B
Binary Audit Report
3.25
Derivation Chain
Step 1
AI+Ghidra binary backdoor detection technology
→
Step 2
Automated binary security auditing
→
Step 3
SI/outsourced software binary integrity verification Report service
Problem
Financial institutions and public agencies lack the internal capability to verify whether software delivered by SI vendors contains backdoors or malicious code. External security audits cost $7,500-$22,500 (10-30 million KRW) per engagement, and mid-sized financial firms receiving 10-30 deliveries annually cannot afford full inspections, resorting to sample-based checks only.
Solution
A SaaS that automatically detects backdoors and malicious patterns through AI-powered static analysis when delivered binaries are uploaded, generating compliance reports. (1) Automated reverse engineering using Ghidra/radare2 + AI anomaly pattern detection, (2) reports mapped to Financial Supervisory Service/ISMS-P audit criteria, (3) delivery history management + automated change comparison.
NUMR-V Scores
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. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (57%)
Data Availability
20.0/25
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 (58/100)
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 [high]
AI/ML [medium]
Frontend [low]