Published Paper Details:

Startups are engines of innovation but face disproportionately high failure rates, with 90% failing within five years-- often due to inadequate protection of proprietary knowledge. Trade secrets, such as algorithms, customer insights, and business strategies, offer a cost-effective alternative to patents for early-stage ventures, yet are frequently overlooked due to informal practices, limited resources, and lack of awareness. This paper introduces the SECRETS Model (Startup-Enabled Comprehensive Retention and Trade-secret Strategy), a practical, low-cost framework designed to help startups systematically identify, protect, and manage trade secrets using familiar tools like Google Drive, Notion, and Slack. Through a detailed case study of an AI SaaS startup and statistical validation, we demonstrate a 64% risk reduction, 10x return on investment, and significant improvements in founder awareness and NDA compliance. The SECRETS Model is scalable, sector-adaptable, and implementable in under 12 hours for less than $100--offering a transformative IP protection approach tailored to the realities of early-stage entrepreneurship. We also introduce the SECRETS Readiness Score, a diagnostic tool that quantifies a startup's preparedness to safeguard its intellectual assets. This paper highlights the model's policy and investment implications, advocating for broader adoption through targeted training, automation, and incentive programs.