VeilGuard: A Multi-Modal AI Framework for Detecting Hidden Communications, Covert Payloads, and Emerging Cyber Threats
A conceptual communication threat intelligence architecture
Co-Founder & Chief Technology Officer, Tagus Technologies LLC · Dallas-Fort Worth, Texas, USA
PUBLICATIONPreprint / Working Paper
DATESeptember 2026
DOI10.5281/zenodo.22691204
Abstract / Research Summary
VeilGuard proposes an AI-powered Communication Threat Intelligence Platform for detecting hidden or suspicious signals across digital communications and media. The framework broadens traditional steganalysis by combining statistical analysis, signal processing, machine learning, threat intelligence, and behavioral analytics across multiple modalities, with the goal of identifying covert payloads, phishing indicators, suspicious file behavior, hidden communications, and insider-threat patterns.
Chapter 6 presents a proposed evaluation design. Numerical performance values or case-study outcomes should be treated as illustrative targets unless supported by reproducible datasets, code, experiment logs, and independent validation.