Information Week

Approximately 15% to 20% of people are neurodivergent, and that percentage could be even higher in STEM fields. Neurodiversity is a broad term that includes many different conditions: autism spectrum disorder (ASD); attention-deficit/hyperactivity disorder (ADHD); and dyslexia, to name just a few.   As cybersecurity stakeholders continue to discuss filling the talent gap and tackling today’s security challenges, neurodiverse talent is a valuable resource. But attracting and working with this talent requires leaders to recognize the different needs of neurodivergent people and to foster work environments that make the most of their skills.   Neurodiversity as an Asset  Many major companies, such as Microsoft and SAP, recognize the value of neurodiverse talent and have formal recruiting programs. Jodi Asbell-Clarke, PhD, heard firsthand from companies with these kinds of hiring initiatives as she conducted research for her book on teaching neurodivergent people in STEM.   “I expected to hear something like, ‘Oh, the CEO’s nephew was autistic, and we wanted to do the right thing. I expected to hear things about philanthropy and equity, and that w as not what I heard at all,” Asbell-Clarke, a senior leader and research scientist with TERC, a nonprofit focused on advancing STEM education, told InformationWeek. “They were saying it’s because the talent. ‘We consider neurodiversity in our workforce our competitive advantage.’ These are the most persistent and creative and systematic problem solvers.”   Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI How can that talent be put to work in the cybersecurity workforce?   Ian Campbell was diagnosed with major depressive disorder and generalized anxiety early in his life. Then, at the start of the pandemic, he was diagnosed as autistic. Cybersecurity was not his first career. He was providing tech support for the US House of Representatives before he made the switch to security. Currently, he is a senior security operations engineer at DomainTools, a domain research service company.   Throughout his career, Campbell has found hyperfocus to be one of his strengths. “Scrolling through tens of thousands of things, of log files, hyper-focusing on that, and being able to intuitively pattern match or detect pattern deviations was a huge benefit in both tech support and security,” he says.   Megan Roddie-Fonseca, senior security engineer at cloud monitoring as a service company Datadog, is autistic and has ADHD. She shares how productivity is one of her biggest strengths.   “I find efficient ways to do things,” she says. “I use that efficiency to be able to tackle tasks … in a way that some people might not get the same amount of work done in the same amount of time.”  Related:Curtail Cloud Spend With These Strategies Challenges in the Workplace  While awareness of neurodiversity, and the nuance within that very broad term, is growing, there are still plenty of potential challenges in the workplace.   Neurodivergent people face the tricky question of disclosure. Should they tell their managers and coworkers about their diagnoses? Neurodiversity is more openly discussed, but that doesn’t mean there aren’t people who will misunderstand or react to disclosure negatively.  “A lot of people I know who are neurodivergent … haven’t come out as neurodivergent because they don’t want to be seen that way,” says Campbell. “They don’t want, frankly, their careers limited by someone who has a poor view of neurodivergence.”   The decision to conceal neurodivergent traits, known as masking, can be a difficult undertaking.   “Masking … is basically suppressing your own neurodivergent urges and needs for the sake of function in a world that’s not built for us, and masking is incredibly tiring,” says Campbell.   The decision to disclose or not is a personal choice, one that is likely influenced by the level of support people can expect from a workplace.   Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness The way people communicate at work, for example, can potentially lead to misunderstandings. One study using the classic game of telephone — a group passes information to one another down a line of several people — illustrates these potential challenges.    The study broke its subjects into three groups of people: autistic, non-autistic, and mix of both. The first two groups exhibited the same skill level relating to information transfer. But communication problems arose in the mixed group.   In a cybersecurity workplace, neurotypical and neurodiverse people are going to need to find ways to communicate with one another effectively. Some work environments will foster opportunities to learn how to best build those communication pathways. Some won’t.   The physical aspects of the work environment can also be a challenge for neurodivergent people who have sensory processing issues. The lighting and sound levels of an office, for example, can result in sensory overwhelm for some people.    