AI Audit Log Proof
Seal AI decision attribution with a ZK proof at decision time. Make past rationale recoverable after model updates.
Three voices from the front line.
- Internal audit / compliance
“To reconstruct the basis of an AI decision later, we need a trail of the inputs, model and process”
- Legal / executives
“We can't explain AI-driven decisions to regulators or shareholders”
- CISO / security
“We can't detect tampering of AI decisions, so accountability is blurred”
Hand over the source, or just the facts?
Change what reaches the AI, and the leakage risk goes with it.
- prompt:
- credit decision for case ○○
- model:
- gpt-internal-v4
- params:
- temp=0.2 …
- response:
- approved
- timestamp:
- 2024-08-15…
- agent:
- did:lemma:agent-credit-reviewer
- modelId:
- gpt-internal-v4
- policyHash:
- 0x3d90…
- inputCommitment:
- 0x7a2c…
- outputCommitment:
- approval = policy-compliant
- satisfiesPolicy:
- true
- ZK verified:
- ✓ VALID
At the moment the AI decides, the model used, the facts input, the criteria applied, and the final conclusion are fixed as one verifiable trail. The raw data stays in-house; what leaves is only the fact of "when, which model, on what basis, decided what." Past decisions stay immutable even as the model updates, and regulators, auditors and claimants can independently verify the same trail without disclosing the original data.
See the technical details ↗Choose on three criteria.
Only work that needs all three at once — pass without exposing, independent verification, tamper-proof — is Lemma's domain.
| Method | Pass without exposing | Independent verification | Tamper-proof |
|---|---|---|---|
| Access control only | △ | ✗ | ✗ |
| Masking / anonymization | △ | ✗ | ✗ |
| Encryption only | ✓ | ✗ | ✗ |
| Logging / monitoring only | △ | ✗ | ✗ |
| Lemma (ZK proof)the only one with all 3 | ✓ | ✓ | ✓ |
What's next
We enter through AI-governance and regulatory-readiness support and a PoC, and stay alongside you through to operations.
- A 30-minute review — identify the AI decisioning systems where accountability risk concentrates (lending, underwriting, clinical triage, public benefits).
- Narrow to 1–2 decisions (results) to prove — e.g. "a loan decline," "a credit-tier decision" — the facts sealed at decision time. Not the source data.
- Design connection and versioning — connection to your existing MLOps / inference pipeline, and version-fixing of the model identifier and applied guideline.
- Prove one decisioning system via a (quote-based) PoC — confirm decision-time attestation works on a single AI decisioning system.
- Hands-on support from rollout through operations — existing plan tiers (Civic / Critical / Compliance) serve only as a cost reference; the setup and pricing are designed together.
Tell us one AI decisioning system where accountability risk concentrates, in the first 30 minutes. No model implementation details or sensitive information required.
The bigger picture
The bigger picture this use case belongs to.
We map use scenarios across industries and workflows by the four axes.
See use scenarios for Verifiable AI in Solutions →TRY LEMMA
Run it yourself.
No sales call needed — start hands-on with Lemma's products.