Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

2026-05-28

AI Summary

This study addresses the growing challenge posed by adversarial synthetic content generated through generative artificial intelligence and its rapidly evolving disinformation narratives, which have outpaced conventional passive detection approaches. The work proposes a proactive, lifecycle-based detection paradigm that integrates sociological and machine learning perspectives to construct a unified classification framework. Centered on the C5 interaction model—comprising Context, Catalysts, Content, Amplification loops, and Consequences—it systematically unpacks the mechanisms underlying the generation and dissemination of false narratives. By leveraging coordinated inauthentic behavior analysis, epidemic modeling, Hawkes processes, high-dimensional embedding anomaly detection, and multilayer graph collaboration, the approach shifts from reactive responses toward forward-looking threat identification. The research identifies core challenges in tracking dynamic threats and multilevel distributional shifts, and outlines a future agenda emphasizing anomaly cluster detection and the development of resilient systems.

Abstract

The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives. In this survey, we adopt a unified, lifecycle-based taxonomy to combine socio-technical lifecycle models of adversarial campaigns with advanced computational methodologies for emerging inauthentic narrative detection. By structuring the analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), we integrate different research streams from machine learning and social science. To differentiate spread patterns of synthetic amplification from authentic baseline traffic, this paper surveys state-of-the-art techniques for modeling the creation, seeding, and propagation of fresh narratives, including the analysis of Coordinated Inauthentic Behavior (CIB), epidemiological modeling, and Hawkes process. This survey also provides a systematic review of proactive detection methods for adversarial threats at different stages in the C5 interaction model, specifically, anomaly detection in high-dimensional embedding spaces, unsupervised coordination detection on multi-layer graphs, and agentic AI systems. Finally, this survey addresses challenges posed by GenAI, including the difficulty of tracking rapidly changing threats and multi-level distributional drift, and it outlines a future research agenda focused on detecting anomalous clusters and building anticipatory and resilient systems. This survey provides a comprehensive, lifecycle-based review of methods for the proactive detection of emerging synthetic threats for more resilient information ecosystems.

Problem

• Generative AI
• Digital Ecosystem Resilience
• Adversarial Synthetic Content
• Proactive Detection
• Inauthentic Narratives

Innovation

• proactive detection
• generative AI
• C5 Interaction Model
• coordinated inauthentic behavior
• lifecycle-based framework