Opportunity or hidden worries? Deeply analyze the duality of AI in Web3.0

Recently, blockchain media CCN published an article by Dr. Wang Tielei, Chief Security Officer of CertiK, to deeply analyze the duality of AI in the Web3.0 security system. The article points out that AI performs well in threat detection and smart contract audits, which can significantly enhance the security of blockchain networks; however, over-reliance or incorrect integration may not only contradict the decentralization principle of Web3.0, but may also open opportunities for hackers to take advantage of.

Dr. Wang emphasized that AI is not a "panacea" that replaces human judgment, but an important tool to coordinate human wisdom. AI needs to be combined with human supervision and applied in a transparent and auditable way to balance the needs of security and decentralization. CertiK will continue to lead this direction and contribute to building a safer, transparent, and decentralized Web3.0 world.

The following is the full text of the article:

Web3.0 requires AI – but if integrated improperly, it may undermine its

core principles

Core points:

• Through real-time threat detection and automated smart contract audits, AI significantly improves the security of Web3.0.

• Risks include over-reliance on AI and the possibility that hackers can use the same technology to launch attacks.

• Adopt a balanced strategy that combines AI with human supervision to ensure that security measures comply with the decentralized principles of Web3.0.

Web3.0 technology is reshaping the digital world and driving the development of decentralized finance, smart contracts and blockchain-based identity systems, but these advancements have also brought complex security and operational challenges.

Security issues in the digital asset space have long been worrying. As cyber attacks become increasingly 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 at pattern recognition, anomaly detection and predictive analysis, which are essential to protecting blockchain networks.

AI-based solutions have begun to improve security by detecting malicious activity faster and more accurate than manual teams.

For example, AI can identify potential vulnerabilities by analyzing blockchain data and transaction patterns and predict attacks by discovering early warning signals.

This active defense approach has a significant advantage over traditional passive response measures, which usually only take action after a vulnerability has occurred.

In addition, AI-driven auditing is becoming the cornerstone of the Web3.0 security protocol. 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 audit processes to check for vulnerabilities in the code that may be overlooked by human auditors.

These systems can quickly scan complex large smart contracts and dApp code bases to ensure projects start with higher security.

The risks of AI in Web3.0 security

Despite the numerous benefits, AI's application in Web3.0 security also has shortcomings. Although AI's anomaly detection capabilities are of great value, there is also the risk of over-reliance on automated systems that may not always capture all the subtleties of cyber attacks.

After all, the performance of an AI system depends entirely on its training data.

If malicious actors can manipulate or deceive AI models, they may exploit these vulnerabilities to bypass security measures. For example, hackers can launch highly complex phishing attacks or tamper with smart contracts through AI.

This could trigger a dangerous "cat and mouse game" where hackers and security teams use the same cutting-edge technology, and the power balance between the two sides may change unpredictably.

The decentralized nature of Web3.0 also brings unique challenges to the integration of AI into security frameworks. In a decentralized network, control is scattered across multiple nodes and participants, making it difficult to ensure the uniformity required for the effective operation of the AI ​​system.

Web3.0 is naturally fragmented, and the centralized features of AI (usually relying on cloud servers and large data sets) may conflict with the decentralization philosophy advocated by Web3.0.

If AI tools fail to seamlessly integrate into decentralized networks, it may undermine the core principles of Web3.0.

Human Supervisory 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 cybersecurity, the less human supervision of critical decisions will be. Machine learning algorithms can detect vulnerabilities, but they do not necessarily have the ethical or situational awareness they need when making decisions that affect user assets or privacy.

In the anonymous and irreversible financial transaction scenario of Web3.0, this may lead to far-reaching consequences. For example, if AI mistakenly marks legal transactions as suspicious, it may lead to unfairly freezing of assets. As AI systems become increasingly important in Web3.0 security, manual supervision must be retained to correct errors or interpret vague situations.

AI and decentralized integration

Where should we go? 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.

The focus should be on developing AI systems that enhance security and respect the concept of decentralization. For example, blockchain-based AI solutions can be built through decentralized nodes, ensuring that no single party can control or manipulate security protocols.

This will maintain the integrity of Web3.0 while leveraging AI's advantages in anomaly detection and threat prevention.

In addition, the continuous transparency and public audit of AI systems are crucial. By opening up the development process to the wider Web3.0 community, developers can ensure that AI security measures meet standards and are not susceptible to malicious tampering.

The integration of AI in the security field requires collaboration among multiple parties - developers, users and security experts need to jointly build trust and ensure accountability.

AI is a tool, not a panacea

The role of AI in Web3.0 security is undoubtedly full of prospects and potential. From real-time threat detection to automated audits, AI can perfect the Web3.0 ecosystem by providing powerful security solutions. However, it is not without risks.

Over-reliance on AI, as well as potentially malicious exploits, requires us to be cautious.

Ultimately, AI should not be regarded as a universal antidote, but should be a powerful tool to collaborate with human intelligence to jointly protect the future of Web3.0.