Concepts & Architecture
The Data Anchoring Token (DAT) framework defines how AI-native digital assets are represented, verified, and monetized on the LazAI Network.
It provides the foundational logic for data ownership, usage rights, and value attribution, using an interoperable, on-chain token standard.
This section explores the design principles, technical architecture, and lifecycle that power the DAT ecosystem.
Overview
At its core, the DAT standard bridges AI data provenance with decentralized economic infrastructure.
It ensures that every dataset, model, or computational artifact is:
Verifiable — its origin, authenticity, and contribution are cryptographically recorded.
Usable — its access rights are programmable and enforceable via smart contracts.
Rewardable — contributors automatically receive fair value for downstream AI usage.
By combining these three properties, LazAI establishes a new foundation for a transparent and composable AI economy.
Architecture Summary
The DAT system is built on five interoperable layers:
Layer
Description
1. Encryption & Data Layer
Encrypts contributed data and anchors its proof of existence.
2. Metadata & Provenance Layer
Records dataset or model attributes, versioning, and authorship on-chain.
3. Smart Contract Layer
Governs DAT minting, validation, transfers, and settlement logic.
4. Verification Layer (TEE/ZK)
Verifies authenticity through trusted execution or zero-knowledge proofs.
5. Tokenization & Economy Layer
Issues semi-fungible DATs that encode ownership, usage, and value participation.
📘 Learn more: View the Architecture →
Lifecycle Overview
DATs follow a transparent and programmable lifecycle:
Create Class — Define an AI asset category (dataset, model, or agent).
Contribute Data — Upload and encrypt data, storing metadata in decentralized storage.
Mint DAT — Bind ownership and usage rights to a token.
Invoke Service — Use DATs to access or call AI services.
Distribute Rewards — Automatically split revenue based on shareRatio.
Expire or Renew — Handle time-bound access or licensing renewal.
📘 Learn more: See Lifecycle & Value Semantics →
Security & Privacy Principles
The DAT standard integrates privacy-preserving computation and cryptographic guarantees to protect sensitive AI data.
Security Component
Description
Encryption at Source
Data is encrypted locally before upload using hybrid AES–RSA keys.
TEE Verification
Trusted enclaves validate computation integrity without exposing raw data.
Zero-Knowledge Proofs (ZKP)
Optional layer for verifying claims or usage without revealing private details.
Access Control Policies
Enforced on-chain to prevent unauthorized dataset or model invocation.
📘 Learn more: Explore Security & Privacy Model →
Design Highlights
Feature
Description
Composable Data Assets
Combine or split data ownership across classes and users.
Royalty-Backed Tokenization
Link AI model or dataset revenue directly to token holders.
Programmable Usage Rights
Define dynamic access rules, quotas, or billing models.
Interoperable with EVM
Fully compatible with standard Ethereum tools and smart contracts.
Developer Roadmap
Step
Action
1
Learn the DAT Architecture →
2
Understand Lifecycle & Value Semantics →
3
Review Security & Privacy Model →
4
Implement your first DAT using the Developer Implementation Guide →
Summary
The Concepts & Architecture layer provides developers with a clear understanding of how DAT integrates cryptography, smart contracts, and token economics to create a verifiable and monetizable AI data framework.
Together, these concepts enable a scalable foundation for the decentralized AI economy built on LazAI.
Last updated