Hiring and Supporting Neurodiverse Talent   Enterprises can attract neurodiverse talent through formal hiring programs or by working with external organizations, such as Specialisterne. Regardless of the approach, partnered or solo, hiring managers and cybersecurity team leaders need to evaluate and adapt their strategies.   During the interview process, Asbell-Clarke recommends matching that short experience to the work you hope to see in the actual work environment. If you are hiring someone who will be conducting highly detailed work under time constraints, mirror that process when evaluating candidates.   “If you want to see people’s best problem-solving, give them the time and space to solve a task and then ask them about how they did it,” she says.   In the cybersecurity work environment, managers will find that getting the best work from their neurodivergent workers will require varying approaches.   “Neurodiversity is this massive spectrum,” says Jackie McGuire, senior security strategist at Cribl, a unified data management platform. “It can be confusing as a manager because you can have two team members who are on exact opposite ends of that spectrum who need completely polar opposite things.”  For example, one neurodivergent person may thrive in a structured environment, while another may do their best work with a high degree of freedom. Additionally, the ways neurodivergent people best

This slideshow isn’t about kid’s stuff like how many companies are STILL using Microsoft Word macro-enabled templates like it’s a thrice-shouted Beetlejuice dare. Oh no, these are the hardcore terrors that really happened in 2024, many of which are still scaring the bejesus out of people now. And probably will continue into 2025, too. Some scary stuff is as sticky as melted Halloween chocolates.  Read on if you dare. Then lock the doors and pay your software developers and cybersecurity people more!   source

No technology in history has achieved an adoption curve that rivals generative AI (GenAI). Already, organizations use it for everything from chatbots and content creation to product design and software development. The technology boosts efficiency, trims costs, and unlocks innovation.  Yet for all the gains there’s still a good deal of pain. Too often, generative AI systems do not recognize basic facts and information that humans take for granted. For example, they might misinterpret or misclassify events and produce flawed output, struggle to generate desired content, or fall short on more complex tasks that require a combination of text, audio, and video.   That’s where multimodal AI enters the equation. “Multimodal AI models are trained with multiple types of data simultaneously, such as images, video, audio, and text. This enables them to create a shared data representation that improves performance for different tasks,” explains Arun Chandrasekaran, distinguished VP and analyst for artificial intelligence at Gartner.  Adds Scott Likens, US and global chief AI engineering officer at PwC: “Multimodal AI can tackle more complex challenges, create more personalized experiences, and help companies adapt more effectively. It’s about versatility and deeper insights, which are crucial to staying ahead.”  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report Multimodal AI potentially touches chatbots, data analytics, robotics, and numerous other areas. According to research conducted by Gartner, only about 1% of companies were using the technology in 2023 but the figure is projected to jump to 40% by 2027. The technology will have a “transformational” impact on the business world, Gartner reports. “It enables use cases that previously weren’t possible,” Chandrasekaran says.  AI Comes to its Senses  What makes multimodal AI so appealing — and powerful — is its ability for AI to act more like a human being because it understands the world better. “Traditional machine learning uses a specific training set to predict output,” states Matthew Kropp, a partner and managing director at Boston Consulting Group. “Later, you look for ways to adjust the weights in the model. Multimodal AI expands the training data in the pursuit of more realistic results.”  PwC’s Likens compares multimodal AI to the human ability to multitask. “You can ask a question via audio and receive a written response or submit an image and then ask questions about it. The interoperability between mediums is seamless. For business leaders, that means making smarter decisions faster. You’re not just looking at text or just an image; you’re seeing the whole picture,” he says.  Related:Keynote Sneak Peek: Forrester Analyst Details Align by Design and AI Explainability The result is systems that are far better aligned to handle real world tasks — and tools that create more personalized experiences and deeper insights. For example, a chatbot might handle both text and images. This makes it possible for a user to describe a problem in words but also upload a photo of a broken product. A multimodal AI system might also understand video content and seamlessly extract cues that provide context — and answers.  