AI Governance

Training Verification

Privacy-preserving verification of AI training data provenance. Prove compliance without exposing your data.

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Capabilities

Privacy-preserving verification

Zero-Knowledge Proofs

Prove training data compliance without revealing the underlying dataset using zkSNARKs and zkSTARKs.

Secure Enclaves

Run verification within hardware-isolated environments like Intel SGX and ARM TrustZone.

Federated Verification

Distributed verification across multiple independent parties for trustless consensus.

Cryptographic Attestation

Signed proofs of data lineage using ring signatures and commitment schemes.

Consent Tracking

Track consent status for each data source: granted, denied, withdrawn, expired, or not required.

Proof Registry

Immutable registry of verification proofs with expiration dates and verifier attestations.

Developer Integration

Verify training data

verify.ts

import { DRD } from '@drd/sdk';

const drd = new DRD({ token: 'drd_live_sk_...' });

const verification = await drd.training.createVerification({
  modelId: 'model_v3',
  verificationMethod: 'zero_knowledge',
  proofType: 'zksnark',
  datasetDescriptor: 'Licensed-Images-2026',
});

await drd.training.verifyProof({
  id: verification.id,
  verifierName: 'DRD Verification Service',
});

Prove compliance, protect privacy

Zero-knowledge proofs for training data verification without data exposure.

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