Secrecy wall
Confidential information can't be handed to AI. Running internal data through cloud AI exceeds the risk tolerance.
→ A data-less design: AI decides without touching the original.
You can't expose secrets. You can't audit decisions. You can't pass regulation. The last hurdle of AI adoption isn't model quality — it's the proof layer. Lemma resolves it structurally with a data-less design.
● live in production since 2025 · Compatible standards: MCP / A2A / x402 / C2PA / W3C VC · ETHGlobal AI Agents 2026 Finalist
Many companies' AI PoCs succeed but stall before production. The reason isn't model selection — it's the lack of a proof base for using AI accountably as an organization.
Caught at the secrecy / accountability / regulation walls, AI adoption never reaches operations. CISO, Legal, and Compliance have no choice but to slow it down.
Facts get proven without handing over data, dissolving the three walls technically. CISO, Legal, and Compliance can accelerate adoption.
Confidential information can't be handed to AI. Running internal data through cloud AI exceeds the risk tolerance.
→ A data-less design: AI decides without touching the original.
The basis of an AI decision can't be replayed or audited. CISO and internal audit won't sign off on production.
→ Record inputs, model, and process as independently verifiable cryptographic evidence.
There's no way to prove conformity with EU AI Act, ISO 42001, or local rules. Legal blocks PoC from going live.
→ Audit-grade cryptographic evidence and selective disclosure make conformity demonstrable.
Each operational line has data it doesn't want to hand over. Lemma's HIDE → PROVE structure lets AI judge and act while secrets stay put.
Encrypt the original; AI sees only docHash and CID. Authenticity and tamper absence verify without ever opening the original.
Passports and address proofs stay put — only attributes like "over 18", "Japan resident", or "AML clear" disclose via BBS+.
No keys handed to the agent — instead, prove per action that "this AI is delegated for expense approval" and "up to ¥1M" with a scope-bound proof.
Supplier transaction data and origin info stay confidential; only ESG disclosure, tariff classification, and international rule conformity bind to issuer signatures.
Implementation scenarios indexed by industry and operational line. The 'how to integrate' details live in each Use Case.
Preserve inputs, model, and output of AI decisions as tamper-evident evidence.
Disclose only regulatory attributes as cryptographic proofs — no raw data.
Record multi-tier supplier provenance as a tamper-evident chain.
Preserve internal RAG document provenance; make AI citations auditable.
Independently verify civic eligibility without disclosing citizen attributes.
Browse implementation patterns by industry and operational line.
Conceptual structure → Trust Layer (Why) →
Existing systems keep running. Coexistence design: drop the proof layer in front.
Identify which operations need the proof layer and the integration points with existing systems in one session.
Implement the proof layer for one target scenario, end-to-end through internal validation.
Promote the PoC configuration as-is. Keep existing systems running; the proof layer sits in front.
// Add this in front of your existing AI call import { prover } from '@lemmaoracle/sdk' const { proof } = await prover.prove(claim) // Pass the proof to AI. The original never leaves. SDK public release scheduled 2026-06-24. Integration design happens in the Discovery Call.
Check each item that applies. Three or more means it's worth a serious look at integrating Lemma.
Confidentiality, accountability, regulatory compliance — integrating from the proof layer first sets AI adoption's next move in motion.