arXiv:2606.18532v1 Announce Type: cross Abstract: 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, evaluation, verification, and validation across digital AI
The increasing complexity and physical integration of AI systems, particularly in critical applications like robotics and cyber-physical systems, necessitate robust evaluation and assurance frameworks.
Ensuring the safety, reliability, and trustworthiness of AI, especially physical AI, is paramount for its societal adoption and for unlocking new economic and defensive capabilities.
This framework provides a structured approach for developing and testing secure AI environments, which will improve the quality and deployment speed of AI systems in sensitive domains.
- · AI assurance providers
- · Robotics companies
- · Defence contractors
- · Critical infrastructure operators
- · Malicious actors targeting AI systems
- · Companies with lax AI testing methodologies
Widespread adoption of AI sandboxing standards will lead to more secure and predictable AI deployments.
Increased trust in AI systems could accelerate the integration of autonomy into high-stakes sectors like manufacturing, logistics, and defense.
The established assurance processes might become foundational for international AI governance and certification, influencing global technology competition.
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Read at arXiv cs.AI