Software Engineering Institute (SEI) Webcast Series

This webcast introduces and explores the seven topic areas that the SEI identified in its SEI Report, Advancing Cybersecurity: A Framework of Cyber Research Priorities. The SEI identified the seven areas because they have ongoing significance within cybersecurity's rapidly evolving landscape, and each one represents operational and research focus that will drive significant improvements across a wide range of missions and systems. This webcast discusses these areas to show how they help pinpoint relevant technical challenges and define a vision-driven research agenda to meet the challenges of modern cyber threats in the context of the most sophisticated systems.

These seven areas are as follows:

1. Analytic and Operational Tradecraft for Cybersecurity
2. Securing AI-Based Systems and Workflows
3. Cyber-Physical Systems (CPS) Security
4. Cybersecurity for Complex Integrated Systems
5. Insider Threat and Human–Systems Interaction
6. Secure Engineering and Mission Confidence for Critical Software-Reliant Systems
7. Modeling and Simulation (M&S) in Support of Security

What Attendees Will Learn:

• How the seven areas are significant to current problems in cybersecurity.
• How these seven areas can contribute to future states in cybersecurity engineering and operations.
• How accelerating research in these seven areas can provide substantial benefit to the broad community of the Software Engineering Institute's partners in the Department of War, the intelligence community, the Department of Homeland Security, other government agencies, and industry.

Collaboration among the Program Management Office, the Product Team, and Users is paramount to the success of software acquisition. In this webcast, software acquisition experts at Software Engineering Institute (SEI) discuss the User Agreement (UA), a key document for programs on the Software Acquisition Pathway (SWP), which is the cornerstone of that collaboration. Even if you are not using the SWP, if you are using Agile development methods, it's a good idea to consider developing and adopting a UA to get commitment from Users to participate throughout the software development lifecycle.

Attendees will learn

• Who should be involved in user agreement development?
• What types of information should be included in the user agreement?
• How to develop a user agreement that specifies the level of user representation needed during the software development lifecycle.

Fortran is one of the oldest high-level programming languages still in active use and remains a cornerstone of scientific, engineering, and high-performance computing (HPC) software in critical applications areas, such as climate and weather prediction, aerospace, nuclear energy, computational physics, and national security. Fortran provides several features that are particularly well-suited for scientific and high-performance computing, including intrinsic multidimensional array operations, native array slicing and whole-array syntax, built-in support for numerical computation, explicit parallel programming constructs, and language-level facilities for efficient vectorization and mathematical optimization. Recently, the Fortran community has shown growing interest in secure software development practices, vulnerability prevention, and secure coding standards. The SEI CERT Coding Standard for Fortran leverages expertise in secure coding practices and provides well-documented and enforceable coding guidelines to audit Fortran software.

Attendees will learn how to

• identify common security risks in Fortran
• navigate the SEI CERT Coding Standard for Fortran
• leverage Fortran safety features for secure coding
• improve software quality through secure coding practices

Many organizations are discovering, as they accelerate adoption of artificial intelligence (AI), that business and operational success with AI depends on far more than deploying AI models or experimenting with generative AI tools. Successful AI adoption occurs at the intersection of software engineering practices, the realities of system and enterprise architecture modernization, governance, cybersecurity, workforce readiness, workflow reengineering, operational integration, and enterprise strategy. Organizations must manage technological challenges that have intensified with AI adoption, including growing dependencies, vendor lock-in, and the imperative to innovate and scale quickly. Leaders must also adapt to new emerging realities, from the operational and financial demands of supporting multiple frontier models to the novel security and governance risks introduced by agentic AI approaches. Traditional approaches to technology transformation are no longer sufficient to thrive in this environment.

To address these emerging complexities and drive success, Carnegie Mellon University's Software Engineering Institute (SEI) collaborated with Accenture to develop the AI Adoption Maturity Model—an evidence-backed, field-tested instrument that provides a structured, yet agile, pathway for scaling AI capabilities across enterprises to ensure value and return on investment (ROI). This approach is designed for today's realities, including fast-paced technological change, limited time and resources, and the need for lightweight, actionable methods rather than burdensome documentation.

