After years building AI systems for healthcare and LLMs, I’ve found my most challenging and impactful work yet: blockchain security and compliance at Range.org.
The Transition to Blockchain
Moving from traditional ML to blockchain required learning entirely new paradigms:
- Healthcare AI: Structured data, regulated environments, batch processing
- Blockchain ML: Graph data, pseudonymous actors, real-time requirements
The shift wasn’t just technical—it was philosophical. In blockchain, trust is code, and every system must be designed for adversarial environments.
What Makes Blockchain ML Different?
1. Data Complexity
Traditional datasets have rows and columns. Blockchain has transaction graphs where:
- Nodes represent addresses (often pseudonymous)
- Edges are value transfers with complex metadata
- Patterns evolve as attackers adapt
2. Real-Time Requirements
Healthcare AI can process overnight. Compliance can’t wait:
- Sub-second detection for suspicious transactions
- Real-time blocking of high-risk transfers
- Continuous monitoring across 21+ blockchains
3. Adversarial Environment
Unlike clinical data, blockchain actors actively try to evade detection:
- Obfuscation techniques like mixing services
- Smart contract exploits requiring protocol-specific knowledge
- Cross-chain complexity to hide transaction trails
Building at Range.org
At Range, we’ve built ML systems that monitor $30B+ in assets across 730M+ transactions. Key challenges:
Cross-Chain Intelligence
Each blockchain speaks a different language:
- Ethereum: Complex smart contracts
- Cosmos: IBC cross-chain transfers
- Solana: High-speed, low-cost transactions
Our ML models must understand all of them.
Stablecoin Compliance
USDC transfers via CCTP require specialized monitoring:
- Cross-chain tracking of the same asset
- KYT/AML compliance across jurisdictions
- Real-time risk scoring for regulatory reporting
DeFi Protocol Security
Each DeFi protocol creates new attack vectors:
- Flash loan exploits
- Oracle manipulation
- Governance attacks
Our models continuously learn new patterns.
The Impact
Building blockchain security isn’t just about technology—it’s about enabling trust in decentralized systems. When we prevent a hack or detect money laundering, we’re protecting not just individual users but the entire ecosystem.
What’s Next?
The blockchain space evolves daily. We’re pushing the boundaries with:
- Multi-modal AI combining on-chain and off-chain data
- Graph neural networks for complex relationship analysis
- LLM integration for natural language compliance reporting
Every day brings new challenges, but that’s what makes this work so rewarding.
Building the future of blockchain security at Range.org. Connect with me on LinkedIn to discuss blockchain ML challenges.