To ensure trust, artificial intelligence systems need to be built with fairness, accountability, and transparency at each step of the development cycle. In this podcast from the Carnegie Mellon University Software Engineering Institute, Carol Smith, a senior research scientist in human machine interaction, and Dustin Updyke, a senior cybersecurity engineering in the SEI’s CERT Division, discuss the construction of trustworthy AI systems and factors influencing human trust of AI systems. 

In this podcast from the Carnegie Mellon University Software Engineering Institute, Shannon Gallagher, a data scientist with SEI’s CERT Division, and Dominic Ross, multimedia team lead for the SEI, discuss deepfakes, their exponential growth in recent years, their increasing technical sophistication, and the problems they pose for individuals and organizations. Gallagher and Ross also discuss the SEI’s recent research in assessing the technology underlying the creation and detection of deepfakes and understanding current and future threat levels. 

Digital engineering uses digital tools and representations in the process of developing, sustaining, and maintaining systems, including requirements, design, analysis, implementation, and test. The digital modeling approach is intended to establish an authoritative source of truth for the system, in which discipline-specific views of the system are created using the same model elements. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), William “Bill” Nichols, a senior member of the technical staff with the SEI’s Software Solutions Division, discusses with principal researcher Suzanne Miller the challenges in making the transition from traditional development practices to digital engineering.

Over the past several years, zero trust architecture has emerged as an important topic within the field of cybersecurity. Heightened federal requirements and pandemic-related challenges have accelerated the timeline for zero trust adoption within the federal sector. Private sector organizations are also looking to adopt zero trust to bring their technical infrastructure and processes in line with cybersecurity best practices. Real-world preparation for zero trust, however, has not caught up with existing cybersecurity frameworks and literature. NIST standards have defined the desired outcomes for zero trust transformation, but the implementation process is still relatively undefined. As the nation’s first federally funded research and development center with a clear emphasis on cybersecurity, the Carnegie Mellon University Software Engineering Institute (SEI) is uniquely positioned to bridge the gap between NIST standards and real-world implementation. In this podcast, Tim Morrow and Matthew Nicolai, researchers with the SEI’s CERT Division, have outlined 4 steps that organizations can take to implement and maintain zero trust architecture.

In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Hasan Yasar, technical director, Continuous Deployment of Capability at the SEI, and Jay Palat, interim director of AI for Mission in the SEI’s AI Division, discuss how to engineer AI systems with DevSecOps and explore the relationship between MLOps and DevSecOps.

In this podcast from the Carnegie Mellon University Software Engineering Institute, Jonathan Spring, a senior vulnerability researcher, discusses with Suzanne Miller the findings in a paper he published recently analyzing the number of undiscovered vulnerabilities in information systems. This paper examines the paradigm that the number of undiscovered vulnerabilities is manageably small through the lens of mathematical concepts from the theory of computing. 

As the field of artificial intelligence (AI) has matured, increasingly complex opaque models have been developed and deployed to solve hard problems. Unlike many predecessor models, these models, by the nature of their architecture, are harder to understand and oversee. When such models fail or do not behave as expected or hoped, it can be hard for developers and end-users to pinpoint why or determine methods for addressing the problem. Explainable AI (XAI) meets the emerging demands of AI engineering by providing insight into the inner workings of these opaque models. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Violet Turri and Rachel Dzombak, both with the SEI's AI Division, discuss explainable AI, which encompasses all the techniques that make the decision-making processes of AI systems understandable to humans. 

In this podcast from the Carnegie Mellon University Software Engineering Institute, senior researchers Jerome Hugues and Joe Yankel discuss ModDevOps, an extension of DevSecOps that embraces model-based systems engineering (MBSE) practices and technology. Hugues and Yankel also discuss how making this integration between DevSecOps and MBSE explicit unlocks both the speed of DevSecOps and the risk reduction of MBSE.

Organizations are turning to DevSecOps to produce code faster and at lower cost, but the reality is that much of the code is actually coming from the software supply chain through code libraries, open source, and third-party components where reuse is rampant. The downside is that this reused code contains defects unknown to the new user, which, in turn, propagate vulnerabilities into new systems. This is troubling news in an operational climate already rife with cybersecurity risk. Organizations must develop a cybersecurity engineering strategy for systems that addresses the integration of DevSecOps with the software supply chain. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Carol Woody, a principal researcher in the SEI’s CERT Division, talks with Suzanne Miller about supply-chain issues and the planning needed to integrate software from the supply chain into operational environments. The discussion includes building a cybersecurity engineering strategy for DevSecOps that addresses those supply-chain challenges.

In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), director Paul Nielsen talks with principal researcher Suzanne Miller about how the advent of smart systems has led to a growing need for effective collaboration and cross-pollination between the disciplines of systems engineering and software engineering.