From Space Station to AI Security: SGDL's Physics-Based Reasoning Revolution

World premiere announcement at Panoramai: SGDL has pioneered a breakthrough approach to AI that leverages space-proven physics simulation technology for cybersecurity applications. By modeling complex multi-stage interactions in both physical and digital domains, the company offers a fundamentally new paradigm for understanding and defending against sophisticated autonomous threats.

WINNER 2025: SGDL startup has received the Panoramai "Cosmos Award" and will be part of Panoramai Cohort 1 (mentoring and travel).

Space Operations as AI Security Blueprint

Paul-Olivier Dehaye, representing SGDL, opened his keynote with a striking analogy that connects space exploration challenges to modern AI security threats. He described the precise operation of astronauts using robotic arms to capture incoming spacecraft like Dragon modules—a delicate process requiring deep understanding of physical interactions, surface friction, vibrations, and dynamic positioning.

This space-based scenario becomes a powerful framework for understanding AI security challenges. Just as astronauts must navigate uncertain environments while executing complex multi-stage operations, cybersecurity professionals face increasingly autonomous threat actors deploying multi-hop attacks that systematically penetrate deeper into target systems.

Dehaye drew explicit parallels to historical examples like the Stuxnet attacks on Iranian nuclear facilities, where malicious software demonstrated remarkable autonomy by jumping between hardware systems. « We've already seen multi stage actors do multi stage attacks » he noted, emphasizing how space-grade autonomous systems provide the blueprint for both defensive and offensive AI capabilities.

Revolutionary Physics-First Architecture

SGDL's core innovation lies in transferring space station and Mars rover technology to AI reasoning systems. The company has developed operational systems that model complete physical object properties and their interactions, moving far beyond traditional visual or graphical representations.

Dehaye emphasized the comprehensive nature of their approach: « This model should not just be a visual model, a graphical model. It should start to be able to model all kind of different aspects of the physical object that you are simulating » Their system accounts for surface friction dynamics, vibration resonance patterns, shadow interactions from distant objects, and complex multi-dimensional positioning—all factors critical for mission success in space operations.

This physics-aware modeling runs efficiently on severely constrained hardware, demonstrated by its deployment on space stations and Mars rovers where computational resources are extremely limited yet reliability requirements are absolute.

The Exo-Language Breakthrough

Central to SGDL's technological advancement is their proprietary « exo-language »—a programming paradigm specifically designed for machine-to-machine communication rather than human use. This language generates code that simultaneously simulates physical objects and models their interaction logic through multi-stage processes.

« We have machines using a language to generate models that will then be used further by machines » Dehaye explained. This creates unprecedented efficiency in AI system communication, enabling reasoning about complex object interactions that traditional programming languages cannot adequately express.

The exo-language represents a fundamental shift toward AI systems that can communicate complex concepts without human intermediation, potentially revolutionizing how autonomous systems coordinate in both space operations and cybersecurity applications.

Physics-to-Cybersecurity Technology Transfer

The keynote's most significant strategic insight emerged from SGDL's vision to transfer proven space-based reasoning capabilities to cybersecurity domains. Dehaye proposed applying identical multi-stage interaction modeling techniques used for space robotics to security challenges including AWS policy analysis, threat modeling, and defensive system coordination.

This approach promises to revolutionize AI security by moving beyond signature-based detection and pattern matching toward fundamental interaction modeling. SGDL's technology could analyze complex relationships between security policies, system components, and threat actor behaviors with the same mathematical precision used to dock spacecraft in zero gravity.

The implications extend to understanding how modern threat actors build increasingly autonomous systems capable of adaptive multi-hop attacks that evolve based on target environment characteristics.

Space-Filling Curves and Vector Optimization

Dehaye concluded with an illuminating comparison between IKEA's Lausanne store design and SGDL's core mathematical framework. He described how IKEA's top floor creates a carefully engineered path that « crawls through space, a maximal amount of space » enabling customers to efficiently evaluate options while maintaining spatial awareness of the warehouse infrastructure below.

This space-filling curve concept forms the mathematical foundation of SGDL's vector space engineering. The company embeds these curves into AI vector spaces, creating adaptive indexing systems that enable more efficient navigation through complex information domains. Like IKEA's strategic shortcuts to specific destinations, SGDL's system provides optimized pathways through vast data landscapes.

This mathematical approach offers significant advantages for processing and reasoning about large-scale security data where traditional linear processing becomes computationally prohibitive.

Competitive Technological Advantages

SGDL's physics-first approach delivers several distinct advantages over conventional AI security architectures:

Autonomous Multi-Stage Reasoning: Unlike traditional systems that rely on pre-programmed responses, SGDL's technology models fundamental interaction principles, enabling adaptive responses to novel attack patterns.

Hardware Efficiency: Space-grade optimization allows high-performance operation on basic hardware—crucial for edge computing environments and resource-constrained security applications.

Cross-Domain Applicability: Physics-based reasoning principles proven in space operations transfer directly to any domain requiring complex interaction modeling, from network security to financial system analysis.

Mathematical Rigor: Space-filling curve optimization provides theoretically grounded approaches to vector space navigation that outperform heuristic methods.

Strategic Implications for European AI Leadership

SGDL's technology represents a uniquely European contribution to global AI development, combining theoretical mathematical rigor with practical applications addressing real-world security challenges. The company's nomination for Panoramai's AI Startup Awards underscores the strategic importance of their approach for Swiss and European AI ecosystems.

This physics-based paradigm offers European enterprises a differentiated approach to AI security that doesn't rely on massive data collection or computational scale—competitive advantages more aligned with European privacy principles and regulatory frameworks.

The technology addresses the sophisticated AI-powered threats identified throughout Panoramai presentations by matching threat actor autonomy and reasoning capabilities rather than simply detecting known attack patterns.

Future Application Domains

Beyond cybersecurity, SGDL's space-to-AI technology transfer model suggests transformative applications across multiple industries:

Autonomous Manufacturing: Physics-aware robotics optimization for complex assembly processes requiring precise interaction modeling

Financial Risk Management: Multi-stage policy interaction analysis for regulatory compliance and risk assessment

Healthcare Systems: Biological interaction modeling for drug discovery and treatment optimization

Smart Infrastructure: Complex system interaction modeling for smart city and IoT security applications

The keynote highlighted how space exploration continues driving terrestrial innovation, with SGDL representing the latest evolution in this technology transfer tradition. As AI systems become more autonomous and sophisticated, physics-based reasoning capabilities proven in space operations may become essential for maintaining security and reliability across critical applications.

SGDL's approach suggests that the future of AI security lies not in scaling existing paradigms but in fundamentally reimagining how AI systems understand and reason about complex interactions—whether docking spacecraft or defending against sophisticated cyber threats.