These are interesting times for artificial intelligence and trust. A growing number of investment firms are using AI agents to review research notes and corporate documents. Humans are being asked to provide increasingly invasive biometric data, such as facial scans, voice samples and behavioral patterns, just to prove they are not bots. Once in the wild, this data can be weaponized by AI-powered bots to convincingly impersonate real people and defeat the very systems designed to keep them out. That leaves us in a strange new arms race – the more invasive the verification, the greater the risk when it inevitably leaks. So how do we verify who (or what) we’re really dealing with?
It is unconscionable to demand transparency from humans while accepting opacity from machines. Both bots and online people need better ways to verify their identity. We cannot solve this problem by simply collecting more biometric data or by building centralized registries that represent massive honeypots for cybercriminals. Zero-knowledge credentials offer a way forward where both humans and AI can prove their credentials without exposing themselves to exploitation.
Blocking trust deficit
The absence of verifiable AI identity creates immediate market risks. When AI agents can mimic humans, manipulate markets or perform unauthorized transactions, companies are rightly hesitant to deploy autonomous systems at scale. As it happens, LLMs that have been “fine-tuned” on a smaller data set to improve performance are 22 times more likely to produce harmful output than baseline models, with success rates for bypassing the system’s security and ethical railings – a process known as “jailbreaking” – tripled compared to production-ready systems. Without reliable identity verification, every AI interaction takes one step closer to a potential security breach.
The problem is not as obvious as preventing malicious actors from deploying rogue agents, because it is not as if we are facing 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 new agent-to-agent economy. When an AI trading bot executes a transaction with another bot, both parties need assurance about the other’s identity, authorization, and accountability structure.
The human side of this equation is just as broken. Traditional identity verification systems expose users to massive data breaches, all too easily allow for authoritarian surveillance, and generate billions in revenue for big companies from selling personal information without compensating the people who generate it. People are rightly reluctant to share more personal data, but regulatory requirements require increasingly invasive verification procedures.
Zero-Knowledge: The Bridge Between Privacy and Accountability
Zero-knowledge proofs (ZKPs) offer a solution to this seemingly intractable problem. Instead of revealing sensitive information, ZKPs allow entities, whether human or artificial, to prove specific claims without revealing underlying data. A user can prove they are over 21 without revealing their date of birth. An AI agent can prove it was trained on ethical datasets without revealing proprietary algorithms. A financial institution can verify that a customer meets legal requirements without storing personal information that could be violated.
For AI agents, ZKPs can enable the necessary deep levels of trust, 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 a verifiable on-chain trust graph.
Think of it as a composite identity layer that works across platforms and jurisdictions. That way, when an AI agent presents its credentials, it can prove that its training data meets ethical standards, its output has been audited, and its actions are linked to responsible human entities, all without revealing proprietary information.
ZKPs could completely change the game, allowing us to prove who we are without giving away sensitive data, but adoption remains slow. ZKPs remain a technical niche, unknown to users and entangled in regulatory gray areas. On top of that, companies that profit from collecting data have little incentive to adopt the technology. However, that’s not stopping 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 – giving individuals and organizations a way to interact securely and transparently across platforms and borders.
Market Implications: Unleashing the Agency Economy
Generative AI could add trillions annually to the global economy, but much of that value remains locked behind identity verification barriers. There are several reasons for this. One is that institutional investors need robust KYC/AML compliance before committing capital to AI-driven strategies. Another is that companies require verifiable agent identities before allowing autonomous systems to access critical infrastructure. And regulators require accountability mechanisms before approving AI deployment in sensitive domains.
ZKP-based identity systems address all of these requirements while preserving the privacy and autonomy that make decentralized systems valuable. By enabling selective disclosure, they meet regulatory requirements without creating honeypots of personal data. By providing cryptographic verification, they enable trustless interactions between autonomous agents. And by maintaining user control, they adapt to new data protection regulations like GDPR and California privacy laws.
The technology could also help solve the growing deepfake crisis. When each piece of content can be cryptographically linked to a verified creator without revealing their identity, we can fight misinformation and protect privacy. This is particularly important as AI-generated content is indistinguishable from human-made material.
The ZK path
Some would argue that any identity system represents a step towards authoritarianism – but no society can function without a way to identify its citizenry. Identity verification is already happening at scale, just poorly. Every time we upload documents for KYC, submit for facial recognition, or share personal data for age verification, we participate in identity systems that are invasive, insecure, and ineffective.
Zero-knowledge credentials offer a way forward that respects individual privacy while enabling the trust necessary for complex financial interactions. They allow us to build systems where users control their data, verification does not require monitoring, and both humans and AI agents can interact securely without sacrificing autonomy.
