The Chain of Confidentiality: A Method for De-Identified AI Use in Regulated Real Estate and Professional Practice
Year: 2026
Type: article
Abstract: This paper describes a method that lets a licensed professional use a general-purpose artificial intelligence (AI) service on confidential assignment work while client identifiers stay inside the professional's own controlled environment. The method has seven elements. First, fourteen classes of identifiers are replaced with placeholders before anything is transmitted, and the placeholder map never leaves the professional's environment. Second, AI work is separated into a research lane and a generation lane that are never combined in one conversation. Third, an aggregation rule covers de-identified characteristic sets that can still identify a property in a thin market. A measurable candidate-count test, run locally against assessor, recorder or listing data, shows when details must be generalized. Fourth, a read-first session discipline is suited to structured reporting standards such as UAD 3.6. Fifth, a per-assignment audit record is kept. Sixth, that record passes from appraiser to AMC, lender, title and brokerage without the placeholder map. Seventh, the same method extends to other regulated professions. The method was developed in residential and commercial appraisal practice in northern Arizona. De-ID for AI is a workflow method designed to support compliance with professional confidentiality obligations. It does not certify compliance with USPAP, UAD 3.6 or any other standard; that responsibility must always remain with the appraiser.
Author: Berkson, Bruce R.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225003637