There is a lot of documentation about a zero trust architecture, as well as directives that it be used for U.S. federal agencies and the Department of Defense (DoD), but little information on how to go about implementing it to improve an organization’s enterprise or DoD weapon system security. Use cases typically describe requirements for these systems, but they do not provide the contextual awareness that organizations need to help them...

According to the Verizon Data Breach Report, Log4j-related exploits have occurred less frequently over the past year. However, this Common Vulnerabilities and Exposures (CVE) flaw was originally documented in 2021. The threat still exists despite increased awareness. Over the past few years, the Software Engineering Institute (SEI) has developed guidance and practices to help organizations reduce threats to U.S. supply chains. In this webcast,...

We stand at a pivotal moment in software engineering, with artificial intelligence (AI) playing a crucial role in driving approaches poised to enhance software acquisition, analysis, verification, and automation. While generative AI tools initially sparked excitement for their potential to reduce errors, scale changes effortlessly, and drive innovation, concerns have emerged. These concerns encompass security risks, unforeseen failures, and is...

AI system trustworthiness is dependent on end users’ confidence in the system’s ability to augment their needs. This confidence is gained through evidence of the system’s capabilities. Trustworthy systems are designed with an understanding of the context of use and careful attention to end-user needs. In this webcast, we’ll discuss how to evaluate trustworthiness of AI systems given their dynamic nature and the challeng...

A Software Bill of Materials (SBOM) is a comprehensive list of software components involved in the development of a software product. While recently gaining attention in the context of security, SBOMs have limited value unless properly integrated into effective cyber risk management processes and practices. The SEI SBOM Framework compiles a set of leading practices for building an SBOM and using it to support risk reduction. The SEI SBOM Frame...

Historically, a lot of discussion in software security focused on the project level, emphasizing code scanning, penetration testing, reactive approaches for incident response, and so on. Today, the discussion has shifted to the program level to align with business objectives. In the ideal outcome of such a shift, software teams would act in alignment with business goals, organizational risk, and solution architecture and would understand that...

In this talk, we will present and discuss approaches to using data science and machine learning to address cybersecurity challenges. We provide an overview of data science, including a discussion of what constitutes a good problem to solve with data science. We also discuss applying data science to cybersecurity challenges, highlighting specific challenges such as detecting advanced persistent threats (APTs), assessing risk and trust, determin...

As Artificial Intelligence permeates mission-critical capabilities, it is paramount to design modular solutions to ensure rapid evolution and interoperability. During this webcast, we’ll discuss some of the primary quality attributes guiding such design, and how a Next Generation Architecture can facilitate an integrated future state. What attendees will learn: current challenges facing AI engineering approaches to promoting interoperabi...

IEEE 2675 standard specifies technical principles and practices to build, package, and deploy systems and applications in a reliable and secure way. The standard focuses on establishing effective compliance and IT controls. It presents principles of DevOps including mission first, customer focus, shift-left, continuous everything, and systems thinking. It also describes how stakeholders, including developers and operations staff, can collabora...

According to recent estimates, around 85% of AI projects fail to move from conceptualization to implementation. Why are these failures happening, and how can we prevent them? AI engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. The SEI is leading the national initiative to create an AI engineering discipline to operationalize hu...