The results can be impressive. Multimodal systems can introduce visual question-answering and even complex audio and video generation, Chandrasekaran explains. This includes creating AI podcasts and instructional materials. Organizations also are better equipped to tune into market and consumer sentiment through various types of data.  Over the next few years, the range of multimodal inputs will increase beyond text, images, and video, Chandrasekaran says. Systems are likely to incorporate a greater range of audio data, sensor and IoT data, log files, code snippets and more. This will boost the accuracy, contextual awareness, and overall utility of chatbots, robots, diagnostics systems, and predictive maintenance tools.  Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations Evolving Beyond the Bot  Multimodal models come with a major caveat: Stringing together a mélange of unimodal data models is not the same as constructing a purpose-built multimodal framework. “Multimodal data must be aligned and integrated. It is more complex because it has varying degrees of quality and comes in different formats than unimodal data,” Chandrasekaran explains.  Specific tools that aid in building multimodal frameworks are evolving rapidly. Cloud platforms AWS, Google, and Azure have introduced multimodal features into their toolkits. Pre-trained models like OpenAI’s CLIP (Contrastive Language-Image Pretraining) and BERT (Bidirectional Encoder Representations from Transformers) have appeared. And multimodal libraries and tools like MMDet (Multimodal Detection) and Hugging Face Transformers tie together diverse data sets.  CIOs and IT teams must take a hands-on approach to multimodal AI. An effective framework must fit an organization’s specific data and objectives, and data must be clean and clearly labeled. There’s also a need to address business risks that include data bias, privacy concerns, fairness standards, copyright concerns, and overall data accuracy. This requires appropriate training and evaluation techniques like cross-validation and accuracy metrics.  “Because multimodal AI involves diverse inputs — text, images, audio, and video — maintaining consistent data quality is key,” Likens notes. “Privacy concerns are equally critical, because multimodal data can reveal unintended patterns.” It’s also critical to keep humans in the loop. “Investing in responsible AI from the start helps companies manage risks, build trust, and stay ahead of government regulations,” he argues.  For now, organizations can benefit by reviewing applications, tools, and partners, Kropp says. This includes using open-source models and tools that help lower the entry barrier and reduce risks associated with major IT commitments. “Matching the model and the vendor with your desired use case is important. Different combinations result in different and potentially better results,” he notes.  Structural changes may also be in order, Chandrasekaran says. Among his suggestions? “Educate your AI team on multimodality, including the benefits and risks. Break up AI technical silos by encouraging AI experts to work on projects outside their area of technical specialization, such as natural language processing and computer vision. Expose AI teams to vendors that focus on multimodal models as part of the overall education process.”  Make no mistake, multimodal AI will emerge as a powerful force

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Generative AI’s wildfire adoption is both a blessing and a curse. On one hand, many people are using GenAI to work more efficiently, and businesses are trying to scale it in an enterprise-class way. Meanwhile, the courts and regulators aren’t moving at warp speed, so companies need to be very smart about what they’re doing or risk intellectual property (IP) infringement, leakage, misuse and abuse.   “The law is certainly behind the business and technology adoptions right now, so a lot of our clients are entering into the space, adopting AI, and creating their own AI tools without a lot of guidance from the courts, in particular around copyright law,” says Sarah Bro, a partner at law firm McDermott Will & Emery. “I’ve been really encouraged to see business and legal directives help mitigate risks or manage relationships around the technology and use, and parties really trying to be proactively thinking about how to address things when we don’t have clear-cut legal guidance on every issue at this point.”  Why C-Suites and Boards Need to Get Ahead of This Now  GenAI can lead to four types of IP infringement: copyright, trademark, patent, and trade secrets. Thus far, there’s been more attention paid to the business competitiveness aspect of GenAI than the potential risks of its usage, which means that companies are not managing risks as adeptly as they should.  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report “The C-suite needs to think about how employees are using confidential and proprietary data.  