Sensitive Data Classification Pre-Screening

4.40

Derivation Chain

Step 1 Policy for transferring sensitive public data to private cloud

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Step 2 Demand for sensitivity classification of data to be transferred

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Step 3 Automatic pre-screening tool for dataset sensitivity levels

Problem

As public data moves to private cloud, information security officers at each institution must manually classify thousands of datasets into sensitivity levels (general/restricted/secret). Each dataset takes 30 minutes to 1 hour, and interpretation varies by officer, leading to audit risk.

Solution

Upload dataset metadata (column names, sample data), and automatically determine sensitivity level based on the Personal Information Protection Act and Public Data Act. Provide the basis for determination (relevant legal provisions, matched sensitive patterns) for audit evidence.

Target: Information security officers (grade 5-7) at central government ministries and local governments, public institution cloud migration PMs, and cloud migration consulting firms (10-30 employees).

Revenue Model: API pay-as-you-go: 5,000 KRW per dataset (up to 100) (approx. $3.75), 3,000 KRW per dataset for 101+ (approx. $2.25). Annual flat rate for institutions: 5,000,000 KRW (approx. $3,750) for unlimited.

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

5.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

5.0/5

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 (70%)

Tech Complexity

29.3/40

Data Availability

20.6/25

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 (80/100)

Competition

10.0/20

Market Demand

20.0/20

Timing

20.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

15.0/15

Solo Buildability

5.0/10

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 [low] AI/ML [medium]

Dashboard