A systematic review and Zero Trust governance framework for agentic UAV robotics in public safety
This paper presents a systematic review of literature spanning agentic UAV autonomy, Zero Trust Architecture, UAV cybersecurity, LLM-agent security, and public-safety robot governance. It synthesizes these findings into the Zero Trust Agentic Drone Public-Safety (ZTADP) Framework: a six-layer model applying continuous verification to every actor, data stream, tool call, and AI-mediated recommendation in a UAV mission. The study contributes a mission-pipeline threat model, design principles for least-privilege autonomy, and a validation roadmap for trustworthy public-safety aerial robotics.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, developers, and public-safety administrators seeking a rigorous framework to secure and govern agentic UAV robotics.
What the document contains
The document addresses the integration of agentic AI capabilities, Zero Trust Architecture, and public-safety robot governance for Unmanned Aerial Vehicle (UAV) robotic systems. It details a six-layer verification framework, a mission-pipeline threat model, dataset-based feasibility analysis, and governance requirements for drone-as-first-responder operations.
The text does not provide a commercial software implementation or a legally binding regulatory statute enacted by a government body, as it is an academic research review and framework proposal.
- Public-safety UAV robotics is rapidly shifting from remotely piloted aircraft to agentic systems that perceive, plan, use tools, and recommend actions.
- The expanded capabilities of agentic AI introduce complex threat vectors across mission authorization, identity, device, communication, and reasoning planes.
- Existing Zero Trust frameworks and UAV security models largely ignore the trustworthiness of the AI agent's own reasoning and tool invocation.
- The proposed ZTADP framework introduces a six-layer architecture extending continuous verification to perception, planning, tool use, and recommendations.
- Effective public-safety deployment requires binding technical Zero Trust controls to rigorous governance, human oversight, and audit-ready evidence handling.
· 1 Introduction · 2 Background concepts · 3 Review methodology · 4 Public-safety applications of UAV robotic systems · 5 Agentic AI capabilities for trustworthy UAV robotics · 6 Threat landscape for trustworthy agentic UAV robotics · 7 Zero Trust Architecture for agentic UAV robotic systems · 8 Proposed framework: Zero Trust Agentic drone public-safety framework · 9 Trustworthiness requirements for safe, adaptive, and sustainable UAV robotics · 10 Governance and responsible AI issues · 11 Framework validation roadmap · 12 Open-source dataset-based feasibility analysis · 13 Research gaps and open challenges · 14 Future research directions · 15 Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
every robot action must be tied to a specific incident, purpose, geography, time window, and legal basis
8.1 Design principles
screening score · 2026-10-24
Enforcement
4/5Consequences and compliance mechanisms with real force
the AI’s planned tool calls are validated against a least-privilege allow-list scoped to the active, authorized mission
8 Proposed framework: Zero Trust Agentic drone public-safety framework
screening score · 2026-10-24