Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era
For enterprises and ecosystems deploying generative AI, this paper provides a comprehensive taxonomy of harms and a governance framework to mitigate regulatory, legal, and market risks, though it is a conceptual survey rather than an empirical study.
The paper introduces 'authenticity debt' as cumulative institutional liability from deploying AI-generated content without verifiable origin, integrity, and accountability, and proposes a layered reference architecture integrating cryptographic provenance, human-in-the-loop verification, and continuous governance to address risks across authenticity, provenance, integrity, and accountability layers.
Generative artificial intelligence has fundamentally changed how content is now produced. It has enabled how high-fidelity text, images, audio, and videos are created, modified, and redistributed at near-zero marginal cost. This shift exposes enterprises and ecosystems to a number of risks across four reinforcing authenticity layers -- authenticity, provenance, integrity, and accountability -- that traditional controls are inadequate to address in isolation. We introduce the concept of authenticity debt: the cumulative institutional liability that accumulates when organizations deploy AI-generated content without preserving verifiable origin, integrity, and accountability, deferring exposure that surfaces under regulatory, legal, or market scrutiny. This paper presents a comprehensive, multi-dimensional taxonomy of generative AI harms and attack vectors, surveys the capabilities and failure modes of technical controls including digital watermarking, provenance frameworks (C2PA, Adobe CAI), and detection technologies, and argues that no single mechanism is sufficient in open, adversarial, and evolving environments. Drawing on Zero Trust Architecture principles and enterprise governance frameworks, we propose a layered reference architecture that integrates cryptographic provenance, human-in-the-loop verification, and continuous governance to sustain defensible authenticity at scale. We further examine the regulatory landscape (EU AI Act, U.S.\ FTC, NIST AI RMF) and identify practical guiding principles for organizations seeking to build authenticity as institutional infrastructure rather than an afterthought.