\Augmented Ontological-Semantic Platform 

What is A-OSP? 

A-OSP (Augmented Ontological-Semantic Platform) is an open-source, AI-driven software framework specifically designed to support companies in managing regulatory compliance, initially focused on the Italian Legislative Decree 231/01 and subsequently extending to other national and European regulatory frameworks. A-OSP provides a structured and transparent method to autonomously generate complex regulatory compliance documents, such as the Organization, Management and Control Model (Modello 231), Risk Assessments, and Gap Analyses, by integrating epistemic methodologies, semantic analysis, and advanced AI tools.

How Does A-OSP Work?

A-OSP combines several advanced methodologies and technologies:

1. Epistemic Compliance Methodology

A-OSP employs a systematic epistemic approach, structuring regulatory processes into clear, logical, and auditable steps. This transforms traditionally unstructured compliance activities into structured, traceable analyses that provide robust legal documentation and defensible positions.

2. Semantic and Ontological Analysis

The platform leverages a proprietary knowledge base aligned with international standards, including:

• ISO 31000 (Risk Management)

• ISO 37001 (Anti-bribery Management Systems)

• ISO 27001 (Information Security Management)

• COSO Framework (Internal Control Framework)

• ...

This knowledge base feeds the semantic extraction processes and enhances the precision and consistency of generated documents.

3. Advanced AI Integration (LLMs)

At the core of A-OSP lies the sophisticated use of Large Language Models (LLMs), such as OpenAI's GPT models. Through advanced prompt-engineering, the platform formulates targeted queries (prompt chains) to the AI, ensuring highly specific, contextually accurate, and semantically consistent outputs. For example:

• A-OSP decomposes corporate data (policies, processes, internal audits, questionnaires) into anonymized semantic fragments (231chunks).

• These fragments are analyzed through API-based interactions with enterprise-level LLM providers.

• AI responses are subsequently recomposed locally into coherent regulatory documents, ensuring maximum accuracy and minimizing ambiguity.

Compliance Output Examples

A-OSP autonomously generates customized compliance documents, such as:

1. Organization, Management and Control Model (Modello 231):

   • Mapping and analysis of corporate processes against the Legislative Decree 231/01.

   • Detailed control measures, preventive procedures, and corrective recommendations.
     
     

2. Risk Assessments:

   • Automated identification of potential legal and regulatory risks.

   • Evaluation and classification of risks according to industry standards.
     
     

3. Gap Analyses:

   • Comparison between current organizational posture (as-is) and regulatory requirements (to-be).

   • Precise identification of gaps, areas for improvement, and strategic action plans.
     
     

Data Privacy and Confidentiality

A-OSP's architecture ensures privacy by design:

• Local-first approach: Document processing and semantic correlation occur strictly locally.

• Anonymous Data Transmission: Fragmentation and semantic anonymization before external AI processing.

• Enterprise AI Licensing: Optional enterprise subscriptions ensure complete confidentiality, explicitly preventing AI providers from reusing submitted data for further machine learning. Users should verify enterprise licensing terms directly with AI service providers (typical cost: approx. €100-200/month as of mid-2025, API calls excluded; suitable even for short-term subscriptions).

Key Technical Features

• Backend: Python (3.9+), FastAPI framework, SQLAlchemy ORM, Celery task-queue, Redis, and GitPython for version control.

• Frontent: React.js (SPA architecture), Zustand state management, React Router, and WebSockets for real-time interactions.

• Prompt Engineering and AI Management: Dynamic prompt-generation for precise semantic extraction, fully customizable via YAML configuration.

• Audit Trail & Versioning: Complete auditability through detailed logging, semantic artifacts tracking, and rigorous version control (Git).

• Local-first Data Management: Data privacy ensured through local processing; fragmented and anonymized information is transmitted externally only in disconnected, semantically anonymized units.