Polyhedra CTO Highlights ZKML's Role in Securing AI Services

Polyhedra's Chief Technology Officer (CTO), Xie Tiancheng, recently spoke at the "2025 Web3 x AI Innovation Forum" about the transformative potential of Zero-Knowledge Machine Learning (ZKML) technology in verifying AI service quality and ensuring the security of AI agents. The event was co-hosted by the Tsinghua University Student Blockchain Association and Polyhedra.

Xie Tiancheng emphasized that ZKML's primary value in AI model applications lies in its ability to verify service quality without compromising data privacy. He illustrated this with an example from the trading sector, where users can employ ZKML to validate the performance of trading bots, ensuring that the AI-driven decisions are reliable and trustworthy. This capability is particularly significant in fields where the integrity of AI services is crucial for maintaining client trust and stakeholder confidence.

As AI agents become more prevalent and are granted sensitive permissions, ZKML can play a critical role in securing these agents. By enabling users to act as validators, ZKML establishes a trust system between humans and AI agents. This not only enhances security but also ensures that AI agents operate within the parameters set by their human counterparts, thereby protecting sensitive information and maintaining system integrity.

Xie Tiancheng also highlighted additional functions that ZKML can provide, such as creating a data marketplace and auditing training data. These features are expected to be instrumental in the future development of on-chain AI, further solidifying ZKML's role in the AI landscape.

The CTO's insights underscore the growing importance of ZKML in the AI industry. As AI technologies continue to advance and permeate various sectors, the need for robust verification and security measures becomes increasingly critical. ZKML offers a solution that not only verifies the quality of AI services but also ensures that AI agents operate securely, protecting sensitive information and maintaining the integrity of the systems they interact with.

The application of ZKML extends beyond trading to other sectors where AI is used for critical decision-making, such as healthcare, finance, and cybersecurity. In healthcare, ZKML can verify the accuracy of AI-driven diagnoses without exposing patient data. In finance, it can ensure that AI algorithms used for risk assessment and fraud detection are reliable and secure. This versatility makes ZKML a valuable tool in various industries, enhancing the reliability and trustworthiness of AI services.

In conclusion, Xie Tiancheng's emphasis on the role of ZKML in verifying AI service quality and ensuring AI agent security highlights the importance of advanced technologies in the AI landscape. As AI continues to evolve, the adoption of ZKML and similar technologies will be crucial in ensuring that AI services are reliable, secure, and trustworthy. This will not only build trust with clients and stakeholders but also pave the way for more innovative and secure AI applications in the future.

AInvest Newsletter

Daily stocks & crypto headlines, free to your inbox

Get Newsletter with Email

Continue with Google

Or continue with others

Apple

Facebook

X

By continuing, I agree to the Market Data Terms of Service and Privacy Statement