In March, the secretary of protection directed the Division of Protection (DoD) to undertake the Software program Acquisition Pathway (SWP) to speed up the event and deployment of capabilities to the warfighter. The mandate to make use of the SWP, which the SEI contributed to, arrives at a time when DoD missions more and more depend on software program and the associated applied sciences of cybersecurity and synthetic intelligence (AI)—all of that are focus areas on the SEI. These technical areas develop in significance as nationwide safety and protection organizations require enhanced capabilities to guard a broader vary of targets towards extra refined and adept threats.
On this submit, I’ll spotlight the methods by which our analysis and improvement assist DoD’s use of contemporary software program practices at every section of the software program improvement and operation lifecycle.
The SEI and the Software program Acquisition Pathway
The SEI’s depth of expertise with data-driven methods, strategies, and approaches; software program engineering; and acquisition science catalyzed our work on the SWP.
Part 255 of the FY2020 Nationwide Protection Authorization Act (NDAA) known as on the DoD to orient its software program actions in analysis, improvement, testing, and acquisition towards trendy software program engineering practices described in two research that SEI additionally contributed to: the Protection Innovation Board’s 2019 Software program Acquisition and Practices (SWAP) and the Protection Science Board Job Drive’s 2018 Design and Acquisition of Software program for Protection Methods.
As a part of our SWP work, the SEI labored hand-in-hand with stakeholders throughout the DoD and the protection industrial base within the iterative improvement, testing, and updating of the coverage and supporting instruments and sources. As known as for within the coverage doc DoDI 5000.87, DoD applications are to emphasise risk-based cybersecurity all through the lifecycle, counting on trendy software program practices together with DevSecOps. Cybersecurity and DevSecOps kind a basis on which these applications can incorporate new AI applied sciences extra securely.
For the reason that SEI’s inception as a federally funded analysis and improvement heart (FFRDC), we have now labored with academia, authorities, and trade to conduct analysis and assist DoD applications apply these important instruments, methods, practices, and insurance policies. The SEI prioritizes improvement of methods to assist steady, resilient, and well timed deployment of software program functionality for the warfighter whereas making certain that software program system efficiency and safety are usually not compromised in mission-critical settings.
SEI Impression on Modernizing DoD Software program Improvement
Over the past decade, the DoD has been incorporating rules and practices that promote steady, iterative deployment of software program functionality. One such initiative was the event of the 250-plus member Agile Collaboration Group. Members share classes realized to assist DoD practitioners extra readily profit from utilizing Agile strategies of their larger-scale programs. When the group started in 2012, it targeted on overcoming adoption challenges round Agile. In 2017, it expanded into DevSecOps analysis, improvement, and subject engagement.
In 2024 the SEI performed a examine inspecting the state of DevSecOps within the DoD, the outcomes of which have been not too long ago launched by the DoD Chief Data Officer (CIO). The examine discovered that whereas sure applications have had success adopting DevSecOps practices, the DoD nonetheless must implement these successes at scale. The examine holds partly that
- Investing in DoD software program factories is vital to securing our future functionality.
- DevSecOps is a key technique to speed up supply time.
- Success rests on reimagining a mission-ready DevSecOps workforce.
- Robust management dedicated to creatively driving options is important to overcoming limitations.
The DevSecOps examine additionally acknowledges that it’s essential to align these practices with the mission.
To a big diploma, the efficient use of contemporary software program practices rests on recognizing the strategic worth of knowledge that’s now accessible to the DoD in more and more large quantities. To provide DoD analysts better visibility into DevSecOps pipeline knowledge, the SEI not too long ago launched Polar, an answer to the restrictions of conventional batch knowledge processing. Polar offers visibility into the present state of a corporation’s DevSecOps infrastructure, permitting for the whole lot of the information to be engaged for knowledgeable choice making. The Polar framework, which could be downloaded from the SEI’s GitHub web site, helps DevSecOps organizations monitor and achieve insights into safety elements and handle the challenges posed by constructing advanced software program programs in extremely regulated environments.
DoD program leaders should more and more handle the entire software program improvement functionality. This accountability typically means dealing successfully with the technical debt that may accumulate in ageing programs in addition to that which might accumulate in speedy, iterative improvement. The SEI has been a pioneer in growing and making use of technical debt administration practices in advanced programs for nationwide safety and protection. Starting in 2010, the SEI challenged the software program engineering analysis group to seek out methods to handle technical debt and convened annual workshops on the subject. These workshops produced case research, empirical outcomes from making use of methods, and comparisons of instruments shared by the SEI and the software program group in tons of of publications within the Affiliation for Computing Equipment (ACM) and IEEE digital libraries. In 2018, the SEI’s group efforts resulted within the first worldwide convention on the topic, TechDebt. The eighth TechDebt convention was held in April 2025.
