The study explores the risks and tradeoffs when adapting enterprise-IT security and zero trust principles to weapon systems.
Churilla, M., VanHoudnos, N., and Beveridge, R., 2023: The Challenge of Adversarial Machine Learning. Carnegie Mellon University, Software Engineering Institute's ...
Kartch, R., 2018: Best Practices and Considerations in Egress Filtering. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...
DeCapria, D., 2025: DataOps: Towards More Reliable Machine Learning Systems. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed ...
Scherlis, B., 2024: Weaknesses and Vulnerabilities in Modern AI: Integrity, Confidentiality, and Governance. Carnegie Mellon University, Software Engineering ...
Keylor, E., Beveridge, R., and Frederick, J., 2025: Amplifying AI Readiness in the DoD Workforce. Carnegie Mellon University, Software Engineering Institute's ...
Executive Order 13587 requires federal agencies that operate or access classified computer networks to implement an insider threat detection and prevention program. Proposed changes to the National ...
Scherlis, B., 2024: The Latest Work from the SEI: Counter AI, Coordinated Vulnerability Disclosure, and Artificial Intelligence Engineering. Carnegie Mellon University, Software Engineering ...
CERT Insider Threat Center, T., 2011: Insider Threat Deep Dive: Theft of Intellectual Property. Carnegie Mellon University, Software Engineering Institute's Insights ...
Ruefle, R., 2017: Critical Asset Identification (Part 1 of 20: CERT Best Practices to Mitigate Insider Threats Series). Carnegie Mellon University, Software ...
Snoke, T., Shick, D., and Horneman, A., 2013: Working with the Internet Census 2012. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...
Over the past decade, artificial intelligence (AI) has made remarkable strides. From deep neural networks that can identify and label objects in images, to AI systems that outperform humans in complex ...