A

Public Data Residual PII Detector

4.05

Derivation Chain

Step 1 Tightened public data center safety standards + migration to private cloud
→
Step 2 Residual PII risk in migration-target data
→
Step 3 Automated detection of un-de-identified personal information in public datasets

Problem

Public agencies must de-identify data before migrating to private cloud, but it is extremely difficult to manually find residual PII — names, national ID numbers, phone numbers, addresses — buried in unstructured text fields across hundreds of thousands of records. Even a single de-identification failure constitutes a Personal Information Protection Act violation, risking fines and media exposure.

Solution

(1) Scan DB tables/files to auto-detect PII patterns (national ID numbers, phone numbers, emails, addresses, names) in unstructured text, (2) display results per record with masking recommendations, (3) generate a de-identification completion certification report for audit readiness.

Target: Privacy officers at public agencies, data migration teams at public-sector SI firms
Revenue Model: Up to 100K records: $750; up to 500K records: $2,250; up to 1M records: $3,750. 30% discount for annual recurring scan contracts.
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
4.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (71%)

Tech Complexity
29.3/40
Data Availability
21.7/25
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 (68/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
20.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
15.0/15
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 [medium] Frontend [low]
Dashboard