The SEI additionally led via ground-breaking analysis on the subject. An early paper on the subject, In Search of a Metric for Managing Architectural Debt, authored by SEI and College of British Columbia researchers, acquired the Most Influential Paper Award for its lasting impression on software program structure analysis and follow on the 2022 IEEE Worldwide Convention on Software program Structure. The SEI’s experience in technical debt R&D is the rationale that the DoD commissioned a crew of our software program improvement consultants to put in writing a report addressing the NDAA 2022 Part 835 mandate, delivered to Congress in December 2023.
The SEI has used its function to allow the DoD’s efficient use of contemporary software program engineering practices in different methods, as properly, together with instruments to
SEI Impression on Modernizing DoD Software program in Operation
A core tenet of the SEI’s mission is to allow the DoD to quickly deploy resilient software program capabilities. To do that, they want the proper tooling to make use of trendy software program practices and the means to guarantee system efficiency.
To facilitate DevSecOps use with large-scale programs, the SEI created the Platform-Unbiased Mannequin (PIM)—accessible on the SEI’s GitHub web site—to explain a DevSecOps pipeline on the highest stage: necessities, the product improvement lifecycle course of, and the organizational roles wanted to supply software program. Since its launch, a cross-disciplinary SEI crew has enhanced the PIM by incorporating risk eventualities: assault sort, actors, results, and pipeline belongings for defense. The ensuing upgraded instruments can be utilized to create safer processes and pipelines or spot safety weaknesses in current ones. When the pipeline is safer, so too is the software program it produces.
To enhance software program danger evaluation, SEI researchers and gear builders not too long ago launched an open-source software that streamlines and automates high quality assurance testing and evaluation, Silent Sentinel. This software gives a repeatable, constant course of to present system stakeholders a sensible evaluation of how an utility will have an effect on their deployment atmosphere.
Steady supply of software program functionality additionally implies that programs utilizing these capabilities want steady assurance of security, safety, and different qualities. In ongoing work, the SEI is in search of to scale back the effort and time required to re-assure massive programs. This notion of system assurance extends past safety to embody a number of architecturally vital considerations, together with efficiency, modifiability, security, and reliability.
SEI Impression on AI System Assurance
The world, and positively the DoD’s expertise atmosphere, is changing into more and more AI-augmented. When AI programs for nationwide safety fail in improvement or operation, they trigger critical, real-world penalties. Sadly, there are few accepted greatest practices for testing AI programs because of the challenges of correctly defining necessities and evaluating standards.
In 2023, the Workplace of the Underneath Secretary of Protection for Analysis and Engineering (OUSD(R&E)) and the SEI launched a middle geared toward establishing strategies for assuring trustworthiness in AI programs with emphasis on interplay between people and autonomous programs. The Heart for Calibrated Belief Measurement and Analysis (CaTE) goals to assist the DoD be certain that AI programs are protected, dependable, and reliable earlier than being fielded to operators in important conditions.
Additional, as detailed in a latest weblog submit and podcast, a gaggle of SEI software program and AI consultants not too long ago launched Machine Studying Take a look at and Analysis (MLTE), a brand new course of and gear collectively developed by the SEI and the Military AI Integration Heart (AI2C) to create safer, extra dependable ML programs. MLTE addresses three issues widespread within the ML mannequin improvement course of which might be limitations to efficient take a look at and analysis processes.
- Communication limitations between product improvement crew members. Group members are sometimes siloed throughout organizations, resulting in issues in gathering ML mannequin necessities cognizant of the system context and speaking ML mannequin analysis outcomes.
- Documentation issues for ML mannequin necessities. Eliciting and documenting ML mannequin necessities is commonly a problem for organizations, and documentation for ML system necessities is commonly lacking or of low high quality.
- Requirement analysis. Even when necessities are correctly outlined and documented, there isn’t any ML-specific technique to assist their implementation, testing, and analysis.
Broadening our perspective to AI danger administration, we explored how one can conceptualize trendy AI danger administration frameworks (RMFs) analogous to these for cyber danger. This work illustrates the broad scope of challenges that AI Engineering practices should handle, together with software program engineering and cybersecurity concerns. A latest SEI weblog submit famous this:
We should take into account, in different phrases, the conduct of a system or an related workflow below each anticipated and sudden inputs, the place these inputs could also be significantly problematic for the system. It’s difficult, nonetheless, even to border the query of how one can specify behaviors for anticipated inputs that aren’t precisely matched within the coaching set. A human observer could have an intuitive notion of similarity of latest inputs with coaching inputs, however there isn’t any assurance that this aligns with the precise that includes—the salient parameter values—inner to a skilled neural community.