What gives us a competitive advantage?” says Brad Chin, IP partner at the Bracewell law firm. “Are they using it in marketing for branding a new product or process? Are they using generative AI to create reports? Is the accounting department using generative AI to analyze data they might get from a third party?”   Historically, intellectual property protection has involved non-disclosure agreements (NDAs), and that has not changed. In fact, NDAs should cover GenAI. However, according to Chin, using the company’s data, and perhaps others’ data, in a GenAI tool raises the question of whether the company’s trade secrets are still protected.  “We don’t have a lot of court precedent on that yet, but that’s one of the considerations courts look at in a company’s management of its trade secrets: what procedures, protocols, practices they put in place, so it’s important for C-suite executives to understand that risk is not only of the information their employees are putting into AI, but also the AI tools that their employees may be using with respect to someone else’s information or data,” says Chin. “Most company NDAs and general corporate agreements don’t have provisions that account for the use of generative AI or AI tools.”  Related:Keynote Sneak Peek: Forrester Analyst Details Align by Design and AI Explainability Some features of AI development make GenAI a risk from a copyright and confidentiality standpoint.  “To train machine learning models properly, you need a lot of data. Most savvy AI developers cut their teeth in academic environments, where they weren’t trained to consider copyright or privacy. They were simply provided public datasets to play with,” says Kirk Sigmon, an intellectual property lawyer and partner at the Banner Witcoff law firm, in an email interview. “As a result, AI developers inside and outside the company aren ’t being limited in terms of what they can use to train and test models, and they’re very tempted to grab whatever they can to improve their models. This can be dangerous: It means that, perhaps more than other developers they might be tempted to overlook or not even think about copyright or confidentiality issues.”  Similarly, the art and other visual elements used in generative AI, such as Gemini and DALL-E, may be copyright protected, and logos may be trademark protected.  GenAI could also result in patent-related issues, according to Bracewell’s Chin.   Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations “A third party could get access to information inputted into generative AI, which comes up with five different solutions,” says Chin. “If the company that has the information then files patents on that technology, it could exclude or preclude that original company from getting that part of the market.”  Boards and C-Suites Need to Prioritize GenAI Discussions  Boards and C-suites that have not yet had discussions about the potential risks of GenAI need to start now.  “Employees can use and abuse generative AI even when it is not available to them as an official company tool. It can be really tempting for a junior employee to rely on ChatGPT to help them draft formal-sounding emails, generate creative art for a PowerPoint presentation and the like. Similarly, some employees might find it too tempting to use their phone to query a chatbot regarding questions that would otherwise require intense research,” says Banner Witcoff’s Sigmon. “Since such uses don’t necessarily make themselves obvious, you can’t really figure out if, for example, an employee used generative AI to write an email, much less if they provided confidential information when doing so. This means that companies can be exposed to AI-related risk even when, on an official level, they may not have adopted any AI.”  Emily Poler, founding partner at Poler Legal, wonders what would happen if the GenAI platform a company uses becomes unavailable.  “Nobody knows what’s going to happen in the various cases that have been brought against companies offering AI platforms, but one possible scenario is that OpenAI and other companies in the space have to destroy the LLM they’ve created because the LLMs and/or the output from those LLMs amounts to copyright infringement on a massive scale,” says Poler in an email interview. “Relatedly, what happens to your company’s data if the generative AI platform you’re using goes bankrupt? Another company could buy up this data in a bankruptcy proceeding and your company might not have a say.”  Another point to consider is whether the generative AI platform can use a company’s data