In this webcast, experts from the SEI and Accenture share technical insights and lessons learned from maturing AI adoption in complex environments. They will demonstrate how a nimble assessment instrument such as the road-tested AI Adoption Maturity Model fills critical gaps faced by organizations adopting AI.

What Will Attendees Learn?

• How AI maturity extends beyond isolated experimentation to encompass scalable, repeatable, measurable, and governed organizational capabilities.

• How common pitfalls and strengths that we observed in early adopter organizations during AI Adoption Maturity Assessments can influence AI adoption.

• How certain approaches to integrating existing risk management routines and security processes can support AI adoption.

• How an agile, lightweight maturity assessment approach can enable organizations to rapidly prioritize activities, align efforts, and make targeted progress.

AI is rapidly changing how software is produced but not the need to engineer software to meet business and mission goals. AI is enabling developers to move from idea to implementation at incredible speeds. However, this fast pace has implications that teams must manage. Product quality does not come for free, and there is some tendency for AI to accelerate the accumulation of technical debt. In addition, what works well on small code bases doesn't always work as well on large code bases. So, what's a good software engineer to do? Software engineering principles and practices are essential in guiding software engineers to use AI to achieve production-ready outcomes. In this webcast, experienced software engineers discuss their observations and lessons from applying AI-native software engineering and studying its use across multiple projects.

What Will Attendees Learn?

• Distinguish between "vibe coding" and software engineering

• Understand how software engineering principles improve the use of AI and where these principles need to be adapted to be used with AI

• Recognize the different criteria that can be used to assess the benefits of AI-native software engineering (e.g., productivity and software quality) and their potential tradeoffs

Metrics are about more than just compliance. They ground you in fact, enable informed decision making, and help your team understand performance while providing insight into program health. By understanding the why, what, and how behind metrics, your team can move from "just collecting data" to using metrics that enable better decision making, improve program outcomes, and deliver mission value. Get ready to hear all about our new Tactical Guide called Pack Light, Measure Right and Supplement called Decoding SWP Metrics.

What Will Attendees Learn?

• How to select a minimal but sufficient set of measures to get the insight you need

• How to implement measurement approaches that are compatible with iterative delivery

• The hidden value in the metrics that you're already collecting if you are on the Software Acquisition Pathway

Artificial intelligence (AI) systems offer tremendous potential, but compared to traditional software, they introduce novel safety and security risks. System theory provides a powerful lens for understanding these risks and developing effective mitigations. In this webcast, we'll introduce System Theoretic Process Analysis (STPA), a system-theory-based approach to safety analysis. We'll explain how STPA helps organizations build stronger assurances about the safety and security of complex systems, including those that incorporate AI.

What Will Attendees Learn?

• How complex systems fail due to design flaws and unsafe interactions—not just component failures

• How these types of accidents can occur in AI-enabled systems

• How to apply a system-theoretic perspective, including System Theoretic Process Analysis (STPA), to analyzing AI systems

• Practical insights into improving the design, testing, and operational use of AI systems to strengthen safety and security

Building an effective security operations capability such as a security operations center (SOC) has always been a challenging endeavor. Balancing the need to successfully integrate the people, processes, and technologies required to support your mission requires a deep understanding of your network and your threat landscape. New AI tools and technologies present both challenges and opportunities. These tools can complicate your operational environment but might be used by malicious actors to enhance their attacks. But AI can also be leveraged to build out and enable your SOC by covering gaps in tools, your workforce, and automation.

What Will Attendees Learn?

• How AI has changed the way SOC teams operate

• Ways to deploy AI tools in security operations settings

• How the SEI is using structured knowledge to build more effective SOCs

Are you confused about what should be in your Software Acquisition Pathway program's Capability Needs Statement (CNS)? You are not alone! One of the most frequent requests we receive is for a sample CNS. If only it was that easy! Never fear; our recently released Tactical Guide called Cracking the CNS Code will provide you with the practical insight you need to develop a CNS that will be effective for your program. We'll also talk about the Cracking the CNS Code's complementary supplement called How Does a CNS Drive the Process for Creating Working Software?

What Will Attendees Learn?

• What a CNS is

• Strategies for and approaches to creating a CNS

• How to break down your approved CNS into backlog items