AI Agent Permission Policy Generator

3.80

Derivation Chain

Step 1 Proliferation of AI coding agents

→

Step 2 AI agent security management services

→

Step 3 Agent filesystem permission policy auto-generation tool

Problem

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.

Solution

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.

Target: Security leads and CTOs at IT companies with 10-50 employees, organizations with 3+ developers using AI agents

Revenue Model: Single project free; organization-wide (unlimited projects + violation logs + auto-updates) at $59/month; annual plan at $44/month

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

5.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

4.0/5

NUMR-V Scoring System

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (78%)

Tech Complexity

34.7/40

Data Availability

23.1/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

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.

Market Validation (62/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

18.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

7.0/10

Validation Breakdown

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.

Technical Requirements

Backend [medium] AI/ML [low] Frontend [low]

Dashboard