“Navigating the Risks: Why SaaS Management is Crucial for Compliance and Security in Healthcare & Finance“ Thursday, November 14, 2024 – On-Demand In today’s world of remote work and SaaS proliferation, organizations in healthcare and finance face increasing challenges in managing data as it moves to SaaS applications, all while ensuring compliance with HIPAA, NIST, PCI DSS and SOC2. Join Auvik SaaS Management’s Director & Tech Evangelist, Steve Petryschuk, and Senior Product Manager, Ben Botti, as they explore the critical role of SaaS management in improving security and maintaining compliance in these highly regulated industries. This webinar will cover:• Mitigating security risks with effective SaaS discovery.• Strategies for safeguarding data and preventing unauthorized access.• Live demo of Auvik’s automated SaaS visibility, risk management, and compliance reporting. Learn how to streamline your SaaS operations, reduce wasted licenses, and ensure adherence to regulatory standards in your organization. Speakers:Steve Petryschuk, Director & Tech Evangelist, AuvikAt Auvik, Steve works with prospects, clients, and the IT community at large to identify, research, and analyze complex IT Operations challenges, helping guide the Auvik roadmap to better service the IT community. Steve holds a Bachelor of Engineering and Management and is a registered Professional Engineer in Ontario with IT, networking, and IT security experience spanning product management, devops, systems admin, solutions engineer, and technical trainer roles. Ben Botti, Senior Product Manager – SaaS Management, AuvikBen specializes in partnering with MSPs and IT teams to address their most critical challenges, with nearly 15 years of expertise in the space. Initially honing his skills within a regional MSP, he now contributes to developing tools designed to support IT teams as a Senior Product Manager at Auvik. His background covers Automation, Telecom, and now, overseeing solutions in Auvik’s SaaS Management product, reflecting his commitment to enhancing and building the best tools for frictionless IT. Moderator: Michael Krieger, InformationWeek Offered Free by: Auvik Networks, Inc. See All Resources from: Auvik Networks, Inc. source

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“State of ITSM in Retail“ An InformationWeek Report | Sponsored by TeamDynamix In today’s competitive market, retailers must harness the power of robust technology service delivery to thrive. Whether managing a complex e-commerce platform or a network of physical stores—or often both—retailers rely heavily on dependable IT systems. These systems pave the way for a seamless omnichannel customer experience, personalized marketing, streamlined supply chain operations, and flawless business functionality. Key Benefits Could Include,-Enhanced Efficiency: Automation reduces administrative costs and improves Service Level Agreements (SLAs).-Future-Ready Solutions: Introduce self-service functionality and leverage emerging technologies to stay ahead.-Enterprise Service Management (ESM) Innovation: Extend ITSM capabilities across retail departments for comprehensive service enhancements. Discover how cutting-edge IT solutions can elevate your retail operations. Download our full report to learn more. Offered Free by: TeamDynamix See All Resources from: TeamDynamix Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. source

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Accessibility testing, often referred to as a11y testing, has traditionally depended heavily on manual processes. However, the integration of automation is transforming our approach to achieving accessibility compliance. The term “a11y” is a numeronym for “accessibility,” where the number 11 represents the count of letters between the “a” and the “y.” At Chase, our A11y efforts are driven by feedback from our customers with disabilities. Because we release a new version of the Chase Mobile® App every two weeks in addition to maintaining Chase.com without compromising accessibility, testing and automation are crucial. Importance and Role of Accessibility Automation Automated accessibility testing is crucial for several reasons. It has emerged as a powerful ally in the quest for digital accessibility and helps ensure digital content is inclusive and compliant with accessibility standards, such as Web Content Accessibility Guidelines (WCAG), and to reduce the risk of discrimination lawsuits. By leveraging automated tools and scripts, organizations can efficiently identify and rectify accessibility issues across various digital interfaces, including websites, mobile applications, and software platforms. Test Accuracy and Precision Automation can cover various aspects of accessibility, including keyboard navigation, semantic HTML structure, Accessible Rich Internet Applications (ARIA) attributes and more. This comprehensive coverage helps identify a wide range of accessibility issues. One of the primary advantages of accessibility automation is its ability to provide accurate and precise results. Automated tools can detect accessibility violations with a high degree of accuracy, offering detailed insights into compliance issues that might otherwise go unnoticed. Benefits of Accessibility Automation The benefits of a11y automation are vast. It saves time and resources, catches issues early in the development cycle, and provides consistent and scalable testing. Early Detection: Automation tools can quickly identify accessibility