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 ...
Robert, J., and Schmidt, D., 2024: 10 Benefits and 10 Challenges of Applying Large Language Models to DoD Software Acquisition. Carnegie Mellon University, Software ...
Morales, J., 2019: Challenges to Implementing DevOps in Highly Regulated Environments: First in a Series. Carnegie Mellon University, Software Engineering Institute's ...
DeCapria, D., 2024: Introduction to MLOps: Bridging Machine Learning and Operations. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Ozkaya, I., and Schmidt, D., 2024: Generative AI and Software Engineering Education. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Robinson, K., and Turri, V., 2024: Auditing Bias in Large Language Models. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...
This report describes 11 common vulnerabilities and 3 risks related to application programming interfaces, providing suggestions about how to fix or reduce their impact. Application programming ...
Sherman, M., 2024: Using ChatGPT to Analyze Your Code? Not So Fast. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed October 27 ...
Woody, C., 2024: Applying the SEI SBOM Framework. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed November 1, 2025, https://doi ...
Sarvepalli, V., 2023: UEFI: 5 Recommendations for Securing and Restoring Trust. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...
Nicolai, M., Richmond, N., and Morrow, T., 2023: 5 Best Practices from Industry for Implementing a Zero Trust Architecture. Carnegie Mellon University, Software ...
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