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Over 80% of individuals and small teams hosting open-source AI models (LLaMA, Stable Diffusion, etc.) on their own servers deploy without checking network exposure, authentication settings, model file permissions, or API endpoint security. This leads to increasing incidents of unauthorized access resulting in GPU resource hijacking (cryptomining) or training data leaks.
Automatically scan the security posture of self-hosted AI servers with a single CLI command, delivering a vulnerability report and one-click remediation scripts. Core features: (1) Network exposure scanning (open ports, unauthenticated APIs), (2) Model file and data permission auditing, (3) Docker/Kubernetes security configuration auditing, (4) Auto-generated remediation scripts.
| 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. |