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Each January on the SEI Weblog, we current the ten most-visited posts of the earlier yr. This yr’s high 10 highlights our work in quantum computing, software program modeling, massive language fashions, DevSecOps, and synthetic intelligence. The posts, which have been printed between January 1, 2023, and December 31, 2023, are offered under in reverse order primarily based on the variety of visits.
#10 Contextualizing Finish-Person Wants: How one can Measure the Trustworthiness of an AI System
by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner
As potential purposes of synthetic intelligence (AI) proceed to develop, the query stays: will customers need the know-how and belief it? How can innovators design AI-enabled merchandise, providers, and capabilities which can be efficiently adopted, slightly than discarded as a result of the system fails to fulfill operational necessities, akin to end-user confidence? AI’s promise is certain to perceptions of its trustworthiness.
To highlight a number of real-world situations, think about:
- How does a software program engineer gauge the trustworthiness of automated code era instruments to co-write useful, high quality code?
- How does a physician gauge the trustworthiness of predictive healthcare purposes to co-diagnose affected person situations?
- How does a warfighter gauge the trustworthiness of computer-vision enabled menace intelligence to co-detect adversaries?
What occurs when customers don’t belief these techniques? AI’s capacity to efficiently accomplice with the software program engineer, physician, or warfighter in these circumstances relies on whether or not these finish customers belief the AI system to accomplice successfully with them and ship the end result promised. To construct acceptable ranges of belief, expectations should be managed for what AI can realistically ship.
This weblog put up explores main analysis and classes realized to advance dialogue of easy methods to measure the trustworthiness of AI so warfighters and finish customers basically can understand the promised outcomes.
Learn the put up in its entirety.
#9 5 Finest Practices from Business for Implementing a Zero Belief Structure
by Matthew Nicolai, Nathaniel Richmond, and Timothy Morrow
Zero belief (ZT) structure (ZTA) has the potential to enhance an enterprise’s safety posture. There may be nonetheless appreciable uncertainty concerning the ZT transformation course of, nonetheless, in addition to how ZTA will in the end seem in observe. Current govt orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and lots of personal sector organizations are following go well with. In response to those govt orders, researchers at the SEI’s CERT Division hosted Zero Belief Business Days in August 2022 to allow trade stakeholders to share details about implementing ZT.
On this weblog put up, which we tailored from a white paper, we element 5 ZT greatest practices recognized through the two-day occasion, focus on why they’re important, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.
Learn the put up in its entirety.
#8 The Problem of Adversarial Machine Studying
by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge
Think about driving to work in your self-driving automotive. As you method a cease signal, as an alternative of stopping, the automotive accelerates and goes by means of the cease signal as a result of it interprets the cease signal as a pace restrict signal. How did this occur? Despite the fact that the automotive’s machine studying (ML) system was skilled to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automotive into pondering it was a 45-mph pace restrict signal. This easy act of placing stickers on a cease signal is one instance of an adversarial assault on ML techniques.
On this SEI Weblog put up, I study how ML techniques could be subverted and, on this context, clarify the idea of adversarial machine studying. I additionally study the motivations of adversaries and what researchers are doing to mitigate their assaults. Lastly, I introduce a primary taxonomy delineating the methods by which an ML mannequin could be influenced and present how this taxonomy can be utilized to tell fashions which can be strong towards adversarial actions.
Learn the put up in its entirety.
#7 Play it Once more Sam! or How I Discovered to Love Giant Language Fashions
by Jay Palat
“AI is not going to exchange you. An individual utilizing AI will.”
