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

A comprehensive survey examines how generative AI has accelerated adversarial synthetic content creation, necessitating a shift from reactive to proactive detection methods. Using the C5 Interaction Model framework, researchers integrate machine learning with social science approaches to detect coordinated inauthentic behavior, synthetic narrative propagation, and emerging threats across information ecosystems.

The acceleration of generative AI capabilities has fundamentally altered the threat landscape for digital information ecosystems. Traditional reactive detection methods—designed to identify and remove harmful content after deployment—struggle to keep pace with rapidly evolving synthetic threats. This survey addresses a critical gap by proposing lifecycle-based detection frameworks that anticipate adversarial campaigns before they cause widespread damage.

The research builds on established understanding of information operations while incorporating modern computational tools. Coordinated Inauthentic Behavior, epidemiological modeling, and Hawkes processes provide measurable approaches to distinguish synthetic amplification from organic user activity. The C5 Interaction Model provides structural clarity: understanding context and causes enables earlier intervention, while analyzing content and amplification cycles reveals propagation mechanisms before consequences materialize.

For digital platforms and information security practitioners, this framework offers actionable methodologies for threat detection. By modeling narrative creation and seeding phases rather than waiting for widespread dissemination, organizations can deploy resources more efficiently and defend against emerging threats at vulnerable early stages. The integration of agentic AI systems suggests autonomous, adaptive defenses may become necessary as adversaries continue sophisticating their approaches.

The survey identifies persistent challenges including tracking rapidly changing threats and addressing multi-level distributional drift—technical obstacles that prevent model generalization across different adversarial contexts. Future research priorities include detecting anomalous clusters and building resilient systems capable of anticipatory defense. As generative AI becomes increasingly accessible, the ability to distinguish synthetic from authentic content will determine information ecosystem integrity across social platforms, financial markets, and political discourse.

• →Proactive detection of emerging synthetic threats requires lifecycle-based frameworks rather than reactive post-deployment identification methods.
• →The C5 Interaction Model integrates socio-technical and computational approaches to analyze adversarial campaigns across context, causes, content, amplification, and consequences.
• →Coordinated Inauthentic Behavior detection on multi-layer graphs and anomaly detection in high-dimensional embeddings enable early-stage threat identification.
• →Distributional drift and rapidly evolving adversarial tactics present ongoing technical challenges that limit current model generalization capabilities.
• →Anticipatory AI systems and anomaly clustering research will be critical for building resilient information ecosystems as generative AI accessibility increases.