWASP
    • Microsoft Threat Modeling Tool