B

Agentic AI Permission Audit Service

3.35

Derivation Chain

Step 1 Agentic AI proliferation (Galaxy Unpacked 2026)
→
Step 2 Security infrastructure for devices with agentic AI
→
Step 3 Permission audit service for autonomous agent actions

Problem

With agentic AI being embedded in smartphones, led by Samsung Galaxy, agents autonomously perform sensitive actions such as payments, reservations, and message sending on behalf of users. App developers (10-50 employees) have no way to audit which permissions the agent used and what actions it took, leading to an average of 2-4 weeks of manual log analysis and legal response costs when customer complaints or personal data incidents occur. In particular, as Korea's Personal Information Protection Act strengthens the obligation to explain automated decision-making, compliance risks are rapidly increasing.

Solution

Automatically collect agentic AI's API calls and permission usage logs to (1) detect in real time whether permissions were exceeded for each action, (2) generate a one-click automated decision-making explanation report under Article 37-2 of the Personal Information Protection Act, and (3) provide a monthly permission usage statistics dashboard to support proactive governance.

Target: Mobile app developers incorporating agentic AI features, IT startups and agencies with 10-50 employees.
Revenue Model: SaaS monthly subscription of 59,000 KRW (approx. $44.25) per app (up to 100,000 agent sessions per month), 50 KRW (approx. $0.04) per additional session. 20% discount for annual payment.
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (72%)

Tech Complexity
29.3/40
Data Availability
23.1/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

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