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Manufacturing’s IT Talent Crunch Creates New Opportunities
For many IT professionals, the manufacturing industry still evokes assembly lines, repetitive work and legacy technology, but the reality inside many factories is far different. Today’s facilities increasingly rely on cloud platforms, artificial intelligence, industrial internet of things (IIoT) devices, robotics, digital twins and cybersecurity to keep production moving. That transformation is creating demand for a different kind of worker—one whose skills were more likely to come from an enterprise IT department than a factory floor. According to Deloitte and The Manufacturing Institute, U.S. manufacturers will need 3.8 million additional workers between 2024 and 2033, with roughly half—1.9 million jobs—at risk of going unfilled if companies can’t close both a growing skills gap and a shortage of qualified applicants. Factories Need Tech Talent Manufacturers are investing heavily in automation, AI and connected systems, fundamentally changing the types of skills they need. “The facto
As AI Adoption Accelerates, Cybersecurity Pros Confront Security Blind Spots
With cybersecurity spending expected to exceed $300 billion globally this year, organizations are accelerating their investments in artificial intelligence-based security platforms. Security teams and cybersecurity professionals, however, are confronting issues such as the lack of visibility into how employees are deploying AI across the network. At the same time, the number of AI-based attacks is growing. Specifically, nearly 48 percent of cybersecurity professionals report that they lack visibility into how employees deploy AI tools across corporate networks, raising fresh concerns about "shadow AI" use within organizations. At the same time, attackers are now using these virtual chatbots and other technologies as part of their arsenal, with approximately 52 percent of cyber pros reporting that AI is helping threat actors more than defenders. These results are part of a report released by cybersecurity firm Bitdefender, which surveyed 1,200 IT and cybersecurity professionals, includi
Entry-Level Cyber Jobs Demanding Mid-Level Skills
The entry-level cybersecurity job isn’t disappearing. It’s becoming something very different. As generative AI (GenAI) takes over more of the repetitive work that once served as a proving ground for junior analysts, employers are increasingly raising expectations for candidates entering the field. Rather than spending their first year triaging alerts or reviewing logs, new hires are now expected to understand cloud environments, evaluate AI-generated outputs, write code and contribute to security operations almost immediately. The result is a growing disconnect between what employers expect and how cybersecurity talent has traditionally been developed. “We’re seeing a clear move toward more senior hiring in cybersecurity,” says Diana Kelley, chief information security officer at Noma Security. “AI is accelerating the shift. However, it’s not the only driver.” She explains that budget pressure and a growing expectation that candidates arrive job-ready are pushing employers to hire fewer
AI Alone Won't Solve Cybersecurity's Skills Gap
For decades, the cybersecurity industry has struggled with high stress and burnout among security professionals. The very nature of the work – assessing risk, countering threats, keeping ahead of vulnerabilities, ensuring personal and corporate data is safe – can weigh on CISOs, senior leaders and staff. The last several years have been especially troublesome for the industry thanks to the growing interest in artificial intelligence, which has the promise of reducing issues like alert fatigue while automating more mundane processes. These technological advances, however, have failed to reassure cybersecurity professionals. A recent survey conducted by the Information Systems Security Association (ISSA) and Omdia finds that while 83 percent of organizations are currently using or planning to adopt AI for cybersecurity, 68 percent of cyber pros report that their jobs have become more difficult over the last 24 months. The researchers also found that the cybersecurity skills gap affects t
Beyond Autocomplete: AI Prompting Strategies for Software Architects
Software engineering is changing quickly. We are moving from manual coding to using AI to help manage complex systems. As large language models (LLMs) become standard tools, senior engineers and CTOs face a new challenge: how to use AI to learn new architectures, understand legacy code, and check that AI code is actually correct. If you treat an AI like a simple autocomplete tool, you’ll get generic, flawed results. To really get ahead, you need to stop asking simple questions and start using clear, structured instructions. This guide shares practical ways to get more out of AI - because let’s face it, you’re using AI every day anyway. By developing the skills and mindset for effective LLM prompting, you can shorten your learning curve, improve code reviews, and build better tools. How to use AI to learn software architectures When migrating systems or exploring unfamiliar architectural paradigms—such as shifting from monolithic backends to event-driven microservices—engineers frequent