AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework
AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate, communicate, and fail through physical processes, networked devices, and human operators. This article develops an assurance-oriented account of AI sandboxes as controlled environments for testing, evaluati
Record details
Published: 16 June 2026
Source: arXiv
Category: Research
Topics: Military & security · Environment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale
arXiv · 11 June 2026
AI Assurance in UK Defence: Challenges in Operationalising JSP 936
arXiv · 8 June 2026
Insurance of Agentic AI
arXiv · 3 June 2026
Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
arXiv cs.CR (AI security) · 1 July 2026
Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment
arXiv · 1 June 2026
Perceived and actual AI literacy in military organizations: a self-efficacy framework for training design
AI & Society · 6 July 2026
How to cite this record
ethics.ai (16 June 2026), “AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework,” evidence record 858, https://ethics.ai/record/858 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.