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Each January on the SEI Weblog, we current the 10-most visited posts of the earlier yr. This yr’s listing of high 10 posts highlights our work in deepfakes, synthetic intelligence, machine studying, DevSecOps, and zero belief. Posts, which have been printed between January 1, 2022 and December 31, 2022, are introduced beneath in reverse order based mostly on the variety of visits.
#10 In all probability Don’t Depend on EPSS But
by Jonathan Spring
Vulnerability administration includes discovering, analyzing, and dealing with new or reported safety vulnerabilities in data methods. The providers offered by vulnerability administration methods are important to each laptop and community safety. This weblog submit evaluates the professionals and cons of the Exploit Prediction Scoring System (EPSS), which is a data-driven mannequin designed to estimate the likelihood that software program vulnerabilities can be exploited in follow.
The EPSS mannequin was initiated in 2019 in parallel with our criticisms of the Frequent Vulnerability Scoring System (CVSS) in 2018. EPSS was developed in parallel with our personal try at enhancing CVSS, the Stakeholder-Particular Vulnerability Categorization (SSVC); 2019 additionally noticed model 1 of SSVC. This submit will concentrate on EPSS model 2, launched in February 2022, and when it’s and isn’t applicable to make use of the mannequin. This newest launch has created a variety of pleasure round EPSS, particularly since enhancements to CVSS (model 4) are nonetheless being developed. Sadly, the applicability of EPSS is far narrower than folks may count on. This submit will present my recommendation on how practitioners ought to and mustn’t use EPSS in its present type.
Learn the submit in its entirety.
#9 Containerization on the Edge
by Kevin Pitstick and Jacob Ratzlaff
Containerization is a expertise that addresses lots of the challenges of working software program methods on the edge. Containerization is a virtualization technique the place an utility’s software program information (together with code, dependencies, and configuration information) are bundled right into a bundle and executed on a number by a container runtime engine. The bundle known as a container picture, which then turns into a container when it’s executed. Whereas just like digital machines (VMs), containers don’t virtualize the working system kernel (normally Linux) and as a substitute use the host’s kernel. This method removes a few of the useful resource overhead related to virtualization, although it makes containers much less remoted and transportable than digital machines.
Whereas the idea of containerization has existed since Unix’s chroot system was launched in 1979, it has escalated in reputation over the previous a number of years after Docker was launched in 2013. Containers are actually extensively used throughout all areas of software program and are instrumental in lots of tasks’ steady integration/steady supply (CI/CD) pipelines. On this weblog submit, we talk about the advantages and challenges of utilizing containerization on the edge. This dialogue might help software program architects analyze tradeoffs whereas designing software program methods for the sting.
Learn the submit in its entirety.
#8 Ways and Patterns for Software program Robustness
by Rick Kazman
Robustness has historically been regarded as the flexibility of a software-reliant system to maintain working, according to its specs, regardless of the presence of inner failures, defective inputs, or exterior stresses, over a protracted time frame. Robustness, together with different high quality attributes, equivalent to safety and security, is a key contributor to our belief {that a} system will carry out in a dependable method. As well as, the notion of robustness has extra lately come to embody a system’s capability to resist adjustments in its stimuli and atmosphere with out compromising its important construction and traits. On this latter notion of robustness, methods needs to be malleable, not brittle, with respect to adjustments of their stimuli or environments. Robustness, consequently, is a extremely vital high quality attribute to design right into a system from its inception as a result of it’s unlikely that any nontrivial system might obtain this high quality with out conscientious and deliberate engineering. On this weblog submit, which is excerpted and tailored from a lately printed technical report, we are going to discover robustness and introduce techniques and patterns for understanding and attaining robustness.
Learn the submit in its entirety.
View a podcast on this work.
#7 The Zero Belief Journey: 4 Phases of Implementation
by Timothy Morrow and Matthew Nicolai
Over the previous a number of years, zero belief structure has emerged as an vital matter inside the subject of cybersecurity. Heightened federal necessities and pandemic-related challenges have accelerated the timeline for zero belief adoption inside the federal sector. Non-public sector organizations are additionally trying to undertake zero belief to deliver their technical infrastructure and processes according to cybersecurity greatest practices. Actual-world preparation for zero belief, nevertheless, has not caught up with current cybersecurity frameworks and literature. NIST requirements have outlined the specified outcomes for zero belief transformation, however the implementation course of remains to be comparatively undefined. Zero belief can’t be merely carried out via off-the-shelf options because it requires a complete shift in the direction of proactive safety and steady monitoring. On this submit, we define the zero belief journey, discussing 4 phases that organizations ought to handle as they develop and assess their roadmap and related artifacts towards a zero belief maturity mannequin.
