Trust infrastructure
for AI.
Provenance, AI decisions, authority, regulatory attributes. For each, a cryptographic proof.
Every time AI uses data, the questions follow: where did it come from, what did it look at to decide, on whose behalf did it act, and does it meet the regulations? Lemma trust infrastructure answers all four across four axes.
Trust, across every phase.
Your AI workflow stays as-is. Add the Lemma trust layer once, and proofs ride along every phase — from input through delegated execution.
Proprietary cryptography,
three cores.
Three core technologies make the trust infrastructure work — establishing "it's the same thing," "disclose only this," and "no one can rewrite it," all without touching the original data.
Proof
Turn business rules like "18 or older" or "revenue over threshold" into machine-verifiable cryptographic facts. Prove a statement's truth without touching the original.
Disclosure
Keeping the binding to the original issuer signature, the holder reveals only the attributes a model needs. Minimize what AI receives as input.
Provenance
Anchor the document commitment, schema, issuer, and ZK verification result on-chain. Fix it as provenance that can be neither rewritten nor erased.
Data stays in place.
Only proofs travel.
The source mints a proof, only the necessary parts are selectively disclosed, and a verifier checks them with a public key. Through all three phases, raw data never moves.
Mint the proof at the source
Embed the Lemma SDK at the source. Issue attribute proofs for data, models, and authority. Raw data never leaves.
Reveal just enough
Selectively disclose only the facts a verifier needs — authority, attribute, integrity. Raw data is never touched.
Anyone can confirm
Anyone, anytime, verifies authenticity with a public key. Optionally anchor as tamper-evident audit attestations.
Four axes,
your schema.
Trust infrastructure has four axes — verifiable origin, verifiable AI, agent authority proof, regulatory attribute proof. Schemas aren't dictated by Lemma; define them to fit your domain and issue/verify them via the SDK. The examples below are framed as your.*.v1.
"schema": "your.provenance.v1",
"subject": "did:lemma:utility-meter-001",
"issuer": "did:lemma:org-acme-utility",
"sourceHash": "0x9f3a...c4e8",
"lineageChain": ["sensor", "scada", "oracle"],
"recordedAt": "2026-06-02T14:30:00Z",
"integrity": "poseidon-merkle",
"proof": {
"type": "BBS+Signature2020",
"value": "0x4a2b...e1d3"
}
}
P1 · Verifiable Origin
Provenance schema
Record the multi-tier provenance of data as it propagates sensor → SCADA → Oracle, in tamper-evident form. The body never leaks; a hash chain + BBS+ signature proves "this is the same thing."
Capability: Prove data source and tamper-evidence without disclosing content
Use cases: RAG source trust / training data clearance / IoT sensor provenance
"schema": "your.model.v1",
"agent": "did:lemma:agent-fin-bot-007",
"modelId": "claude-3.7-sonnet",
"policyHash": "0x71c5...8b9a",
"inputCommitment": "0xb4e2...3f10",
"outputCommitment": "0xc8f1...a2d5",
"satisfiesPolicy": true,
"proof": {
"type": "Groth16",
"circuit": "lemma/model-attest@1.0",
"value": "0x9d7e...c4f2"
}
}
P2 · Verifiable AI
Model schema
Record the model ID, applied-policy hash, and commitments over inputs and outputs. The actual I/O is never revealed; only policy satisfaction is proven via Groth16. Closes the LLM audit blind spot.
Capability: Independently verify AI input/output integrity
Use cases: AI audit logs / compliance reporting / accountability
"schema": "your.agent.v1",
"agent": "did:lemma:agent-treasury-042",
"delegatedBy": "did:lemma:org-acme-fin",
"role": "treasury_agent",
"spendLimitUSDC": 500,
"scope": "x402://api.partner.jp/*",
"validUntil": "2026-06-30T23:59:59Z",
"proof": {
"type": "Groth16+EIP3009",
"x402PaymentId": "0xa1f3...7d8e",
"value": "0x6b2c...e4a9"
}
}
P3 · Agent Authority
Agent schema
delegatedBy says who delegated; role / spendLimit / scope say what and how far. Attached per x402 payment via Trust402, proving an autonomous agent's actions with authority bound.
Capability: Run autonomous agents in production—full speed in scope, stop out of scope
Use cases: Agent payments / expense approval / API metering / cross-agent
"schema": "your.attribute.v1",
"holder": "did:lemma:org-fsa-licensed",
"issuer": "did:lemma:authority-jp-fsa",
"jurisdiction": "JP-FSA",
"licenseType": "type-1-financial",
"disclosed": ["isLicensed", "validUntilYear"],
"hidden": ["licenseNo", "address", "executives"],
"proof": {
"type": "BBS+SelectiveDisclosure",
"value": "0x3c8d...f7a2"
}
}
P4 · Regulatory Attribute
Attribute schema
Attribute credentials issued by authorities. disclosed / hidden control what is shown and what stays hidden, via BBS+ selective disclosure. Eliminates the need to centralize KYC / license raw data.
Capability: Prove regulatory attributes selectively without sharing originals
Use cases: KYC / AML / CBAM / EUDR / AI Act / public procurement
Run every step of AI work on top of trust.
Consistent provability
From input to delegated execution, proof holds at every step of the AI workflow.
Regulatory compliance × AI automation
Regulatory compliance and the speed of AI work coexist within the same structure. Compliance doesn't stop the pace.
Multi-layered trust chains
Every layer of agent chains and organizational chains can be built into your operations as a verifiable flow of trust.
Three entry points.
By theme, by scenario, by incident.
Solutions
Tie trust infrastructure to business themes across both offense and defense.
See Solutions →Use Cases
30+ business scenarios across industries and tasks. See the before/after for each axis.
See Use Cases →Critical Brief
Major incidents across AI, cryptographic infrastructure, and supply chains, analyzed structurally through the lens of trust infrastructure.
Read the Brief →Get started building.
Documentation to start implementing the four axes with shared schemas and cryptographic primitives.
Start the conversation.
Discovery Call
We'll hear out your business scenario in 30 minutes. No technical details, personal information, or confidential data required.
Get in touch →Dashboard
Try the canonical schemas for all four axes in the Lemma Dashboard in five minutes. Read it alongside the SDK and Guides.
Dashboard →