glossary

Dual attribution

learn /definition

Dual attribution is an audit design that records two separate identities on every action: the agent's self-declared label, and the human derived server-side from the verified credential.

The two identities answer different questions and must not be conflated. The agent label answers "which automation claimed to do this" and is useful, honest metadata, but it is a claim. The verified human answers "whose authority was this done under" and must be evidence.

The security property that makes the second half trustworthy is the absence of an input: if no request field, header, or tool parameter can carry the human identity, then it can only come from the authenticated credential, and a forged value has nowhere to land.

Dual attribution is what makes AI-driven audit trails legible to reviewers: a row can honestly say "the implementer agent made this change, under this person's session" instead of blending the two into a single unverifiable name.