AI agents urgently need identification, zk-SNARKs provide the solution.

Zero-knowledge proofs (ZKPs) are expected to become a pillar of a new era of trustworthy AI and digital identity, providing individuals and organizations with a secure and transparent way to interact across platforms and borders.

This is an “interesting” era for AI and trust.

Currently, more and more investment companies are starting to use AI agents to review research reports and company documents. Meanwhile, humans are required to provide increasingly strict biometric data, such as facial scans, voice samples, and behavior patterns, just to prove that they are not robots. Once this data is leaked, it could be maliciously exploited by AI-driven robots masquerading with highly realistic identities to impersonate real people, thereby breaching the systems designed to protect against them. This has led us into a bizarre new arms race— the deeper the data required by verification methods, the more severe the damage if it leaks. So, how exactly should we confirm who (or what) we are interacting with?

It is illogical to require humans to maintain transparency while accepting the opaque operations of machines. Both robots and online users need better identification methods. To solve this problem, we cannot rely on endlessly collecting more and deeper biometric data, nor can we depend on establishing centralized databases (which are like “honey pots” for hackers). zk-SNARKs point us to a way out, allowing both AI and humans to effectively verify identification while protecting their own safety.

Trust Deficit Hindering Development

The lack of verifiable AI identification poses direct market risks. When AI agents can impersonate humans, manipulate markets, or execute unauthorized transactions, companies will naturally be cautious about whether to deploy automated systems on a large scale. In fact, large language models that are “fine-tuned” on small datasets to enhance performance have a 22 times higher probability of generating harmful outputs compared to the base models; moreover, their success rate in bypassing system security and ethical safeguards (a process known as “jailbreaking”) increases to three times when facing production-ready systems. Without reliable identification verification, every AI interaction is a step closer to potential security vulnerabilities.

The complexity of the issue goes far beyond this. It is not as simple and direct as preventing malicious actors from deploying illegal agents, because we are not facing a single AI interface. In the future, there will be more and more autonomous AI agents with increasingly powerful capabilities. In this ocean of agents, how can we accurately identify who we are interacting with? Even legitimate AI systems need to have verifiable credentials in order to participate in the emerging agent economy. For example, when one AI robot trades with another robot, both parties must be able to confirm the authenticity of each other's identity, the scope of operational authorization, and a clear framework of responsibility.

The human side of this equation is equally fraught with issues. Traditional identification systems not only expose users to the risk of large-scale data breaches, but also easily foster authoritarian surveillance, while allowing large companies to profit by selling personal information that rightfully belongs to users, generating billions of dollars in revenue—these come from the users themselves, yet they receive not a penny in compensation. Therefore, people instinctively resist sharing more personal data, while the evolution of verification technology continues to demand deeper layers of personal information.

zk-SNARKs: A Bridge Between Privacy and Accountability

zk-SNARKs provide a solution to this seemingly tricky problem. ZKPs allow entities (whether human or artificial intelligence) to verify specific claims without revealing underlying data, while not directly disclosing sensitive information. For example, users can prove they are over 21 years old without revealing their date of birth; AI agents can prove that their training data meets ethical standards without exposing proprietary algorithms; financial institutions can verify that customers meet regulatory requirements without storing personal information that could be leaked.

For AI agents, ZKPs can establish the necessary depth of trust mechanisms, as what we need to verify is not only the technical architecture but also behavioral patterns, legal accountability, and social reputation. With the help of ZKPs, these verification statements can be stored on-chain in the form of verifiable trust graphs.

We can regard it as a composable identification layer that operates across platforms and jurisdictions. When AI agents present credentials, they can prove that their training data meets ethical standards, that the output has been audited, and that all actions are bound to accountable human entities without disclosing proprietary information.

ZKPs have the potential to completely change the existing model, allowing us to complete identification without needing to hand over sensitive data, but the current application of the technology is still slow to spread. This technology still belongs to a niche field, with low user awareness, and the corresponding regulatory framework has yet to be clarified. More critically, companies that profit from data collection lack the incentive to adopt this technology. However, this has not prevented more flexible identification companies from using it. As regulatory standards gradually become clearer and public awareness increases, ZKPs are expected to become a pillar of a new era of trusted AI and digital identity—providing individuals and organizations with secure and transparent interactions across platforms and borders.

Market Impact: Unlocking the Smart Agent Economy

Generative AI creates trillions of dollars in value for the global economy each year, but much of this value remains locked due to barriers in identification. The main reasons are threefold: First, institutional investors need to complete rigorous KYC/AML compliance reviews before investing in AI-driven strategies; second, businesses require verifiable agent identification before allowing autonomous systems to access critical infrastructure; third, regulatory bodies must have robust accountability mechanisms in place before approving AI applications in sensitive areas.

The identity system based on zk-SNARKs meets all these requirements while still retaining the advantages of privacy protection and autonomy that decentralized systems rely on. By implementing a selective disclosure mechanism, it can meet regulatory requirements while avoiding the creation of easily attackable personal data sets. Through cryptographic verification technology, it enables autonomous agents to establish an interaction environment without pre-established trust; while the user control guarantee mechanism naturally aligns with the core principles of emerging data protection regulations such as GDPR and the California Privacy Law.

This technology also helps address the increasingly severe deepfake crisis. When every piece of content can be cryptographically linked to a verified creator without revealing their true identity, we can effectively curb the spread of misinformation while protecting privacy. As the lines between AI-generated content and human creations become increasingly blurred, this technological mechanism becomes particularly important.

The Road of ZK

While some may argue that any identification system is a step toward authoritarianism, without a civil identification mechanism, no society can function. The reality is that identification verification is already widely implemented, but the effectiveness of its execution is concerning. Each time we upload documents to meet KYC requirements, undergo facial recognition scans, or submit personal data for age verification, we are participating in an intrusive identification system that poses security risks and is inefficient.

zk-SNARKs provide a path forward that fully respects personal privacy while establishing the necessary trust for complex economic activities. This technology enables us to build systems in which users truly control their own data, and the verification process does not rely on monitoring means. Both humans and AI agents can achieve secure interactions while maintaining their respective autonomy.