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About Webacy
Webacy is an on-chain risk intelligence platform that positions itself as the ratings agency for digital assets, analogous to S&P or Moody's in traditional finance. Its core business covers continuous risk monitoring and scoring for stablecoins, tokenized real-world assets, yield vaults, and other on-chain financial products, plus wallet and transaction screening. The platform combines code analysis, behavioral intelligence, liquidity and governance monitoring, and machine learning across hundreds of real-time detectors to surface risks like address poisoning, hidden contract permissions, collateral mismarking, or liquidity deterioration before they become incidents.
The market pain point Webacy addresses is that existing tools are fragmented and backward-looking: smart contract auditors review code at a single point in time, security monitors watch for exploits after architecture is deployed, and compliance tools look at past transactions rather than forward risk. None answer the question institutions care about most—whether an asset is fundamentally sound right now and under what conditions it might fail. Webacy provides live ratings, domain breakdowns, exposure maps, and stress-scenario analysis that update continuously as conditions change.
In the past six months (March to September 2026), Webacy launched a completely revamped DD.xyz dashboard, unifying wallet screening, transaction assessment, asset evaluation, and stablecoin intelligence into a single multi-chain interface. New chain support was added including Stellar, Sui, and Hedera. The stablecoin intelligence suite entered beta preview, providing real-time depeg insights across thousands of assets. Webacy also established the Digital Asset Ratings Council (DARC) to develop shared methodology standards across the industry, and retired legacy features like Backup Wallet and Panic Button to focus on due diligence and risk intelligence. No significant negative developments were reported during this period.
Webacy's key progress in the past six months (March 2026 - September 2026):
Real-time stablecoin depeg detection (launched January 2026): Full coverage of the stablecoin ecosystem with real-time depeg risk monitoring.
Structured vault risk scoring (February 2026): Expanded to vault structure risk assessment.
Cascade risk monitor (March 2026): Detects depeg signals, liquidity shocks, and protocol pauses, mapping downstream risk exposure through dependency graphs (vault collateral, lending markets, correlated stablecoins).
MINTER_ROLE monitoring: Tracks stablecoin minting authority grants and revocations, providing early warnings before risk events.
Upgrade caller classification: Identifies whether vault governance actions are executed by EOAs or contracts, with structured multisig configuration and timelock duration data.
DD.xyz Dashboard (March 11, 2026): Unified multi-chain risk dashboard supporting all chains in the Webacy network.
DD SDK (March 2026): TypeScript risk intelligence SDK with out-of-the-box support for 12 major blockchains, providing millisecond-level address scanning, token risk detection, OFAC sanctions screening, and more.
Future development focus:
- Continuously expand the risk intelligence toolkit to cover more chains and detection scenarios
- Deepen stablecoin and vault risk assessment systems
- Drive the DD risk engine to become industry infrastructure
Maika Isogawa: Co-Founder and CEO of Webacy. Holds a BS in Symbolic Systems from Stanford University. Previously worked in cybersecurity at Microsoft. Founded Webacy in 2021 to secure digital assets and provide risk intelligence. Currently also serves as Venture Partner at Dent Capital.
Jeremiah O'Connor: CTO of Webacy. Former Principal Research Scientist at Coinbase and Principal Investigations Scientist at Binance, with earlier security/data science roles at Cisco, Uber, Evernote, and Mandiant. Co-founded blockchain security firm Trugard in 2022; after its acquisition by Webacy, he became CTO in April 2025. Holds a BA in Philosophy and English from UC Davis, an MS in Computer Science from USF, and completed post-master's ML coursework at UC Berkeley.