B

AI Risk Behavior Detection Instructor

3.65

Derivation Chain

Step 1 OpenAI system flaws and strengthening safety reporting systems
→
Step 2 AI service safety testing tools
→
Step 3 AI red team test scenario auto-generation and execution training service

Problem

QA managers and PMs at Korean startups (10-50 employees) launching AI chatbots/agents need to conduct safety testing before release, but without dedicated red team testing expertise, they resort to ad-hoc tests like 'trying profanity input.' If unexpected risky behaviors are discovered after launch, as in the OpenAI Canada incident, it can lead to brand damage, legal liability, and service disruption, with recovery costs reaching tens of millions of KRW.

Solution

Select AI service type (chatbot/agent/image generation) and target user base to automatically generate Korean-specific red team test scenarios (jailbreaking, harmful content elicitation, personal information extraction, bias induction, etc.) and provide a report classifying test execution results by risk level. Includes step-by-step guides and video tutorials that non-experts can follow.

Target: QA teams and PMs at startups planning to launch AI services (10-50 employees), information security teams at mid-sized companies adopting AI, and bootcamps/university courses needing AI safety education.
Revenue Model: Basic free (5 scenario generations per month), Pro: 49,000 KRW (approx. $36.75) per team per month (unlimited scenarios + auto execution + reports), Enterprise: 199,000 KRW (approx. $149.25) per month (custom scenarios + consulting reports + training videos), annual license for educational institutions: 990,000 KRW (approx. $742.50)
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
4.0/5
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 (73%)

Tech Complexity
29.3/40
Data Availability
23.3/25
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
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
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] AI/ML [medium] Frontend [low]
Dashboard