B

Defense Tech Disclosure Scope Assessor

3.55

Derivation Chain

Step 1 K-Defense startup entry barrier reduction policy
→
Step 2 Defense startup technology disclosure/protection dilemma
→
Step 3 Automated tech disclosure scope assessment and document generation

Problem

Defense startups face difficulty determining how much of their technology can be disclosed during VC fundraising, overseas partnerships, or academic presentations. Violating the Defense Industry Technology Protection Act carries penalties of up to 5 years imprisonment, yet legal consultation costs $2,250-$6,000 (3-8 million KRW) per case. Setting disclosure boundaries too conservatively means lost investment/business opportunities; too aggressively means legal risk.

Solution

A SaaS where users upload technical documents, presentations, or proposals, and the system automatically classifies content into disclosable/restricted/caution zones based on the Defense Industry Technology Protection Act, then generates a safe-to-share version of the document. Core features: (1) Automatic sensitivity classification of technical documents (public/caution/restricted), (2) Automated masking of restricted items, (3) Auto-generation of disclosure-safe document versions.

Target: Defense startup CTOs/CEOs (5-30 employees), defense company technology protection officers
Revenue Model: SaaS Monthly Subscription at ~$112/account/month (149,000 KRW, 20 document analyses/month). Additional documents at ~$15/each (20,000 KRW). Law firm referral matching commission: 10%.
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

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

Feasibility (72%)

Tech Complexity
29.3/40
Data Availability
22.5/25
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 (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
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

AI/ML [medium] Backend [medium] Frontend [low]
Dashboard