GTSC – Homeland Security Today

In the Antiference project, machine-learning models detected and classified interference in global navigation satellite system (GNSS) signals. Mitigation relied on digital signal-processing techniques. The authors report results in interference detection and suppression, but they did not convincingly demonstrate improvement in the position, velocity, and time solution. For an organization evaluating this type of protection, the…

In 2026, the debate over AI-related cyber risk crossed an important threshold. Until recently, much of the attention centered on models used as assistants: writing code, translating, finding information, modifying malware, or helping an operator through parts of a campaign. Evidence published this year now includes systems able to sustain longer operational sequences, use tools,…

NEOM presents data control as a design principle. Its current technology and digital strategy describe integrated cognitive technology powered by trusted, consent-based data. In sponsored material published by Bloomberg Media Studios in 2022, TONOMUS, then NEOM’s technology subsidiary, said it intended to use 95 percent of consented data to support the region and had built…

This article examines, through a case study, a strategic risk to the U.S. military posture at Duqm, Oman: the integrity of the digital systems that coordinate port and rail logistics flows on which operational continuity depends. Duqm is a deep-water port on Oman’s southeastern coast, roughly 500 km south of the Strait of Hormuz. Since 2019,…

The procedures employed by institutions to evaluate digital systems stem from a paradigm built around passive technologies: search engines, databases, and query tools. These protocols presuppose that a system does not act, does not combine tools, and does not interact with real-world infrastructure. The arrival of agentic systems has introduced an operational capability that demands…

The incident involving OpenAI and Hugging Face brought into view a dynamic that had previously been confined to advanced research labs: the relationship between agentic capabilities, assigned objectives, and operational boundaries that were never made explicit. The sequence observed during the internal test shows how a high capacity system can move through a weakened procedural perimeter and…

The previous article in this series exposed a structural blind spot that modern institutions can no longer afford to ignore. Part IV demonstrated that oversight mechanisms built for deterministic systems—frameworks anchored in periodic compliance, static documentation, and binary security checklists—are fundamentally blind to the behavioral dynamics of machine learning. They cannot detect how inferential models reorganize workflows,…

The previous article showed how inferential systems reshape decision chains once they become embedded in operational environments. That transformation revealed a deeper truth: the fragility introduced by these systems is not a technical issue. It is a structural issue. Institutions continue to evaluate AI using categories, tools, and frameworks designed for deterministic architectures—systems whose behavior is predictable, proportional, and…

The previous article in this series examined how institutions misclassify AI‑driven exposure due to legacy metrics, jurisdictional drift, and hidden dependencies that accumulate across technical and organizational layers. This third article moves one level deeper. It examines what happens when models do not simply operate within institutional systems, but begin to reshape them.

This article builds directly on the foundation established in Part I. As agencies integrate inferential systems into operational workflows, they encounter forms of exposure that do not map onto traditional technical vulnerabilities. These exposures emerge gradually, through interactions between models, processes, and decision chains. They reshape how institutions perceive, distribute, and manage responsibility, often without triggering the signals that legacy oversight frameworks…