Deterministic compliance document linter. Every flag traces to a regulation paragraph. Zero LLM cost at runtime. DLP-safe.
The problem: Compliance teams review documents manually at $200-$500/hour. Inconsistent. Slow.
The LLM trap: Uploading 200 internal policy docs to ChatGPT violates your DLP policy. LLM output is non-deterministic and produces no audit trail.
The solution: Generate deterministic rules from regulations once. Run them in milliseconds on every document. Each flag is auditor-verifiable.
Every finding chains from document to regulation paragraph. An auditor can verify the rule exists, it maps to the correct section, and the token matched. No black box.
| Regulation | Rules | Domain | Authority |
|---|---|---|---|
| SOC2 Trust Services | 10 | Security / GRC | AICPA |
| GDPR | 10 | Data Protection | EU |
| HIPAA Security Rule | 10 | Healthcare | HHS |
| ISO 27001 | 10 | Information Security | ISO/IEC |
| BSA/AML | 6 | Financial Crime | FinCEN |
| SEC Marketing Rule | 6 | Investment Advisors | SEC |
| FINRA Communications | 5 | Broker-Dealers | FINRA |
| SOX Section 404 | 5 | Internal Controls | PCAOB |
Plus common quality rules: plain language, passive voice, sentence length, date formats, defined term consistency.
Uploading internal policies to ChatGPT violates data loss prevention policies. Redline runs locally.
LLM output is a blob of text. Redline traces every flag to a specific regulation paragraph. Auditors can verify.
Same document, different LLM output each time. Redline produces identical results every run. CI-friendly.