The role of AI in enhancing smart contract security is increasingly crucial as these self-executing agreements on blockchain platforms like Ethereum face various vulnerabilities, leading to potential financial losses and data breaches. AI and machine learning (ML) models are now employed to detect anomalies and flaws in smart contracts, offering a more efficient alternative to traditional, time-consuming code review and verification methods. These technologies automate the identification of vulnerabilities through pattern recognition, continuous monitoring, and natural language processing (NLP) tools. AI techniques such as Graph Neural Networks (GNNs), Transformer Structures/BERT, and Deep Learning (DL) approaches are instrumental in analyzing the interconnected structures of smart contracts to identify potential security threats. The integration of AI not only speeds up the audit process but also enhances the accuracy of identifying and mitigating vulnerabilities, thereby strengthening smart contract security. Despite AI’s significant contributions, human expertise remains indispensable in designing and interpreting AI-powered audits. As the decentralized finance (DeFi) ecosystem evolves, the reliance on AI for smart contract security signifies a promising advancement towards safeguarding transactions and user data in the blockchain space. 🤖💼🔒
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