Recent air disasters and near misses have underscored the precarious balance between automation and the responsibilities of human operators such as pilots and air-traffic controllers. Reliance on ML, AI and remote operation further increases the complexity of modern aerospace systems, making conventional risk management methods inadequate.

CSL addresses the safety critical challenges of emergent technologies, such as space robotics, that rely on Machine Learning for autonomous operation. Leveraging years of collective experience with established standards such as MIL-STD-882, RTCA DO-178C, and IEC 61508, CSL steps in to fill this gap. In addition to hazard identification and risk analyses, CSL employs new innovative strategies and methods including formal (mathematical) methods to ensure the safety of complex systems.

CSL contributed to the development of RTCA DO-178C Software Considerations in Airborne Systems and Equipment Certification, and its Formal Methods supplement RTCA DO-333, used as a basis for the certification of airborne software.