Regulatory Alignment
EU AI Act. August 2026.
DRM3 provenance is not a compliance product.
It is infrastructure that makes compliance provable.
The EU AI Act enforcement deadline is August 2026. Articles 12, 13, 14, and 17 require transparency, record-keeping, human oversight, and quality management for high-risk AI systems. Most deployed AI infrastructure does not qualify.
The gap is not policy. It is architecture. Compliance requires proof. Proof requires records that are created at the moment of operation, not reconstructed afterward. Records that are cryptographically signed, not administratively managed. Records that any third party can verify without trusting the provider.
That is what DRM3 builds. Not for compliance. For data integrity. Compliance is a consequence.
Record-Keeping
What It Requires
High-risk AI systems must allow for automatic recording of events (logs) throughout the system's lifetime. Logging must capture operation periods, input data references, and the identification of natural persons involved in verification.
How DRM3 Addresses It
Every DRM3 operation produces an Ed25519-signed receipt at the moment it occurs. Receipts contain input hashes, output hashes, signer identity, and timestamps. Receipts chain into Merkle trees. The chain is unbroken from raw source to final output. Not a log reconstructed after the fact. A cryptographic record created in real time.
Transparency and Provision of Information
What It Requires
High-risk AI systems must be designed to ensure their operation is sufficiently transparent to enable deployers to interpret the system's output and use it appropriately. Users must be informed of the system's capabilities, limitations, and intended purpose.
How DRM3 Addresses It
Every AI analysis in DRM3 carries a receipt that identifies the model version, data sources, and transformations applied. Walk the provenance chain from any output back to every input. No black boxes. No trust required. The chain is independently verifiable by any third party.
Human Oversight
What It Requires
High-risk AI systems must be designed to allow effective oversight by natural persons during the period of use. Oversight measures must enable individuals to fully understand the system's capacities and limitations and to properly monitor its operation.
How DRM3 Addresses It
DRM3 provenance receipts are queryable, filterable, and auditable. Every receipt carries a human-readable attestation statement describing what was fetched, from where, and how. The Global Monitor at status.drm3.network provides real-time visibility into every signer, scanner, and pipeline across the network.
Quality Management System
What It Requires
Providers must implement a quality management system that ensures compliance throughout the AI system's lifecycle, including data management, risk management, and post-market monitoring.
How DRM3 Addresses It
Merkle trees roll up all receipts per batch. Service signers attest over producer receipts. The DRM3 Root certifies service signers. Every data source license is commit-hash-pinned in the receipt, proving which license version was in effect at the moment of collection. Verification requires no trust in DRM3 or any intermediary.
The Distinction
Compliance reporting vs. compliance architecture.
Most AI compliance tools generate reports after the fact. They reconstruct what happened from logs, metadata, and system state. The report is only as trustworthy as the system that generated it.
DRM3 provenance creates the proof at the moment of operation. Every receipt is signed before the next operation begins. The proof is inseparable from the data. A third party can verify any receipt without access to the system that created it and without trusting anyone in the chain.
That is the difference between reporting compliance and being compliant.