Opportunities or Hidden Concerns? A Depth Analysis of the Dual Nature of AI in Web3.0

Recently, the blockchain media CCN published an article by Dr. Wang Tielei, the Chief Security Officer of CertiK, which delves into the dual nature of AI in the Web3.0 security system. The article points out that AI performs exceptionally well in threat detection and smart contract audit, significantly enhancing the security of blockchain networks; however, if overly relied upon or improperly integrated, it could not only contradict the decentralization principles of Web3.0 but also open up opportunities for hackers.

Dr. Wang emphasized that AI is not a “cure-all” to replace human judgment, but an important tool to collaborate with human intelligence. AI needs to be combined with human supervision and applied in a transparent and auditable manner to balance safety and decentralization needs. CertiK will continue to lead in this direction, contributing to the creation of a safer, more transparent, and decentralized Web3.0 world.

The following is the full text of the article:

Web3.0 needs AI - but if integrated improperly, it may harm its core principles.

Key points:

• Through real-time threat detection and automated smart contract audit, AI significantly enhances the security of Web3.0.
• Risks include over-reliance on AI and hackers potentially using the same technology to launch attacks.
• Adopt a balanced strategy that combines AI and human supervision to ensure that safety measures align with the decentralization principles of Web3.0.

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 space have been a concern. With cyber attacks becoming increasingly sophisticated, this pain point has become more urgent.

AI undoubtedly has huge potential in the field of cybersecurity. Machine learning algorithms and deep learning models excel at pattern recognition, anomaly detection, and predictive analysis, which are crucial for protecting Blockchain networks.

AI-based solutions have begun to detect malicious activities faster and more accurately than human teams, enhancing security.

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

This proactive defense approach has significant advantages over traditional passive response measures, which typically only take action after a vulnerability has occurred.

In addition, AI-driven audits are 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 vulnerabilities in the code that may be overlooked by human auditors.

These systems can quickly scan complex large smart contracts and dApp codebases to ensure that projects launch with higher security.

The risks of AI in Web3.0 security

Despite the numerous benefits, the application of AI in Web3.0 security also has its flaws. While AI’s anomaly detection capabilities are extremely valuable, there is also the risk of over-reliance on automated systems, which may not always capture all the nuances of cyber attacks.

After all, the performance of AI systems completely depends on their training data.

If malicious actors can manipulate or deceive AI models, they may exploit these vulnerabilities to bypass security measures. For example, hackers could launch highly sophisticated phishing attacks or alter the behavior of smart contracts through AI.

This could trigger a dangerous “cat-and-mouse game,” where hackers and security teams use the same cutting-edge technology, leading to unpredictable shifts in the balance of power between the two sides.

The decentralized nature of Web3.0 also presents unique challenges for the integration of AI into security frameworks. In decentralized networks, control is distributed across multiple nodes and participants, making it difficult to ensure the uniformity required for AI systems to operate effectively.

Web3.0 is inherently fragmented, while the centralized characteristics of AI (often reliant on cloud servers and large datasets) may conflict with the decentralization principles advocated by Web3.0.

If AI tools cannot seamlessly integrate into decentralized networks, it may undermine the core principles of Web3.0.

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 cybersecurity, the less human oversight there is over critical decisions. Machine learning algorithms can detect vulnerabilities, but they may not possess the necessary moral or contextual awareness when making decisions that affect user assets or privacy.

In the context of anonymous and irreversible financial transactions in Web3.0, this could lead to far-reaching consequences. For example, if AI mistakenly marks a legitimate transaction as suspicious, it could result in unjust asset freezes. As AI systems become increasingly important in Web3.0 security, human oversight must be retained to correct errors or interpret ambiguous situations.

AI and Decentralization Integration

Where should we go from here? Integrating AI with Decentralization requires a balance. AI undoubtedly can significantly enhance 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 while respecting the principles of Decentralization. For example, blockchain-based AI solutions can be built through decentralized nodes, ensuring that no single party can control or manipulate the 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 the development process to a wider Web3.0 community, developers can ensure that AI security measures meet standards and are not easily susceptible to malicious tampering.

The integration of AI in the security field requires multi-party collaboration—developers, users, and security experts must work together to establish trust and ensure accountability.

AI is a tool, not a cure-all.

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 enhance the Web3.0 ecosystem by providing robust security solutions. However, it is not without risks.

Over-reliance on AI, as well as potential malicious use, requires us to remain cautious.

Ultimately, AI should not be seen as a panacea, but as a powerful tool that collaborates with human intelligence to jointly safeguard the future of Web3.0.