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When companies adopt AI coding agents, these agents access the filesystem, terminal, and network — but there are no security policies defining what scope to allow. Manually writing policies requires 2-3 days from the security team, and policies must be re-reviewed with every agent update. Without policies, agents risk unintended access such as connecting to production databases or reading secret files.
Analyzes a project's directory structure, .gitignore, and CI/CD configuration to auto-generate least-privilege policies (allowed paths, forbidden paths, network rules) for AI agents. Exports in agent-specific config file formats like CLAUDE.md and .cursorrules, and collects violation attempt logs to suggest policy improvements.
| 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. |