These are interesting times for AI and trust. More and more investment firms are using AI agents to review research notes and company filings. Humans are being asked to submit increasingly invasive biometric data, including facial scans, voice samples, and behavioral patterns, just to prove they’re not a bot. Once this data is out there, it can be weaponized by AI-driven bots to convincingly impersonate real humans and defeat the very systems designed to keep humans out. So we find ourselves in a strange new arms race. The more invasive the verification, the greater the risk in the event of an inevitable breach. So how do you know who (or what) you’re actually dealing with?
It is unconscionable to demand transparency from humans while accepting opacity from machines. Both bots and people online need better ways to verify their identity. This problem cannot be solved by simply collecting more biometric data or building centralized registries that serve as massive honeypots for cybercriminals. Zero-knowledge proofs provide a way forward for both humans and AI to prove their credentials without being exploited themselves.
Progress in preventing trust deficits
The absence of a verifiable AI identity creates immediate market risk. Companies are understandably hesitant to deploy autonomous systems at scale if AI agents can impersonate humans, manipulate markets, or perform fraudulent transactions. Coincidentally, LLMs that have been “tweaked” on small datasets to improve performance are 22 times more likely to produce harmful output than the base model, and three times more successful at circumventing system safety and ethical guardrails (a process known as “jailbreaking”) versus production-ready systems. Without reliable identity verification, every interaction with AI is one step closer to a potential security breach.
This problem is less obvious than preventing malicious actors from deploying rogue agents because we are not facing a single AI interface. In the future, we will increasingly see autonomous AI agents with better capabilities. With so many agents, how do we know what we’re dealing with? Even legitimate AI systems need verifiable credentials to participate in the emerging agent-to-agent economy. When an AI trading bot executes a transaction with another bot, both parties need assurances about the other party’s identity, authorization, and responsibility structure.
The human side of this equation is equally broken. Traditional identity verification systems expose users to massive data breaches, easily allow authoritarian surveillance, and generate billions of dollars in revenue for giant corporations by selling personal information without compensating the individuals who generate it. People are understandably reluctant to share more personal data, but regulatory requirements require ever more intrusive verification steps.
Zero knowledge: the bridge between privacy and accountability
Zero-knowledge proofs (ZKPs) provide a solution to this seemingly intractable problem. Rather than revealing sensitive information, ZKP allows entities, human or artificial, to prove certain claims without exposing the underlying data. Users can prove they are 21 or older without revealing their date of birth. AI agents can prove that they were trained on ethical datasets without exposing their proprietary algorithms. Financial institutions can ensure that customers meet regulatory requirements without storing potentially infringing personal information.
For AI agents, not only their technical architecture but also their behavior patterns, legal liability, and social reputation need to be verified, allowing ZKP to achieve the necessary deep level of trust. ZKP allows you to store these claims in an on-chain verifiable trust graph.
Think of it as a configurable 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, that its output has been audited, and that its actions are associated with responsible human entities, without divulging sensitive information.
ZKP has the potential to completely change the game, allowing people to prove who they are without handing over sensitive data, but adoption remains slow. ZKP remains technologically niche, unfamiliar to users, and caught in a regulatory gray area. What’s more, companies that profit from data collection have little incentive to deploy the technology. But that won’t stop agile identity companies from leveraging ZKP, and as regulatory standards emerge and awareness grows, ZKP has the potential to become the backbone of a new era of trusted AI and digital identity, providing a way for individuals and organizations to interact securely and transparently across platforms and borders.
Market Impact: Unleashing the Agent Economy
Generative AI has the potential to add trillions of dollars annually to the global economy, but much of this value remains locked behind identity verification barriers. There are several reasons for this. One is that institutional investors will require strong KYC/AML compliance before putting money into AI-driven strategies. Second, enterprises require verifiable agent identities before autonomous systems can access critical infrastructure. And regulators are demanding accountability mechanisms before approving the introduction of AI into sensitive areas.
A ZKP-based identity system addresses all of these requirements while maintaining the privacy and autonomy that make decentralized systems valuable. Meet regulatory requirements without creating honeypots of personal data by enabling selective disclosure. Providing cryptographic verification enables trustless interactions between autonomous agents. It also maintains user control and complies with new data protection regulations such as GDPR and the California Privacy Act.
The technology could also help address the growing deepfake crisis. Being able to cryptographically link all content to authenticated authors without revealing their identity helps combat misinformation and protects privacy. This is especially important because AI-generated content will be indistinguishable from human-generated material.
ZK Pass
Some argue that any identity system is a step toward authoritarianism, but societies cannot function without a way to identify their citizens. Identity verification is already being done at scale, but it’s not enough. Every time we upload documents for KYC, submit to facial recognition, or share personal data for age verification, we are participating in an invasive, insecure, and inefficient identity system.
Zero-knowledge proofs provide a path forward that enables the trust necessary for complex economic interactions while respecting individual privacy. These allow you to build systems where users are in control of their data, validation requires no supervision, and both humans and AI agents can interact securely without sacrificing autonomy.
