Enterprise AI governance tools—CASB platforms, DLP solutions, network-layer AI detection—exist for large organizations with dedicated security staff. Small and mid-sized organizations face the same shadow AI risks with none of the same tooling options. This technical note presents a lineage-first detection framework for SMBs: a structured approach to identifying undeclared AI use through behavioral signals, provenance gaps, and delegation boundary violations, requiring no specialized software and no technical staff. The framework reframes shadow AI not as a tool inventory problem but as a lineage discontinuity problem—and derives detection methods from that reframing. The most operationally useful elements—the Helper-Shadow Test, the Sunlight Rituals, and the four-declaration attestation model—are designed to be deployable immediately by non-technical staff in any SMB environment.
Narnaiezzsshaa Truong (Wed,) studied this question.