The study explores the risks and tradeoffs when adapting enterprise-IT security and zero trust principles to weapon systems.

DeCapria, D., 2025: DataOps: Towards More Reliable Machine Learning Systems. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...

The Software Engineering Institute is a leader in researching complex solutions, connecting AI, cyber, and software strategies for maximum impact. Since 1984, the SEI has been one of only 10 Federally ...

At the November event, co-organized by the SEI, artificial intelligence experts will present case studies and research and development in building AI systems for safety-critical applications.

Robert, J., and Schmidt, D., 2024: 10 Benefits and 10 Challenges of Applying Large Language Models to DoD Software Acquisition. Carnegie Mellon University, Software ...

The October 23 virtual workshop will feature presentations on dataset generation, exploration, preparation, and testing for ensuring data quality when training AI systems.

In this webcast, Brett Tucker, Dan Justice, and Matthew Butkovic discuss the challenges to be expected with the realization of quantum computing capabilities.

Robinson, K., and Turri, V., 2024: Auditing Bias in Large Language Models. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...

Shevchenko, N., 2018: Threat Modeling: 12 Available Methods. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 29, 2025 ...

DeCapria, D., 2024: Introduction to MLOps: Bridging Machine Learning and Operations. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...

Shevchenko, N., 2024: An Introduction to Model-Based Systems Engineering (MBSE). Carnegie Mellon University, Software Engineering Institute's Insights (blog ...