From Golf Clubs to Soccer Balls: The Design of AI-Powered Compliance Questionnaires in Lunar and Earth-Based Operations
© Luna Charta 2025. This work is licensed under CC BY-NC 4.0.
During the Apollo missions, every activity—even something as seemingly trivial as a golf shot on the Moon—had to pass through strict safety, engineering, and compliance protocols. Alan Shepard’s famous lunar golf swing in 1971 required him to obtain explicit permission from his manager, George Abbey, before he could hit the golf ball using a specially modified club—repurposed from a lunar sample collection tool—to swing at a standard golf ball that had passed rigorous safety checks.
In a modern mission, a sensor-equipped soccer ball designed to detect hydrogen (an indicator of water) would be integrated into the core mission objectives, making it easier to get sign-off. Its design would undergo extensive testing and certification, and its deployment would include real-time data transmission, automated risk assessments, and continuous compliance checks to ensure both safety and scientific robustness. Moreover, the system would feature automated questionnaires, intuitive UX dashboards, and AI explainability tools that provide transparent insights into compliance decisions, ensuring that all risks are identified and mitigated in real time.
In a modern mission, a sensor-equipped soccer ball designed to detect hydrogen (an indicator of water) would be integrated into the core mission objectives, making it easier to get sign-off. Its design would undergo extensive testing and certification, and its deployment would include real-time data transmission, automated risk assessments, and continuous compliance checks to ensure both safety and scientific robustness. Moreover, the system would feature automated questionnaires, intuitive UX dashboards, and AI explainability tools that provide transparent insights into compliance decisions, ensuring that all risks are identified and mitigated in real time.
Image Source: NASA
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Image Source: Licensed from Adobe Stoke. AI Generated.
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🛠 The Product Design Problem:
Not too long ago in a galaxy not very far away - before AI-driven compliance, regulatory processes were manual, fragmented, and time-consuming. Operators lacked real-time anomaly detection, compliance officers struggled with inconsistent AI decision-making, and risk visibility was limited across missions.
In this case study, we explore how product design processes can improve automated compliance workflows—from Earth-based logistics to lunar operations—through these core AI Driven UX Needs:
✅ Real-time situational awareness →
Lunar analysts require AI-driven anomaly detection to assess mission compliance risks in real-time, such as detecting fuel pressure drops, telemetry deviations, or regulatory drift.
✅ Multi-stakeholder coordination →
Compliance officers, mission controllers, and insurance underwriters need role-specific dashboards to verify regulatory adherence across multiple operational layers—from orbital station-keeping to lunar surface data collection.
✅ AI explainability & trust →
Mission-critical decisions rely on AI-driven risk predictions, but human analysts must be able to interpret, verify, and adjust compliance boundaries with full transparency.
✅ Extreme environments & sparse data networks →
AI compliance monitoring must function even in low-connectivity zones—whether tracking lunar telemetry in deep space or monitoring Earth-based supply chain logistics.
Not too long ago in a galaxy not very far away - before AI-driven compliance, regulatory processes were manual, fragmented, and time-consuming. Operators lacked real-time anomaly detection, compliance officers struggled with inconsistent AI decision-making, and risk visibility was limited across missions.
In this case study, we explore how product design processes can improve automated compliance workflows—from Earth-based logistics to lunar operations—through these core AI Driven UX Needs:
✅ Real-time situational awareness →
Lunar analysts require AI-driven anomaly detection to assess mission compliance risks in real-time, such as detecting fuel pressure drops, telemetry deviations, or regulatory drift.
✅ Multi-stakeholder coordination →
Compliance officers, mission controllers, and insurance underwriters need role-specific dashboards to verify regulatory adherence across multiple operational layers—from orbital station-keeping to lunar surface data collection.
✅ AI explainability & trust →
Mission-critical decisions rely on AI-driven risk predictions, but human analysts must be able to interpret, verify, and adjust compliance boundaries with full transparency.
✅ Extreme environments & sparse data networks →
AI compliance monitoring must function even in low-connectivity zones—whether tracking lunar telemetry in deep space or monitoring Earth-based supply chain logistics.
