Next.js AI Compliance Platform

# AI Data Provenance & Compliance Platform

## Overview

We are building a digital provenance platform focused on AI training data compliance.

The platform enables organizations to:

* Track the origin of datasets used in AI systems
* Verify licensing and usage rights
* Maintain an immutable audit trail of data usage
* Generate compliance-ready reports for regulators (e.g. EU AI Act)

This is not a general-purpose ML platform—it is a compliance and provenance layer for AI systems.

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## Core Scope (MVP → Already Implemented)

The system must:

* Maintain a dataset registry with metadata (source, license, owner)
* Automatically assign risk levels and compliance signals
* Track relationships between datasets and AI models
* Record audit logs for every action
* Generate compliance reports (PDF, CSV, JSON)

All data is:

* organization-scoped (multi-tenant)
* traceable across its lifecycle

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## Extended Scope (Product Differentiation)

The platform evolves into a provenance engine by:

* Capturing every data interaction

* dataset creation
* linking to models
* report generation

* Providing explainable compliance decisions

* why a dataset is risky
* what rules triggered it

* Enabling lineage reconstruction

* “which datasets trained this model?”
* “what risks exist in this pipeline?”

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## Architecture

### Frontend

* Next.js (App Router)
* TypeScript
* Tailwind CSS
* Dashboard-focused UX:

* Compliance score
* Dataset registry
* Model mapping
* Report viewer
* Audit logs

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### Backend

* Node.js + Express (TypeScript)
* PostgreSQL + Prisma
* MinIO (S3-compatible) for report storage

Responsibilities:

* CRUD + validation
* Risk engine
* Audit logging
* Report generation

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### Provenance Layer (Core Concept)

The system acts as a lightweight provenance ledger:

* Every action produces an audit event
* Events are:

* timestamped
* linked to users and entities
* queryable

This enables:

* reproducibility
* compliance verification
* forensic traceability

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## AI / ML Integration (Future Phase)

The platform will expose:

* APIs to register:

* training runs
* inference events

* Logging for:

* which model used which dataset
* when and by whom

Goal:
Extend provenance from data to model to output

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## Role-Based Access Control (Planned)

* Admin: full control
* Analyst: dataset and report access
* Viewer: read-only

RBAC enforced:

* in API
* in frontend UI

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## Deliverables

* Next.js frontend dashboard
* Express API backend
* PostgreSQL schema (datasets, models, audit logs)
* S3-compatible storage for reports
* Compliance report generation (PDF/CSV)

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## Acceptance Criteria

The platform is successful when:

* A user can:

* register datasets
* link them to models
* generate a compliance report

* The system can:

* flag risky datasets
* explain why
* show full audit trail

* A dataset’s lineage can be reconstructed in two clicks or fewer

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## Vision

This evolves into:

A trust layer for AI systems, where every model can prove:

* what data it was trained on
* whether that data was compliant
* how it has been used

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## Positioning

This is not:

* a model training platform
* a generic machine learning tool

This is:
AI data provenance and compliance infrastructure