ChainTrust is bringing its real-time AML screening and risk intelligence tools to Sui Network, marking the Layer 1 blockchain’s latest move to bolster its compliance infrastructure. The integration pairs Sui’s high-throughput architecture with ChainTrust’s AI-driven monitoring capabilities, a combination designed to catch illicit activity before it metastasizes across the network.

ChainTrust Labs isn’t a household name, but its pedigree is hard to ignore. The company’s leadership team includes former Alipay executives with over 20 years of experience in AI and risk modeling. The firm’s product suite spans real-time address screening, transaction monitoring, and risk scoring, all powered by machine learning models trained on blockchain-specific data. ChainTrust currently serves more than 35 blockchains and claims a database covering over 1 billion digital assets.

By integrating these tools directly into Sui’s ecosystem, developers and protocols building on the network gain access to compliance screening without having to source and integrate third-party AML solutions independently.

Sui’s growing compliance playbook

This isn’t Sui’s first compliance-focused partnership. In January 2025, the Sui Foundation announced a collaboration with Chainalysis, the blockchain analytics giant, to enhance on-chain compliance and security. That partnership focused on tracking illicit activities across the network, with Chainalysis expanding its tracking capabilities for SUI tokens and other fungible assets on the chain.

The Chainalysis deal was primarily about surveillance and forensics: seeing what happened and tracing where funds went. ChainTrust’s integration appears oriented more toward prevention, screening transactions and addresses in real time before problems escalate.

Sui, developed by Mysten Labs, has positioned itself as a scalability-first Layer 1 with ambitions to attract institutional-grade applications.

Why AI-driven AML is becoming the standard

Traditional AML systems work on predefined rules: flag transactions above a certain threshold, block addresses on a sanctions list. These approaches catch the obvious stuff but miss the creative stuff. AI models can detect anomalous patterns, cluster related wallets, and score risk dynamically based on behavioral signals that no human-written ruleset would capture.

ChainTrust’s Alipay heritage is particularly relevant here. Alipay processes billions of transactions and has spent years refining AI models for fraud detection in a high-volume, adversarial environment.

The risk to watch is execution. Integrating real-time screening without introducing latency or false positives that degrade the user experience is genuinely difficult. How ChainTrust’s models perform under Sui’s transaction throughput will be the real test.