NeuroCert: Adaptive Certification Framework for AI in Space

As autonomous space systems become more complex, ensuring their safety, reliability, and compliance with mission-critical standards remains a major challenge. Traditional software certification methods are slow, labor-intensive, and not well-suited for rapidly evolving AI-driven autonomy. To address this, M&T Consulting proposes NeuroCert: An Adaptive Certification Framework for AI in Space, an AI-assisted certification framework that accelerates the verification and validation (V&V) of autonomous space software. NeuroCert integrates formal verification techniques, machine learning-based anomaly detection, and model-based systems engineering (MBSE) to create an adaptive, scalable certification process. The framework will leverage AI to automate test case generation, identify failure modes, and validate system behaviors against safety and performance benchmarks. By combining explainable AI (XAI) with formal methods, NeuroCert ensures that autonomous decision-making processes remain transparent and auditable—critical for regulatory acceptance. Phase I funding will be used to develop a proof-of-concept prototype, validate key certification components in a simulated environment, and establish performance benchmarks for scalability and accuracy. The project will also explore integration pathways with existing NASA verification frameworks to ensure seamless adoption. Target applications include NASA's autonomous spacecraft, robotic explorers, and crew-assistive AI systems, as well as broader adoption in DoD space assets and commercial satellite operations. By modernizing the certification process, NeuroCert will reduce the time and cost of validating autonomous space systems while enhancing mission safety and reliability.