Overview of the Zero Belief Journey
Because the nation’s first federally funded analysis and growth heart with a transparent emphasis on cybersecurity, the SEI is uniquely positioned to bridge the hole between NIST requirements and real-world implementation. As organizations transfer away from the perimeter safety mannequin, many are experiencing uncertainty of their seek for a transparent path in the direction of adopting zero belief. Zero belief is an evolving set of cybersecurity paradigms that transfer defenses from static, network-based perimeters to concentrate on customers, belongings, and assets. The CERT Division on the Software program Engineering Institute has outlined a number of steps that organizations can take to implement and preserve zero belief structure, which makes use of zero belief ideas to plan industrial and enterprise infrastructure and workflows. These steps collectively type the idea of the zero belief journey.
Learn the submit in its entirety.
View a podcast on this work.
#6 Two Classes of Structure Patterns for Deployability
by Rick Kazman
Aggressive pressures in lots of domains, in addition to growth paradigms equivalent to Agile and DevSecOps, have led to the more and more widespread follow of steady supply or steady deployment—fast and frequent adjustments and updates to software program methods. In in the present day’s methods, releases can happen at any time—probably a whole bunch of releases per day—and every may be instigated by a distinct workforce inside a company. Having the ability to launch incessantly implies that bug fixes and safety patches shouldn’t have to attend till the following scheduled launch, however slightly may be made and launched as quickly as a bug is found and stuck. It additionally implies that new options needn’t be bundled right into a launch however may be put into manufacturing at any time. On this weblog submit, excerpted from the fourth version of Software program Structure in Apply, which I coauthored with Len Bass and Paul Clements, I talk about the standard attribute of deployability and describe two related classes of structure patterns: patterns for structuring providers and for how you can deploy providers.
Steady deployment is just not fascinating, and even attainable, in all domains. In case your software program exists in a fancy ecosystem with many dependencies, it will not be attainable to launch only one a part of it with out coordinating that launch with the opposite elements. As well as, many embedded methods, methods residing in hard-to-access areas, and methods that aren’t networked could be poor candidates for a steady deployment mindset.
This submit focuses on the big and rising numbers of methods for which just-in-time characteristic releases are a major aggressive benefit, and just-in-time bug fixes are important to security or safety or steady operation. Typically these methods are microservice and cloud-based, though the methods described right here are usually not restricted to these applied sciences.
Learn the submit in its entirety.
View an SEI podcast on this matter.
#5 A Case Examine in Making use of Digital Engineering
by Nataliya Shevchenko and Peter Capell
A longstanding problem in giant software-reliant methods has been to offer system stakeholders with visibility into the standing of methods as they’re being developed. Such data is just not all the time simple for senior executives and others within the engineering path to amass when wanted. On this weblog submit, we current a case examine of an SEI undertaking by which digital engineering is getting used efficiently to offer visibility of merchandise underneath growth from inception in a requirement to supply on a platform.
One of many customary conventions for speaking in regards to the state of an acquisition program is the program administration assessment (PMR). Because of the accumulation of element introduced in a typical PMR, it may be onerous to determine duties which might be most urgently in want of intervention. The promise of contemporary expertise, nevertheless, is that a pc can increase human capability to determine counterintuitive features of a program, successfully rising its accuracy and high quality. Digital engineering is a expertise that may
- improve the visibility of what’s most pressing and vital
- determine how adjustments which might be launched have an effect on an entire system, in addition to elements of it
- allow stakeholders of a system to retrieve well timed details about the standing of a product transferring via the event lifecycle at any cut-off date
Learn the submit in its entirety.
#4 A Hitchhiker’s Information to ML Coaching Infrastructure
by Jay Palat
{Hardware} has made a big impact on the sector of machine studying (ML). Lots of the concepts we use in the present day have been printed many years in the past, however the price to run them and the information essential have been too costly, making them impractical. Latest advances, together with the introduction of graphics processing models (GPUs), are making a few of these concepts a actuality. On this submit we’ll take a look at a few of the {hardware} components that impression coaching synthetic intelligence (AI) methods, and we’ll stroll via an instance ML workflow.
Why is {Hardware} Vital for Machine Studying?
{Hardware} is a key enabler for machine studying. Sara Hooker, in her 2020 paper “The {Hardware} Lottery” particulars the emergence of deep studying from the introduction of GPUs. Hooker’s paper tells the story of the historic separation of {hardware} and software program communities and the prices of advancing every subject in isolation: that many software program concepts (particularly ML) have been deserted due to {hardware} limitations. GPUs allow researchers to beat lots of these limitations due to their effectiveness for ML mannequin coaching.
Learn the submit in its entirety.
