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AI startups (5-30 employees) face high risk of trade secret leakage (model architecture, training data, prompts, etc.) when key personnel leave, but lack dedicated legal teams to build internal systems that meet secrecy management requirements. Cases where companies lose trade secret disputes due to failure to meet 'secrecy management' standards are frequent.
Provides a secrecy management checklist based on Korea's Trade Secret Protection Act, and automatically generates confidentiality classifications, access controls, and departure NDAs for AI-specific assets (model weights, training data, prompts, fine-tuning configurations, etc.). Delivers quarterly self-audit reports and legal response templates.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |