Blockchain security firm AnChain.ai is leveraging the recent artificial intelligence (AI) hype to detect crypto scams and illicit blockchain fund flows.
The firm’s CEO, Dr. Victor Fang, hopes a new framework called Web3 SOC will reduce the seven-day detection window for 2022’s smart contract exploits.
How Blockchain Security Firms Monitor Smart Contracts
The company wants to close the gap between Web3 and traditional cybersecurity breaches. Web3 projects only respond to hacks almost 40 days after the exploit, compared to the five-hour response time for conventional cyber breaches.
AnChain.ai agrees that the smart contract audit is the first step of a comprehensive risk management framework. It uses machine learning to detect smart contract anomalies, together with a multichain analytics platform.
According to Fang, funds lost to smart contract exploits in 2022 totaled $4 billion, costing victims about $200 million per incident.
Notable hacks include Harmony’s Horizon Bridge exploit, which cost victims over $600 million, and the $566 million Binance Smart Chain hack.
According to AnChain.ai competitor Cyvers.ai’s Web3 security report, smart contract audits are insufficient to prevent crypto scams. Audited smart contracts were involved in around 52% of hacks. Additionally, human error, including compromised keys, accounted for 30% of 2022 Web3 hacks.
AnChain.ai also helps money laundering authorities detect whether companies violate the Bank Secrecy Act. The Internal Revenue Service uses the firm’s technology to examine digital asset filings and white-collar crime.
AI Can Also Flag Suspicious Blockchain Activity
On a corporate cybersecurity level, artificial intelligence can detect deviations from a company’s normal network activity. Companies are using firewalls and artificial intelligence to keep threat actors at bay. Cyvers.ai advocates real-time on-chain and off-chain analysis with alert systems for critical smart contracts or wallet addresses
Crypto scams often convert stolen funds to censorship-resistant cryptocurrency. Hackers later pass the funds through a mixer, like Tornado Cash, to break the links between the funds’ source and destinations.
Forensic firms like Elliptic, Chainalysis, and PeckShield can provide transaction data to train AI models to detect suspicious transactions. Cyvers.ai uses geometric machine learning to learn typical crypto user behaviors and identify anomalies.
Firms developing AI cybersecurity tools say the first goal is to reduce false positives. Acumen Research estimates that the global market for AI cybersecurity will reach $133.8 billion by 2030.
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