Risk taxonomy data model

## Parent Issue
Part of #149 — AI Risk: IBM/MIT Risk Template Integration

## Description

Define the risk category schema based on IBM AI Risk Atlas and MIT AI Risk Repository. Create database models for risk categories, risk factors, and assessment templates.

## Requirements

- Risk category schema covering: fairness, explainability, robustness, privacy, security, accountability, hallucination
- Risk factors per category (sub-dimensions)
- Assessment template model — stores questionnaire structure per taxonomy
- Schema must be framework-agnostic so new taxonomies (OECD, custom) can be added without a redesign
- Follow existing SQLAlchemy model patterns in `apps/backend/src/infrastructure/db/database/models.py`

## Deliverables

- [ ] Database models for risk categories and risk factors
- [ ] Assessment template model
- [ ] Alembic migration
- [ ] Seed data for initial IBM/MIT taxonomy
- [ ] Basic Pydantic schemas for the new models

## Getting Started

1. Read `apps/backend/src/infrastructure/db/database/models.py` to understand existing model patterns
2. Read `apps/backend/src/domain/compliance/` for how compliance models are structured — risk assessment should follow similar patterns
3. Create models, migration, and seed data
4. Open a PR against `main` and link this issue