Operationalizing Governance for AI-Enabled Defense-Relevant Robotics: From Principles to Controls, Metrics, and Enforcement
This academic chapter reviews literature from 2020 to 2025 on translating high-level AI governance principles into concrete design-phase and deployment-phase controls for AI-enabled defense robotics. It proposes a governance-to-engineering checklist mapping security governance themes to controls, required artifacts, gate authorities, and Department of Defense Risk Management Framework steps. The study aims to provide decision-makers with auditable evidence for go/no-go milestones in defense acquisition.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Defense acquisition professionals, researchers, and systems engineers should read this to understand how to operationalize AI governance controls for robotic systems.
What the document contains
The document reviews literature on translating broad AI governance principles into concrete design and deployment controls for AI-enabled defense robotics. It covers lifecycle gates, supporting artifacts, security metrics, and alignment with the Department of Defense Risk Management Framework.
The chapter does not provide binding legal regulations, prescribed numeric metric thresholds, or mandatory statutory mandates, as it is an academic literature review and proposed checklist.
- AI-enabled defense robotics greatly expand the cyber-physical attack surface and require concrete controls beyond high-level principles.
- Measurable security assurance is achieved by implementing governance through defined gates, supporting artifacts, and metrics.
- Design-phase controls include threat modeling, identity management baselines, code testing, and software bill of material provenance.
- Deployment-phase measures include segmented networks, robotics-tailored monitoring, cryptographically signed updates, and incident response.
- The proposed governance-to-engineering checklist maps themes to specific controls, required artifacts, and RMF steps for go/no-go milestones.
· Abstract · Introduction and Background · Literature Review on Robotic Cybersecurity · Governance-to-Engineering Checklist (Design, Build/Integration, Pre-OT/Fielding, Authorization, Operations)
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
Owner (program-defined): the role responsible for assembling the evidence and recommending a go/no-go decision (for example, chief engineer, ISSO, test lead, or program manager).
Appendix
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
The study proposes a governance-to-engineering checklist that maps each security governance theme to specific controls, required artifacts, responsible gate authorities, and corresponding RMF steps, providing decision makers with auditable evidence for go/no-go milestones.
Abstract
screening score · 2026-10-24
Enforcement
4/5Consequences and compliance mechanisms with real force
Results indicate that measurable security assurance is achieved by implementing governance through defined gates, supporting artifacts, and metrics.
Abstract
screening score · 2026-10-24
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Rigorous test and evaluation (TEVV) process should set gate criteria for requirements coverage, vulnerability management (with patch latency targets), test completion, and AI-specific model robustness.
Abstract
screening score · 2026-10-24