Open Source vs. Proprietary AI: Navigating the Future
Evaluating the security and engineering tradeoffs in LLM development approaches
This research examines the tensions between open-source and proprietary approaches to Large Language Models, focusing on security implications and engineering considerations.
- Open-sourcing offers transparency and trustworthiness but creates potential for misuse
- Proprietary models benefit from private sector resources and clearer paths to ROI
- Development approach decisions involve balancing privacy, transparency, financial incentives, and IP concerns
- Each path presents distinct security tradeoffs that organizations must strategically evaluate
For security professionals and AI strategists, this research provides a data-driven framework to assess development approaches based on organizational priorities and risk tolerance.