We live in interesting times for AI and trust. A growing number of investment firms are using AI agents to review research notes and company filings. Humans are being asked to provide increasingly invasive biometric data, like facial scans, voice samples and behavioral patterns, just to prove they’re not robots. Once in the wild, this data can be used by AI-driven bots to convincingly impersonate real people, defeating the very systems designed to keep them out. This leaves us in a strange new arms race: the more invasive the verification, the greater the risk in the event of an inevitable leak. So how do we check who (or what) we are really dealing with?
It is unacceptable to demand transparency from humans while accepting the opacity of machines. Online bots and humans need better ways to verify their identities. We cannot solve this problem by simply collecting more biometric data, nor by creating centralized registries that represent huge honeypots for cybercriminals. Zero-knowledge proofs offer a path forward where humans and AI can prove their credentials without exposing themselves to exploitation.
The trust deficit blocks progress
The lack of a verifiable AI identity creates immediate market risks. When AI agents can impersonate humans, manipulate markets, or execute unauthorized trades, companies are rightly hesitant to deploy autonomous systems at scale. It turns out that LLMs that have been “fine-tuned” on a smaller data set to improve performance are 22 times more likely to produce harmful results than baseline models, with the success rate of bypassing the system’s security and ethics guardrails – a process known as “jailbreaking” – tripling compared to production-ready systems. Without reliable identity verification, every AI interaction moves closer to a potential security breach.
The problem isn’t as obvious as preventing malicious actors from deploying malicious agents, because it’s not like we’re dealing with a single AI interface. The future will see more and more autonomous AI agents with greater capabilities. In such a sea of agents, how do we know what we are dealing with? Even legitimate AI systems need verifiable credentials to participate in the emerging agent-to-agent economy. When an AI trading robot executes a trade with another robot, both parties must have assurance of each other’s identity, authorization, and accountability structure.
The human side of this equation is also broken. Traditional identity verification systems expose users to massive data breaches, enable authoritarian surveillance too easily, and generate billions in revenue for large companies by selling personal information without compensating the individuals who generate it. People are rightly reluctant to share more personal data, but regulatory requirements require ever more invasive verification procedures.
Zero Knowledge: The Bridge Between Privacy and Accountability
Zero-knowledge proofs (ZKP) offer a solution to this seemingly intractable problem. Rather than revealing sensitive information, ZKPs allow entities, whether human or artificial, to prove specific claims without exposing the underlying data. A user can prove that they are over 21 without revealing their date of birth. An AI agent can prove that it was trained on ethical datasets without exposing proprietary algorithms. A financial institution can verify that a customer meets regulatory requirements without storing personal information that could be disclosed.
For AI agents, ZKPs can help achieve the deep levels of trust needed, as we need to verify not only technical architecture, but also behavioral patterns, legal accountability, and social reputation. With ZKPs, these claims can be stored in an on-chain verifiable trust graph.
Think of it as a composable identity layer that works across platforms and jurisdictions. This way, when an AI agent presents their credentials, they can prove that their training data meets ethical standards, that their results have been audited, and that their actions are linked to responsible human entities, all without exposing proprietary information.
ZKPs could be a complete game-changer, allowing us to prove who we are without transmitting sensitive data, but adoption remains slow. ZKPs remain a technical niche, unfamiliar to users, and entangled in regulatory gray areas. To top it off, companies that profit from data collection have little incentive to adopt the technology. However, that doesn’t stop more agile identity companies from leveraging them, and as regulatory standards emerge and awareness improves, ZKPs could become the backbone of a new era of trusted AI and digital identity, providing a way for individuals and organizations to interact securely and seamlessly across platforms and borders.
Market implications: unlocking the agent economy
Generative AI could add billions of dollars each year to the global economy, but much of that value remains locked behind identity verification barriers. There are several reasons for this. The first is that institutional investors need strong KYC/AML compliance before deploying capital into AI-enabled strategies. Another reason is that companies require verifiable agent identities before allowing autonomous systems to access critical infrastructure. And regulators are requiring accountability mechanisms before approving the deployment of AI in sensitive areas.
ZKP-based identity systems meet all of these requirements while preserving the privacy and autonomy that make decentralized systems valuable. By allowing selective disclosure, they satisfy regulatory requirements without creating personal data honeypots. By providing cryptographic verification, they enable trustless interactions between autonomous agents. And by maintaining user control, they align with emerging data protection regulations such as GDPR and California privacy laws.
The technology could also help solve the growing deepfake crisis. When every piece of content can be cryptographically linked to a verified creator without revealing their identity, we can fight misinformation and protect privacy. This is especially crucial as AI-generated content becomes indistinguishable from human-created material.
The ZK path
Some would say that any identity system represents a step toward authoritarianism – but no society can function without a way to identify its citizens. Identity verification is already happening on a large scale, but poorly. Every time we upload documents for KYC, submit to facial recognition, or share personal data to verify age, we are participating in invasive, insecure, and ineffective identity systems.
Zero-knowledge proofs offer a path forward that respects individual privacy while enabling the trust necessary for complex economic interactions. They allow us to create systems where users control their data, verification does not require monitoring, and humans and AI agents can interact securely without sacrificing autonomy.
