Voice Phishing Simulation Training Studio

3.85

Derivation Chain

Step 1 Spread of AI voice phishing detection technology

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Step 2 Demand for voice phishing response training in companies and institutions

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Step 3 Automated creation tool for training simulation scenarios

Problem

Security managers at financial institutions, local governments, and SMEs (20-200 employees) are required to conduct quarterly voice phishing response training, but creating realistic scenarios takes 3-5 days per case, or 2-5 million KRW (approx. $1,500-$3,750) when outsourced. Failing to reflect the latest tactics reduces training effectiveness, and when actual incidents occur, insufficient evidence of training implementation often leads to criticism from supervisory authorities.

Solution

Based on a database of the latest voice phishing cases and tactics, AI automatically generates customized simulation training scenarios by industry and job level, and aggregates employee response results into reports, producing one-click evidence of training implementation for submission to supervisory authorities. TTS-based simulated phone calls and real-time scoring provide experiential training.

Target: Security managers at financial institutions, credit unions, and community credit cooperatives with 20-200 employees, and public officials in charge of information protection at local governments

Revenue Model: SaaS monthly subscription of 99,000 KRW (approx. $74) per organization (up to 50 employees), 199,000 KRW (approx. $149) for up to 200 employees. Additional simulation rounds at 30,000 KRW (approx. $22.50) per transaction. 20% discount for annual payment.

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

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.

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	/ 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 (61/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

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] Frontend [medium] AI/ML [low]

Dashboard