# kaal:claim:4796714-032

**Claim.** Managing machine learning assets and complying with laws such as GDPR and CCPA becomes significantly harder under decentralized governance, because distributed data and operations complicate tracking data flows, enforcing privacy controls, and demonstrating compliance during audits.

**Type.** failure  **Support.** evidenced

**Holds when.**

- applies to decentralized ML governance spanning multiple stakeholders and locations
- concerns stringent privacy regimes such as GDPR and CCPA

**Source quote.**

> Managing ML assets and adhering to laws such as GDPR and CCPA is significantly more challenging under decentralized governance, raising concerns over privacy and data management.

**From.** Wulf A. Kaal, *AI Governance* (2024), Adapting Decentralization of AI Governance to AI Models, page 35

**Cite as.** Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

**Verify.** sha256 of source PDF `59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance.pdf

**Failure mode.** decentralized compliance verification gap  (family: compliance-cost-and-barrier-to-entry)

**Topics.** decentralization, governance-design, compliance

**Keywords.** gdpr, ccpa, decentralized-governance, auditability, compliance

**Related claims.**

- restated_by: https://wulfkaal.github.io/claims/4941807-027

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