#3 A Technical DevSecOps Adoption Framework
by Vanessa Jackson and Lyndsi Hughes
DevSecOps practices, together with continuous-integration/continuous-delivery (CI/CD) pipelines, allow organizations to reply to safety and reliability occasions rapidly and effectively and to provide resilient and safe software program on a predictable schedule and price range. Regardless of rising proof and recognition of the efficacy and worth of those practices, the preliminary implementation and ongoing enchancment of the methodology may be difficult. This weblog submit describes our new DevSecOps adoption framework that guides you and your group within the planning and implementation of a roadmap to useful CI/CD pipeline capabilities. We additionally present perception into the nuanced variations between an infrastructure workforce centered on implementing a DevSecOps paradigm and a software-development workforce.
A earlier submit introduced our case for the worth of CI/CD pipeline capabilities and we launched our framework at a excessive stage, outlining the way it helps set priorities throughout preliminary deployment of a growth atmosphere able to executing CI/CD pipelines and leveraging DevSecOps practices.
Learn the submit in its entirety.
#2 What’s Explainable AI?
by Violet Turri
Contemplate a manufacturing line by which staff run heavy, probably harmful gear to fabricate metal tubing. Firm executives rent a workforce of machine studying (ML) practitioners to develop a synthetic intelligence (AI) mannequin that may help the frontline staff in making protected selections, with the hopes that this mannequin will revolutionize their enterprise by enhancing employee effectivity and security. After an costly growth course of, producers unveil their complicated, high-accuracy mannequin to the manufacturing line anticipating to see their funding repay. As a substitute, they see extraordinarily restricted adoption by their staff. What went fallacious?
This hypothetical instance, tailored from a real-world case examine in McKinsey’s The State of AI in 2020, demonstrates the essential position that explainability performs on this planet of AI. Whereas the mannequin within the instance might have been protected and correct, the goal customers didn’t belief the AI system as a result of they didn’t know the way it made selections. Finish-users deserve to grasp the underlying decision-making processes of the methods they’re anticipated to make use of, particularly in high-stakes conditions. Maybe unsurprisingly, McKinsey discovered that enhancing the explainability of methods led to elevated expertise adoption.
Explainable synthetic intelligence (XAI) is a strong software in answering essential How? and Why? questions on AI methods and can be utilized to deal with rising moral and authorized issues. Consequently, AI researchers have recognized XAI as a essential characteristic of reliable AI, and explainability has skilled a latest surge in consideration. Nevertheless, regardless of the rising curiosity in XAI analysis and the demand for explainability throughout disparate domains, XAI nonetheless suffers from numerous limitations. This weblog submit presents an introduction to the present state of XAI, together with the strengths and weaknesses of this follow.
Learn the submit in its entirety.
View an SEI Podcast on this matter.
#1 How Simple is it to Make and Detect a Deepfake?
by Catherine A Bernaciak and Dominic Ross
A deepfake is a media file—picture, video, or speech, sometimes representing a human topic—that has been altered deceptively utilizing deep neural networks (DNNs) to change an individual’s id. This alteration sometimes takes the type of a “faceswap” the place the id of a supply topic is transferred onto a vacation spot topic. The vacation spot’s facial expressions and head actions stay the identical, however the look within the video is that of the supply. A report printed this yr estimated that there have been greater than 85,000 dangerous deepfake movies detected as much as December 2020, with the quantity doubling each six months since observations started in December 2018.
Figuring out the authenticity of video content material may be an pressing precedence when a video pertains to national-security issues. Evolutionary enhancements in video-generation strategies are enabling comparatively low-budget adversaries to make use of off-the-shelf machine-learning software program to generate pretend content material with rising scale and realism. The Home Intelligence Committee mentioned at size the rising dangers introduced by deepfakes in a public listening to on June 13, 2019. On this weblog submit, we describe the expertise underlying the creation and detection of deepfakes and assess present and future risk ranges.
The big quantity of on-line video presents a chance for the US authorities to boost its situational consciousness on a world scale. As of February 2020, Web customers have been importing a mean of 500 hours of recent video content material per minute on YouTube alone. Nevertheless, the existence of a variety of video-manipulation instruments implies that video found on-line can’t all the time be trusted. What’s extra, as the thought of deepfakes has gained visibility in standard media, the press, and social media, a parallel risk has emerged from the so-called liar’s dividend—difficult the authenticity or veracity of reliable data via a false declare that one thing is a deepfake even when it isn’t.
Learn the submit in its entirety.
View the webcast on this work.
Wanting Forward in 2023
We publish a brand new submit on the SEI Weblog each Monday morning. Within the coming months, search for posts highlighting the SEI’s work in synthetic intelligence, digital engineering, and edge computing.
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