Large language models are powerful general-purpose tools. But compliance gap analysis requires deterministic, traceable, enforcement-calibrated intelligence — not probabilistic text generation.
LLMs can invent regulations, misquote section numbers, and confidently state wrong obligations. In compliance, a wrong answer is worse than no answer.
Ask the same question twice, get different answers. Compliance requires deterministic, auditable results that hold up to regulatory scrutiny.
LLMs have no enforcement data. They can't tell you which obligations regulators actually enforce, what penalties result, or where your real risk lies.
| AuditDSS | ChatGPT / Claude | |
|---|---|---|
| Obligation mapping | 146,445 verified obligations from 320 regulations | Generated on-the-fly, unverified |
| Hallucination risk | Zero — fixed knowledge graph | High — can invent regulations and section numbers |
| Enforcement data | 1,732 real enforcement actions, $209B+ in penalties | None — no access to enforcement databases |
| Risk scoring | 4-axis scoring calibrated on enforcement evidence | Subjective opinion with no calibration |
| Reproducibility | Deterministic — same input, same output, every time | Non-deterministic by design |
| Audit trail | Every finding traces to source section and paragraph | No provenance, no traceability |
| Regulatory updates | Knowledge graph maintained and updated | Training data cutoff, may be outdated |
| Coverage | 21 jurisdictions, 25 industries | Claims to know everything, verifies nothing |
| Compliance evidence | Timestamped assessment reports for regulators | Chat transcripts (not audit-worthy) |
| Co-citation analysis | 28,947 Bayesian links between obligations | No structural analysis |
AuditDSS is not an LLM wrapper. We use large language models for one specific, well-bounded step: extracting compliance claims from your uploaded document.
The regulatory knowledge — obligations, dependencies, enforcement history, risk scores, cascade models — lives in our deterministic intelligence database. Built over years. Validated against source legislation. Updated by our regulatory engineering team.
The LLM doesn't know the regulation. Our graph does. The LLM reads your document. Our engine scores it.
AI handles extraction. Everything else is deterministic.
Every gap finding traces back to a specific obligation, in a specific section, of a specific regulation. Your auditor can verify it.
Risk scores are weighted by what regulators actually enforce — not by what an AI thinks sounds important.
Run the same assessment next quarter. Compare results. Track progress. That requires deterministic scoring, not chat conversations.
Upload a compliance document. Get obligation-level gap analysis with enforcement-calibrated risk scores. No hallucinations, no guesswork.