Title: Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

Abstract:

The advent of generative artificial intelligence has radically transformed the content creation landscape. By allowing for the production, alteration, and distribution of high-fidelity text, images, audio, and video at near-zero marginal costs, this technology has introduced significant risks to enterprises and broader ecosystems. These vulnerabilities span four interconnected authenticity layers—authenticity, provenance, integrity, and accountability—which traditional safeguards cannot adequately mitigate when applied in isolation.

This study introduces the notion of "authenticity debt," defined as the growing institutional liability incurred by organizations that implement AI-generated content without maintaining verifiable records of origin, integrity, and accountability. This deferred risk eventually manifests during regulatory, legal, or market examinations. The paper offers a comprehensive, multi-dimensional taxonomy of generative AI harms and associated attack vectors. It also evaluates the strengths and limitations of existing technical controls, such as digital watermarking, provenance frameworks (including C2PA and Adobe CAI), and detection technologies, arguing that no solitary mechanism is sufficient for open, adversarial, and rapidly evolving environments.

Leveraging principles from Zero Trust Architecture and enterprise governance models, we propose a layered reference architecture. This framework combines cryptographic provenance, human-in-the-loop verification, and continuous governance to maintain defensible authenticity at scale. Furthermore, the analysis reviews the current regulatory environment, including the EU AI Act, U.S. FTC guidelines, and the NIST AI RMF, while offering practical guiding principles for organizations aiming to establish authenticity as core institutional infrastructure rather than a secondary consideration.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC