> For the complete documentation index, see [llms.txt](https://docs.zkpass.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zkpass.org/overview/use-cases/ai-and-data-economy.md). # AI and Data Economy Artificial intelligence systems depend on vast amounts of data, yet most data sources are unverified, fragmented, and prone to manipulation. Current AI pipelines lack the ability to distinguish between authentic and falsified information, creating risks of bias, fraud, and adversarial attacks. zkPass introduces a verifiable data layer for AI. By using zkTLS, sensitive Web2 data can be transformed into zero-knowledge proofs that certify authenticity and integrity without revealing the underlying content. This enables AI agents and models to consume reliable inputs while protecting user privacy. **Limitations of current AI data pipelines** * Reliance on unverified or crowd-sourced datasets vulnerable to manipulation. * Inability to use sensitive but high-value data (healthcare, finance) due to privacy constraints. * Lack of standardized provenance and auditability across AI models. **zkPass approach** * Proof generation from authoritative Web2 sources such as banks, hospitals, or education platforms. * Selective disclosure of attributes needed for AI inference without exposing entire records. * Cryptographic audit trails ensuring training data and real-time inputs are tamper-resistant. **Applications** * Financial AI advisors consuming verified transaction histories while preserving user confidentiality. * Healthcare AI systems accessing verified diagnoses or treatment eligibility proofs for clinical decision support. * AI agents in commerce and social networks consuming verifiable identity and behavior proofs to avoid fraud. **Strategic impact**\ zkPass provides the cryptographic foundation for “Verifiable AI,” ensuring that intelligent systems operate on data that is both authentic and privacy-preserving. This unlocks regulated, high-stakes use cases where AI adoption has so far been limited by trust and compliance barriers. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zkpass.org/overview/use-cases/ai-and-data-economy.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.