# kaal:claim:5245185-003

**Claim.** Because blockchain records a verifiable and immutable history of data provenance and alterations, it mitigates data poisoning risk and supports the claim that AI models were trained on genuine datasets.

**Type.** mechanism  **Support.** evidenced

**Holds when.**

- training data recorded on chain
- systems relying on large language models

**Source quote.**

> Blockchain establishes a verifiable record of data provenance and alterations, essential for sustaining trust and mitigating risks such as data poisoning, thereby ensuring AI models are trained on genuine datasets.

**From.** Wulf A. Kaal, *How can we Best Monitor AI Agents* (2025), 2. Convergence of AI and Blockchain Technology, page 2

**Cite as.** Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185

**Verify.** sha256 of source PDF `4d7adba83ec722480e97bde6528cbe9ce98c709e45cb18794f157a64b8fe7da2` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20How%20can%20we%20Best%20Monitor%20AI%20Agents.pdf

**Topics.** blockchain, citation-and-knowledge, ai-and-agents, education-and-practice

**Keywords.** blockchain, data-provenance, data-poisoning, training-data-integrity

**Related claims.**

- extends: https://wulfkaal.github.io/claims/4941807-018

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.