Recently, 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 pointed out that AI performs well in threat detection and smart contract auditing, and can significantly enhance the security of blockchain networks; however, if it is over-reliant or improperly integrated, it may not only violate the decentralized principle of Web3.0, but also open up opportunities for hackers.
Dr. Wang emphasized that AI is not a panacea to replace human judgment, but an important tool to collaborate with human wisdom. AI needs to be combined with human supervision and applied in a transparent and auditable manner to balance the needs of security and decentralization. CertiK will continue to lead in this direction and contribute to building a more secure, transparent, and decentralized Web3.0 world.
The following is the full text of the article:
Web 3.0 needs AI — but improper integration could undermine its core principles
Key points:
AI significantly improves the security of Web 3.0 through real-time threat detection and automated smart contract auditing.
Risks include over-reliance on AI and the potential for hackers to exploit the same technology to launch attacks.
Adopt a balanced strategy combining AI and human supervision to ensure that security measures are consistent with the decentralized principles of Web 3.0.
Web 3.0 technologies are reshaping the digital world, driving the development of decentralized finance, smart contracts, and blockchain-based identity systems, but these advances also bring complex security and operational challenges.
Security issues in the digital asset space have long been a concern, and as cyberattacks become more sophisticated, this pain point has become more urgent.
AI undoubtedly has great potential in the field of cybersecurity. Machine learning algorithms and deep learning models excel in pattern recognition, anomaly detection, and predictive analysis, capabilities that are critical to protecting blockchain networks.
AI-based solutions have begun to improve security by detecting malicious activity 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 signs.
This proactive defense approach offers significant advantages over traditional reactive response measures, which typically take action only after a breach has already occurred.
In addition, 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 extremely vulnerable to errors and vulnerabilities.
AI tools are being used to automate the audit process, checking for vulnerabilities in code that might be overlooked by human auditors.
These systems can quickly scan large, complex smart contracts and dApp code bases, ensuring projects are launched with greater security.
The risks of AI in Web3.0 security
Despite its many benefits, AI’s application in Web 3.0 security also has its drawbacks. While AI’s anomaly detection capabilities are extremely valuable, there is also a risk of over-reliance on automated systems that may not always capture all the subtleties of cyberattacks.
After all, an AI system is only as good as its training data.
If malicious actors are able to manipulate or deceive AI models, they could exploit these vulnerabilities to bypass security measures. For example, hackers could use AI to launch highly sophisticated phishing attacks or tamper with the behavior of smart contracts.
This could trigger a dangerous game of cat and mouse, with hackers and security teams using the same cutting-edge technology and the balance of power between the two sides potentially shifting unpredictably.
The decentralized nature of Web 3.0 also presents unique challenges for integrating AI into security frameworks. In a decentralized network, control is spread across multiple nodes and participants, making it difficult to ensure the uniformity required for AI systems to operate effectively.
Web3.0 is inherently fragmented, and the centralized nature of AI (which typically relies on cloud servers and large data sets) may conflict with the decentralized concept advocated by Web3.0.
If AI tools fail to integrate seamlessly into the decentralized web, they could undermine the core principles of Web 3.0.
Human Supervision vs Machine Learning
Another issue worth paying attention to 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 key decisions. Machine learning algorithms can detect vulnerabilities, but they may not have the required ethical or situational awareness when making decisions that affect user assets or privacy.
In the anonymous and irreversible financial transaction scenarios of Web3.0, this could have far-reaching consequences. For example, if AI mistakenly marks a legitimate transaction as suspicious, it could lead to assets being unfairly frozen. 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
Where do we go from here? Integrating AI and decentralization requires a balance. AI can undoubtedly significantly improve the security of Web3.0, but its application must be combined with human expertise.
Emphasis should be placed on developing AI systems that both enhance security and respect the philosophy of decentralization. For example, blockchain-based AI solutions can be built with decentralized nodes, ensuring that no single party can control or manipulate security protocols.
This will maintain the integrity of Web 3.0 while leveraging AI’s strengths in anomaly detection and threat prevention.
In addition, continuous transparency and public auditing of AI systems are critical. By opening the development process to the broader Web3.0 community, developers can ensure that AI security measures are up to standard and not susceptible to malicious tampering.
The integration of AI in security requires collaboration among multiple parties—developers, users, and security experts—to build trust and ensure accountability.
AI is a tool, not a panacea
The role of AI in Web3.0 security is undoubtedly full of promise and potential. From real-time threat detection to automated auditing, AI can improve the Web3.0 ecosystem by providing powerful security solutions. However, it is not without risks.
Over-reliance on AI, and its potential for malicious use, requires caution.
Ultimately, AI should not be seen as a panacea, but as a powerful tool that works in tandem with human intelligence to safeguard the future of Web 3.0.
