Recently, the blockchain media CCN published an article by Dr. Wang Tielei, Chief Security Officer of CertiK, which deeply analyzed the duality of AI in the Web3.0 security system. The article points out that AI performs excellently in threat detection and smart contract auditing, significantly enhancing the security of blockchain networks; however, if overly relied upon or improperly integrated, it may not only contradict the decentralization principles of Web3.0 but also create opportunities for hackers.
Dr. Wang emphasized that AI is not a "panacea" replacing human judgment, but an important tool that collaborates with human intelligence. AI needs to be combined with human supervision and applied in a transparent, auditable manner to balance security and decentralization needs. CertiK will continue to lead in this direction, contributing to the construction of a more secure, transparent, and decentralized Web3.0 world.
The following is the full article:
Web3.0 Needs AI—But Improper Integration Could Compromise Its Core Principles
Key Points:
AI significantly enhances Web3.0 security through real-time threat detection and automated smart contract auditing.
Risks include over-reliance on AI and potential exploitation by hackers using similar technologies.
Adopt a balanced strategy combining AI with human oversight to ensure security measures align with Web3.0's decentralization principles.
Web3.0 technology is reshaping the digital world, driving the development of decentralized finance, smart contracts, and blockchain-based identity systems, but these advancements also bring complex security and operational challenges.
For a long time, security issues in the digital asset domain have been a concern. As cyber attacks become increasingly sophisticated, this pain point has become more urgent.
AI undoubtedly has enormous potential in cybersecurity. Machine learning algorithms and deep learning models excel at pattern recognition, anomaly detection, and predictive analysis, capabilities crucial for protecting blockchain networks.
AI-based solutions have already begun to improve security by detecting malicious activities faster and more accurately than human teams.
For example, AI can identify potential vulnerabilities by analyzing blockchain data and transaction patterns, and predict attacks by discovering early warning signals.
This proactive defense approach has significant advantages over traditional passive response measures, which typically only act after vulnerabilities have occurred.
Moreover, AI-driven auditing is becoming the cornerstone of Web3.0 security protocols. Decentralized applications (dApps) and smart contracts are the two pillars of Web3.0, but they are highly susceptible to errors and vulnerabilities.
AI tools are being used to automate audit processes, checking for code vulnerabilities that might be overlooked by human auditors.
These systems can quickly scan complex, large-scale smart contract and dApp code bases, ensuring projects launch with higher security.
Risks of AI in Web3.0 Security
Despite numerous benefits, AI's application in Web3.0 security also has flaws. While AI's anomaly detection capabilities are highly valuable, there is a risk of over-relying on automated systems that may not always capture all the nuanced aspects of cyber attacks.
After all, AI system performance entirely depends on its training data.
If malicious actors can manipulate or deceive AI models, they might exploit these vulnerabilities to bypass security measures. For instance, hackers could launch highly sophisticated phishing attacks or tamper with smart contract behaviors using AI.
This could trigger a dangerous "cat and mouse game" where hackers and security teams use the same cutting-edge technologies, with potentially unpredictable shifts in power dynamics.
Web3.0's decentralized nature also brings unique challenges to AI integration into security frameworks. In decentralized networks, control is distributed across multiple nodes and participants, making it difficult to ensure the uniformity required for effective AI system operation.
Web3.0 inherently has fragmented characteristics, while AI's centralized nature (often relying on cloud servers and large datasets) may conflict with the decentralization principles championed by Web3.0.
If AI tools fail to seamlessly integrate into decentralized networks, they might undermine Web3.0's core principles.
Human Supervision vs Machine Learning
Another issue worth noting is the ethical dimension of AI in Web3.0 security. The more we rely on AI to manage network security, the less human oversight there is for critical decisions. Machine learning algorithms can detect vulnerabilities, but they may lack the necessary moral or contextual awareness when making decisions that impact user assets or privacy.
In Web3.0's anonymous and irreversible financial transaction scenarios, this could have far-reaching consequences. For example, if AI incorrectly flags a legitimate transaction as suspicious, it could lead to unjust asset freezing. As AI systems become increasingly important in Web3.0 security, human supervision must be retained to correct errors or interpret ambiguous situations.
AI and Decentralization Integration
What is our path forward? Integrating AI with decentralization requires balance. AI can undoubtedly significantly enhance Web3.0 security, but its application must be combined with human expertise.
The focus should be on developing AI systems that both enhance security and respect decentralization principles. For instance, blockchain-based AI solutions can be built using decentralized nodes, ensuring no single party can control or manipulate security protocols.
This will maintain Web3.0's integrity while leveraging AI's strengths in anomaly detection and threat prevention.
Additionally, continuous transparency and public auditing of AI systems are crucial. By opening the development process to the broader Web3.0 community, developers can ensure AI security measures meet standards and are not easily susceptible to malicious tampering.
AI integration in security requires multi-party collaboration—developers, users, and security experts must collectively establish trust and ensure accountability.
AI is a Tool, Not a Panacea
AI's role in Web3.0 security is undoubtedly full of prospects and potential. From real-time threat detection to automated auditing, AI can refine the Web3.0 ecosystem by providing robust security solutions. However, it is not without risks.
Over-reliance on AI and potential malicious exploitation demand our caution.
Ultimately, AI should not be viewed as a universal remedy but as a powerful tool that collaborates with human intelligence to safeguard Web3.0's future.
