This analysis identified 20 compliance patterns across SOC2, GDPR. After independent verification by Gemini 2.5 Flash, 3 high-severity findings were confirmed and 1 were identified as possible false positives — likely pattern matches in documentation or configuration files rather than application source code. The 3 confirmed findings represent the primary areas requiring attention.
| Framework | Regulatory Body | Max Penalty | Enforcement Trend |
|---|---|---|---|
| SOC2 (2 high) | AICPA | Loss of enterprise contracts | Mandatory for enterprise SaaS sales |
| GDPR (1 high) | EU DPAs | Up to EUR 20M or 4% turnover | Cross-border enforcement rising |
| Framework | High Risk | Confirmed | False Pos. | Medium | Concern | No Issue |
|---|---|---|---|---|---|---|
| SOC2 | 2 | 9 | 1 | 3 | 3 | 2 |
| GDPR | 1 | 9 | 0 | 3 | 5 | 1 |
OpenDocket scans source code patterns — specifically whether code handles regulated data in compliance-aware ways. It searches for evidence of encryption, access controls, consent mechanisms, audit logging, and other patterns that regulators look for.
Claude Sonnet scans broadly — it finds every pattern that could indicate a compliance risk. This produces a comprehensive set of findings but includes noise from documentation files, config references, and test data.
Gemini 2.5 Flash then challenges each finding independently. It asks: is this evidence from actual application code? Would a regulator actually find this concerning? Is the severity appropriate?
The result is a tiered output:
The confirmed count is the number you should act on. The total count shows the full scope of what was examined.
Evidence is classified into three tiers based on file type:
A finding is only classified as High Risk if supported by at least two source code files with matching evidence. Documentation references alone produce Pattern of Concern at most.