# Research Questions

This document defines the key questions guiding the literature review on local-first AI tools in regulated environments.

## Primary Research Questions

### Q1: Privacy and Data Sovereignty

What are the trade-offs between local-first AI architectures and traditional cloud-based approaches regarding data privacy and user control over personal information?

### Q2: Security Implications

How do local-first AI tools affect the security posture of organizations, particularly regarding threat surfaces, attack vectors, and incident response capabilities?

### Q3: Governance and Compliance

What governance frameworks and compliance considerations are unique to local-first AI deployments in regulated industries (healthcare, finance, legal)?

### Q4: Performance and User Experience

What are the realistic performance expectations for local-first AI tools compared to cloud alternatives, and how do offline capabilities affect user experience?

### Q5: Organizational Readiness

What factors determine whether an organization is ready to adopt local-first AI tools, and what transformation requirements exist?

## Secondary Considerations

- Cost implications of local vs. cloud infrastructure
- Integration challenges with existing systems
- Vendor lock-in concerns with each approach
- Environmental impact considerations
- Future trajectory of local-first AI development

## Evaluation Criteria for Sources

When assessing research papers, consider:

1. **Methodology Rigor**: Is the research methodology clearly described and appropriate?
2. **Sample Size**: How many organizations or use cases were studied?
3. **Generalizability**: Can findings be applied to different regulated contexts?
4. **Recency**: When was the research conducted (AI field evolves rapidly)?
5. **Funding Sources**: Any potential bias from vendor funding?
6. **Conflict of Interest**: Are researchers connected to solution providers?