-Santiago @svpino
In our work as advisors in software program and AI engineering, we are sometimes requested concerning the efficacy of AI code assistant instruments like Copilot, GhostWriter, or Tabnine primarily based on massive language mannequin (LLM). Current innovation within the constructing and curation of LLMs demonstrates highly effective instruments for the manipulation of textual content. By discovering patterns in massive our bodies of textual content, these fashions can predict the following phrase to jot down sentences and paragraphs of coherent content material. The priority surrounding these instruments is robust – from New York colleges banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many purposes are strictly restricted to writing textual content, a number of purposes discover the patterns to work on code, as effectively. The hype surrounding these purposes ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to worry, uncertainty, and doubt (“LLMs are going to take my job”). Within the Communications of the ACM, Matt Welsh goes as far as to declare we’ve reached “The Finish of Programming.” Whereas built-in growth environments have had code era and automation instruments for years, on this put up I’ll discover what new developments in AI and LLMs imply for software program growth.
Learn the put up in its entirety.
#6 How one can Use Docker and NS-3 to Create Practical Community Simulations
by Alejandro Gomez
Generally, researchers and builders have to simulate numerous forms of networks with software program that will in any other case be onerous to do with actual gadgets. For instance, some {hardware} could be onerous to get, costly to arrange, or past the talents of the group to implement. When the underlying {hardware} just isn’t a priority however the important capabilities that it does is, software program could be a viable different.
NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer season of Code, and others. It has a excessive diploma of functionality to simulate numerous sorts of networks and user-end gadgets, and its Python-to-C++ bindings make it accessible for a lot of builders.
In some instances, nonetheless, it isn’t enough to simulate a community. A simulator may want to check how knowledge behaves in a simulated community (i.e., testing the integrity of Person Datagram Protocol (UDP) visitors in a Wi-Fi community, how 5G knowledge propagates throughout cell towers and person gadgets, and so forth. NS-3 permits such sorts of simulations by piping knowledge from faucet interfaces (a characteristic of digital community gadgets offered by the Linux kernel that move ethernet frames to and from person house) into the working simulation.
This weblog put up presents a tutorial on how one can transmit stay knowledge by means of an NS-3-simulated community with the added benefit of getting the data-producing/data-receiving nodes be Docker containers. Lastly, we use Docker Compose to automate advanced setups and make repeatable simulations in seconds.
Learn the put up in its entirety.
#5 5 Challenges to Implementing DevSecOps and How one can Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the venture degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Not too long ago, nonetheless, the dialogue has shifted to this system degree to align safety with enterprise targets. The best final result of such a shift is one by which software program growth groups act in alignment with enterprise objectives, organizational threat, and answer architectures, and these groups perceive that safety practices are integral to enterprise success. DevSecOps, which builds on DevOps ideas and locations further give attention to safety actions all through all phases of the software program growth lifecycle (SDLC), will help organizations understand this very best state. Nevertheless, the shift from project- to program-level pondering raises quite a few challenges. In our expertise, we’ve noticed 5 widespread challenges to implementing DevSecOps. This SEI Weblog put up articulates these challenges and offers actions organizations can take to beat them.
Learn the put up in its entirety.
#4 Utility of Giant Language Fashions (LLMs) in Software program Engineering: Overblown Hype or Disruptive Change?
by Ipek Ozkaya, Anita Carleton, John E. Robert, and Douglas Schmidt (Vanderbilt College)
Has the day lastly arrived when massive language fashions (LLMs) flip us all into higher software program engineers? Or are LLMs creating extra hype than performance for software program growth, and, on the similar time, plunging everybody right into a world the place it’s onerous to tell apart the superbly shaped, but typically faux and incorrect, code generated by synthetic intelligence (AI) applications from verified and well-tested techniques?
This weblog put up, which builds on concepts launched within the IEEE paper Utility of Giant Language Fashions to Software program Engineering Duties: Alternatives, Dangers, and Implications by Ipek Ozkaya, focuses on alternatives and cautions for LLMs in software program growth, the implications of incorporating LLMs into software-reliant techniques, and the areas the place extra analysis and improvements are wanted to advance their use in software program engineering.
Learn the put up in its entirety.
