Security Startup Cube3.ai Raises $8.2 Million to Scale Up Transaction Security

Summary:
• Cube3.ai offers protection for transactions on Ethereum, BNB Blockchain, Polygon and Arbitrum; and will add Avalanche at the end of the month.

Cube3.ai, a crypto-centric security startup, has secured $8.2 million in seed funding to protect smart contracts from fraudulent transactions. Multiple investors contributed to the seed round, including Dispersion Capital, Symbolic Capital, Hypersphere Ventures, ICLUB, and TA Ventures. Cube3 says it plans to invest the money in expanding the platform to improve web3 security and in spreading awareness about the dangers of weak transaction security.

How Cube3.ai secures transactions

Cube3.ai has invested in machine learning, which it employs to prevent malicious blockchain transactions in real-time. As of now, the company provides protection against attacks on Ethereum, BNB Smart Chain, Arbitrum, and Polygon blockchains. In addition, it plans to add support for Avalanche later this month. The company’s core offerings may be broken down into three categories: Detect, Protect, and Manage.

• Detect: Each smart contract, wallet, and transaction is evaluated for cyber risks, fraud, and compliance using algorithms available in the Detect application. In the case that a suspicious transaction is identified, the algorithm will notify the business so that it may use a smart contract to block the transaction.
• Protect: The “Protect” function keeps an eye on the smart contract code layer, the user interface, and any external entities that pose a higher risk than the organisation can tolerate.
• Manage: This functionality makes it possible for protocols to keep tabs on wallets, view analytics and data, and send out notifications using third-party applications.

Those who deploy smart contracts will now be able to prevent malicious transactions in real time, ensuring that their apps will continue to function normally even as they deal with security issues. Cube3 also promises to continue working with its clients to prioritise new features and improve its machine learning algorithms through consultation and white hat hacking.