A

Public Data Migration Residual PII Scanner

4.35

Derivation Chain

Step 1 Sensitive public data migration to private cloud
→
Step 2 Residual PII risk in migration data
→
Step 3 PII detection within data after security classification
→
Step 4 PII detection automation and remediation workflow

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

When migrating public data to private cloud, datasets classified as 'General' security grade frequently contain hidden PII such as resident registration numbers, phone numbers, and addresses. In actual migration incidents, 12% of 'General'-grade datasets were found to contain PII. If residual PII migrates to private cloud, it violates the Personal Information Protection Act (PIPA), risking disciplinary action against agency heads and fines up to ~$37,500 (~50M KRW).

Solution

Scans all records in migration-target datasets to detect Korean PII patterns (resident registration numbers, phone numbers, addresses, bank account numbers, etc.) and delivers a report with the location, type, and count of detected PII. Provides automated masking recommendations and generates PII remediation certificates for use as audit evidence.

Target: SI firms executing public data migration projects (20–200 employees); ministry information security officers
Revenue Model: Volume-based pricing: ~$3.75/GB (~5,000 KRW/GB) with 40% discount over 100GB; project license: ~$590/month (~790,000 KRW) for unlimited scanning
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
5.0/5
V Validation
4.0/5
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 (79%)

Tech Complexity
34.7/40
Data Availability
24.4/25
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 (63/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
18.0/20
Revenue Signals
10.5/15
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] AI/ML [low] Frontend [low]
Dashboard