violations during development, allowing teams to address issues early in the process. Increased Efficiency: Automation accelerates the testing process by swiftly scanning for accessibility violations, minimizing the time and effort required for comprehensive evaluations. Consistency & Reliability: Automated tests deliver consistent results, reducing human error and ensuring a reliable assessment of accessibility compliance. Cost-Effectiveness: Over time, automated accessibility testing proves cost-effective by reducing the need for extensive manual testing efforts. Our Technical Approach Our approach to accessibility automation is grounded in creating a seamless and efficient process that ensures accessibility from the earliest stages of development. External accessibility testing tools and software, combined with our custom Unified Digital Framework (UDF) scripts, plays a critical role in this process by identifying both common and complex accessibility issues before they reach production. In short, UDF scripts provide flexibility and depth in our accessibility testing, allowing us to go beyond out-of-the-box solutions and ensure our products meet both basic and advanced accessibility standards. They allow us to improve test coverage and ensure a more inclusive digital experience by developing customized checks for complex scenarios, dynamic content, and ARIA and enhanced semantic HTML testing. By leveraging built-in accessibility rules, we can automate the detection of common accessibility issues such as missing document-title, empty-heading, html-has-lang, html-lang-valid, and meta-viewport without extensive manual testing. These are often the most frequent accessibility barriers that prevent users with disabilities from fully interacting with digital products. The built-in tool’s ability to check for these issues directly within our code allows us to fix them early and rapidly, contributing to a faster time-to-market and higher overall quality. However, accessibility is more than just catching low-hanging fruit. That’s where our UDF scripts come in. These scripts allow us to extend the tool’s capabilities and address more nuanced accessibility concerns, such as ensuring smooth keyboard navigation and focus management, avoiding keyboard traps, and maintaining a clear semantic HTML structure. Our use of ARIA attributes ensures that dynamic content is properly announced to screen readers, making our web applications more inclusive. This dual approach, combining the power of automated testing with the precision of customized UDF scripts, forms the backbone of our accessibility strategy. It allows us to confidently meet WCAG guidelines while improving the overall usability of our products, ensuring that accessibility is an integral part of our development lifecycle rather than an afterthought. Challenges of Accessibility Automation Despite its benefits, accessibility automation also presents unique challenges: Complexity of Testing: Automated tests may struggle with complex interactions or dynamic content, leading to potential false positives or negatives. Technical Limitations: Some accessibility issues require human judgment and intuition to identify accurately, challenging the efficacy of purely automated approaches. The Hybrid Approach: Automation and Manual Testing To address these challenges, a hybrid approach combining automated testing and manual testing with expert audits is often the most effective strategy. Automation provides speed and scalability, while human intervention ensures accuracy and depth in accessibility evaluations. A recent hybrid approach test scenario resulted in 52% of applicable checkpoints being tested using automation, with the remaining tested manually. As we continue to test, we’ll be able to evaluate our speed, scalability and effectiveness to increase our ratio of automated checkpoints. Applying Automation Testing to Mobile Apps For the Chase Mobile App, automated accessibility checks are powered to scan through hundreds of screens on both the Android and iOS apps focusing primarily on 5 WCAG checkpoints. A set of validations that may have previously required more than 5 hours of effort using several tools can now be performed within minutes, reducing our time to market significantly. To achieve this, we leverage the capabilities of a third-party software development kit (SDK) through a single point integration on our UDF. This allows us to utilize and extend the tool capabilities across our entire suit of automated tests via a runtime command. WCAG, WCAG 2.0 level validations for Contrast, Accessibility Name, Focus Trap are automatically triggered as test scripts execute. Checkpoint validations are continuously and seamlessly performed as each test script flows from one screen to the other. A report is generated per test script, furnished with a screenshot and details that include the id of the element, nature and the severity of the non-compliance, and a detailed description of the WCAG checkpoint violation detected. We are also