User Journey
Monitor Interaction
Explainable AI Interaction
Interaction Model with LLMs
Current large language models (LLMs) often force users into rigid interaction modes—either ignoring conversational prompts in favor of structured interfaces or engaging in AI-driven chat workflows with no direct control over structured inputs.
Why can’t compliance workflows integrate both?
Why can’t compliance workflows integrate both?
© Luna Charta 2025. This work is licensed under CC BY-NC 4.0.
Designing AI Compliance UX for Multi-Stakeholder Teams
Ensuring compliance in high-stakes environments—whether in space exploration, enterprise security, or supply chain automation—requires designing for multiple user roles, each with different priorities:
🔍 The Challenge: Compliance UX is Not One-Size-Fits-All
Without a well-designed compliance experience, teams face inefficiencies, confusion, and reduced trust in AI-driven decisions.
🔍 The Challenge: Compliance UX is Not One-Size-Fits-All
Without a well-designed compliance experience, teams face inefficiencies, confusion, and reduced trust in AI-driven decisions.
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🔵
Project Managers 🚨
Problem: Ambiguous prioritization—no clear decision-making points for task urgency. ✅
Solution: AI-powered compliance dashboards with priority escalation logic. |
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Engineers 🚨
Problem: Overwhelmed by generic system updates, struggling to differentiate critical risks vs. minor issues. ✅
Solution: AI-driven compliance automation that categorizes alerts by severity & risk level. |
🟢
QA Teams 🚨
Problem: No guidance on which errors to focus on first, especially in multi-component failures. ✅
Solution: AI-powered error-ranking in compliance workflows for structured debugging. |
🟡
Data Scientists 🚨
Problem: Lack of explainability—no visibility into how AI-derived compliance flags are triggered. ✅
Solution: Integrated explainability tools that provide justifications for AI-driven compliance decisions. |
📌 By incorporating feedback from these roles, we refine the service design to create seamless compliance workflows that balance technical feasibility with operational efficiency.
Why This Matters for AI-Powered Security Questionnaires
This interface intuitively embeds security questionnaire automation:
✅ Using AI-driven validation to ensure responses meet compliance standards.
✅ Offering simulation tools to refine justifications for flagged security risks.
✅ Providing expert consultation marketplaces to resolve high-risk issues.
✅ Integrating financial modeling for compliance cost-benefit analysis.
🔍 By embedding AI-powered automation into compliance workflows, we transform static regulatory checklists into adaptive, intelligence-driven risk management systems. 🚀
✅ Using AI-driven validation to ensure responses meet compliance standards.
✅ Offering simulation tools to refine justifications for flagged security risks.
✅ Providing expert consultation marketplaces to resolve high-risk issues.
✅ Integrating financial modeling for compliance cost-benefit analysis.
🔍 By embedding AI-powered automation into compliance workflows, we transform static regulatory checklists into adaptive, intelligence-driven risk management systems. 🚀
Meet the ball that inspired it all!
An official Probe Design Submitted to NASA
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Greetings, Earthlings!
I’m Luna C. Tron—the plucky soccer ball probe with a Neutron Spectrometer snugly secured in my gimbal-mounted heart. You can call me Luna. I’m a pretty transparent guy—you have to be if you’re going to map and characterize part of the Shackleton Crater floor with sensors inside you. My life’s ambition is to be kicked into the depths and roll into an extremely cold floor where darkness abounds. But hey! I eat lunar dust for breakfast! My structure dynamically adapts, allowing me to navigate hostile terrain. I’ll start the perilous descent by making my way down the crater walls, well past my spherical predecessors that share their trajectory, thermal data, and individualized sensor readings. My journey will showcase NASA’s commitment to open science and prove that even a soccer ball, equipped with passion and support, can unlock new lunar mysteries. I’m just hoping that astronauts from around the world will get a kick out of me! |
The dashboard bridges design thinking with real-world engineering by applying proven frameworks from MIT Space Systems Engineering, supporting:
NASA’s lunar mobility needs stemming from their moon to mars mission architecture
DARPA's lunar operations requirements led by JHU APL
SBIR research solicitations
NASA’s lunar mobility needs stemming from their moon to mars mission architecture
DARPA's lunar operations requirements led by JHU APL
SBIR research solicitations