Our latest podcast highlights an AI Adoption Maturity Model that organizations can use to create a roadmap for predictable AI adoption and realization of AI benefits.

In this webcast, SEI researchers explore the current challenges and emerging solutions for building and sustaining cyber and AI-ready individuals and teams.

Software analysts use static analysis as a standard method to evaluate the source code for potential vulnerabilities, but the volume of findings is often too large to review in their entirety, causing ...

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

Smith, J., 2024: Incorporating Agile Principles into Independent Verification and Validation. Carnegie Mellon University, Software Engineering Institute's Insights ...

Modeling and validating of quality attributes for real-time, embedded systems is often done with low-fidelity software models and disjointed architectural specifications by various engineers using ...

Mead, N., Woody, D., and Hissam, S., 2024: Measurement Challenges in Software Assurance and Supply Chain Risk Management. Carnegie Mellon University, Software ...

Shannon Gallagher discusses findings and recommendations from the Mayflower Project and provides additional background information about LLMs and how they can be engineered for national security use.

Scanlon, T., 2023: Cybersecurity of Quantum Computing: A New Frontier. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed March 6 ...

Report a Vulnerability to CERT/CC Subscribe to SEI Bulletin Request Permission to Use SEI Materials ...

Wassermann, G., and Svoboda, D., 2023: Rust Vulnerability Analysis and Maturity Challenges. Carnegie Mellon University, Software Engineering Institute's Insights ...

Sible, J., and Svoboda, D., 2022: Rust Software Security: A Current State Assessment. Carnegie Mellon University, Software Engineering Institute's Insights (blog ...