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. --- ## 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 --- ## 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?” --- ## Architecture ### Frontend * [login to view URL] (App Router) * TypeScript * Tailwind CSS * Dashboard-focused UX: * Compliance score * Dataset registry * Model mapping * Report viewer * Audit logs --- ### Backend * Node.js + Express (TypeScript) * PostgreSQL + Prisma * MinIO (S3-compatible) for report storage Responsibilities: * CRUD + validation * Risk engine * Audit logging * Report generation --- ### 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 --- ## 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 --- ## Role-Based Access Control (Planned) * Admin: full control * Analyst: dataset and report access * Viewer: read-only RBAC enforced: * in API * in frontend UI --- ## Deliverables * [login to view URL] frontend dashboard * Express API backend * PostgreSQL schema (datasets, models, audit logs) * S3-compatible storage for reports * Compliance report generation (PDF/CSV) --- ## 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 --- ## 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 --- ## Positioning This is not: * a model training platform * a generic machine learning tool This is: AI data provenance and compliance infrastructure

Project ID: 40362394