#3 Rust Vulnerability Evaluation and Maturity Challenges
by Garret Wassermann and David Svoboda
Whereas the reminiscence security and security measures of the Rust programming language could be efficient in lots of conditions, Rust’s compiler may be very specific on what constitutes good software program design practices. At any time when design assumptions disagree with real-world knowledge and assumptions, there may be the potential of safety vulnerabilities–and malicious software program that may make the most of these vulnerabilities. On this put up, we are going to give attention to customers of Rust applications, slightly than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is accessible or not. These instruments are essential for understanding malicious software program the place supply code is usually unavailable, in addition to commenting on doable instructions by which instruments and automatic code evaluation can enhance. We additionally touch upon the maturity of the Rust software program ecosystem as an entire and the way that may influence future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Middle (CERT/CC). This put up is the second in a collection exploring the Rust programming language. The first put up explored safety points with Rust.
Learn the put up in its entirety.
#2 Software program Modeling: What to Mannequin and Why
by John McGregor and Sholom G. Cohen
Mannequin-based techniques engineering (MBSE) environments are supposed to assist engineering actions of all stakeholders throughout the envisioning, creating, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE surroundings, assist efforts such because the automation of standardized evaluation strategies by all stakeholders and the upkeep of a single authoritative supply of fact about product data. The mannequin faithfully represents the ultimate product in these attributes of curiosity to numerous stakeholders. The result’s an total discount of growth dangers.
When initially envisioned, the necessities for a product could appear to characterize the precise product for the stakeholders. Throughout growth, nonetheless, the as-designed product involves mirror an understanding of what’s actually wanted that’s superior to the unique set of necessities. When it’s time to combine parts, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is not represented and is not a legitimate supply of check instances. Many questions come up, akin to
- How do I consider the failure of a check?
- How can I consider the completeness of a check set?
- How do I monitor failures and the fixes utilized to them?
- How do I do know that fixes utilized don’t break one thing else?
Such is the case with necessities, and far the identical ought to be the case for a set of fashions created throughout growth—are they nonetheless consultant of the carried out product present process integration?
One of many objectives for strong design is to have an up-to-date single authoritative supply of fact by which discipline-specific views of the system are created utilizing the identical mannequin parts at every growth step. The only authoritative supply will usually be a set of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a legitimate supply of full and proper verification and validation (V&V) actions. On this put up, we study the questions above and different questions that come up throughout growth and use the solutions to explain modeling and evaluation actions.
Learn the put up in its entirety.
#1 Cybersecurity of Quantum Computing: A New Frontier
by Tom Scanlon
Analysis and growth of quantum computer systems continues to develop at a fast tempo. The U.S. authorities alone spent greater than $800 million on quantum data science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they are going to be capable to remedy sure issues which can be classically intractable, that means a standard pc can’t full the calculations inside human-usable timescales. Given this computational energy, there may be rising dialogue surrounding the cyber threats quantum computer systems might pose sooner or later. As an example, Alejandro Mayorkas, secretary of the Division of Homeland Safety, has recognized the transition to post-quantum encryption as a precedence to make sure cyber resilience. There may be little or no dialogue, nonetheless, on how we are going to defend quantum computer systems sooner or later. If quantum computer systems are to develop into such worthwhile property, it’s affordable to venture that they are going to ultimately be the goal of malicious exercise.
I used to be not too long ago invited to be a participant within the Workshop on Cybersecurity of Quantum Computing, co-sponsored by the Nationwide Science Basis (NSF) and the White Home Workplace of Science and Expertise Coverage, the place we examined the rising subject of cybersecurity for quantum computing. Whereas quantum computer systems are nonetheless nascent in some ways, it’s by no means too early to handle looming cybersecurity issues. This put up will discover points associated to creating the self-discipline of cyber safety of quantum computing and description six areas of future analysis within the subject of quantum cybersecurity.
Learn the put up in its entirety.
Trying Forward in 2024
We publish a brand new put up on the SEI Weblog each Monday morning. Within the coming months, search for posts highlighting the SEI’s work in synthetic intelligence, cybersecurity, and edge computing.
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