SEI analysis groups additionally noticed the necessity for an AI safety response crew analogous to laptop safety response. An knowledgeable and motivated attacker could intentionally manipulate operational inputs, coaching knowledge, and different elements of the system improvement course of to create circumstances that impair appropriate operation of an AI system. To handle this want, the SEI launched the first-of-its type AI Safety Incident Response Group (AISIRT).
To guarantee that future AI programs shall be strong, safe, scalable, and able to serving warfighter wants, the SEI has been main the initiative to advance the self-discipline of AI Engineering. This emergent self-discipline will allow practitioners to focus R&D efforts in AI on growing instruments, programs, and processes for nationwide safety contexts.
SEI Impression on Software program Acquisition Safety
Just about all services {that a} DoD program acquires are supported by or combine with info expertise that features third-party parts or companies. Practices important to monitoring and managing these dangers could be scattered, leading to inconsistencies, gaps, and sluggish response to disruptions. To handle these points, SEI researchers created the Acquisition Safety Framework (ASF), which gives the DoD with a roadmap for constructing safety and resilience right into a system moderately than bolting them on after deployment. The ASF promotes higher communication and knowledge sharing throughout all program and provider groups to coordinate their administration of engineering and provide chain dangers. On this manner, the ASF helps applications match threats in a dynamic atmosphere with the speedy evolution of wanted software program capabilities.
As well as, in early 2020 the SEI partnered with Johns Hopkins College Utilized Physics Laboratory (APL), a college affiliated analysis heart, to launch the preliminary model of the cybersecurity maturity mannequin on the coronary heart of the Cybersecurity Maturity Mannequin Certification (CMMC) program. CMMC gives the DoD Workplace of the Underneath Secretary of Protection for Acquisition and Sustainment (OUSD(A&S)) with a robust software to enhance risk-informed selections and contractor safety within the protection industrial base provide chain.
SEI Impression on the Way forward for Software program Engineering Analysis
An vital a part of the SEI mission is to anticipate each challenges and alternatives in its three mission areas: software program engineering, cybersecurity, and AI. To develop an agenda for the following decade of software program engineering analysis, the SEI introduced collectively an advisory board of visionaries and senior thought leaders to develop an agenda for the following decade of software program engineering analysis. This effort led to the 2021 publication, Architecting the Way forward for Software program Engineering: A Nationwide Agenda for Software program Engineering Analysis and Improvement. The examine is a catalyst for analysis and improvement at Carnegie Mellon College and the SEI in areas similar to AI-augmented software program improvement, the assurance of repeatedly evolving software program programs, and engineering AI-enabled software program programs.
That SEI-led examine is influencing the DoD software program ecosystem and scary follow-on actions. As an example, in 2023 the SEI and the Networking and Data Know-how Analysis and Improvement (NITRD) program organized and hosted the U.S. Management in Software program Engineering and AI Engineering workshop; see the workshop’s government abstract. As well as, we labored with the Nationwide Protection Industrial Affiliation’s Rising Applied sciences Institute (NDIA ETI) to supply suggestions for the DoD because it shapes its software program modernization actions and analysis portfolio. Additional, the SEI is partnering with the software program engineering and AI communities to implement the suggestions of the analysis agenda. The SEI partnered with the Federal Aviation Administration and Vanderbilt College to convene two workshops in 2024 to handle the reassurance of repeatedly evolving software program programs, one of many examine’s areas of focus. SEI researchers together with leaders from the software program engineering group shall be main a workshop to handle how AI may rework end-to-end software program improvement workflows within the 2025 Worldwide Workshop on Envisioning the AI-Augmented Software program Improvement Lifecycle, collocated with the ACM Worldwide Convention on the Foundations on Software program Engineering.
The SEI: Persistent Concentrate on Software program Modernization
By way of the SEI Weblog and our podcast sequence, we spotlight the work of our researchers to assist the DoD make software program a strategic benefit via integrating our area experience in AI, cybersecurity, and software program. To assist the DoD ship resilient software program functionality on the velocity of relevance, the SEI researches advanced engineering issues; creates, prototypes, and refines revolutionary applied sciences; and transitions maturing options into follow to advertise DoD mission success.
