Daily Tech Digest by Kannan Subbiah: Manufacturing

Showing posts with label Manufacturing. Show all posts

Daily Tech Digest - May 12, 2026

Quote for the day:

"Leadership seems mystical. It's actually methodical. The method is learnable and repeatable — and when followed, produces results that feel magical." -- Gordon Tredgold

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The ghost in the machine: Why AI ROI dies at the human finish line

In "The Ghost in the Machine," Andrew Hallinson argues that the primary barrier to achieving a return on investment for artificial intelligence is not technical inadequacy but human psychological resistance. Despite multi-million dollar investments in advanced data stacks, many organizations suffer from what Hallinson terms an "aversion tax"—the significant loss of potential value caused by low adoption rates and human friction. This resistance stems from three psychological barriers: the "black box paradox," where lack of transparency breeds distrust; "identity threat," where employees feel the technology undermines their professional intuition and autonomy; and the "perfection trap," which involves holding algorithms to much higher standards than human peers. Hallinson illustrates a solution through his experience at ADP, where success was achieved by shifting the focus from restrictive data governance to empowering data democratization. By treating employees as strategic partners and behavioral architects rather than just data processors, leaders can overcome these hurdles. Ultimately, the article posits that technical excellence is wasted if cultural integration is ignored. For executives, the mandate is clear: building an AI-ready culture is just as critical as the engineering itself, as ignoring the human element transforms expensive AI tools into mere "shelfware" that fails to deliver on its mathematical promise.

AI Finds Code Vulnerabilities – Fixing Them Is the Real Challenge

The article "AI Finds Code Vulnerabilities – Fixing Them is the Real Challenge," published on DevOps Digest, explores the double-edged sword of utilizing artificial intelligence in software security. While AI-driven tools have revolutionized the ability to scan vast codebases and identify potential security flaws with unprecedented speed, the author argues that the industry's bottleneck has shifted from detection to remediation. Automated scanners often generate an overwhelming volume of alerts, many of which are false positives or lack the necessary context for immediate action. This "security debt" places a significant burden on development teams who must manually verify and patch each issue. Furthermore, the piece highlights that while AI can identify a problem, it often struggles to understand the complex business logic required to fix it without breaking existing functionality. The real challenge lies in integrating AI into the developer's workflow in a way that provides actionable, verified suggestions rather than just a list of problems. The article concludes that for AI to truly enhance cybersecurity, organizations must focus on automating the "fix" phase through sophisticated generative AI and better developer-security collaboration, ensuring that the speed of remediation finally matches the efficiency of automated detection.

Data Replication Strategies: Enterprise Resilience Guide

The article "Data Replication Strategies: Enterprise Resilience Guide" from Scality explores the critical methodologies for ensuring data durability and availability across physical systems. At its core, the guide highlights the fundamental tradeoff between consistency and availability, a tension that dictates how organizations architect their storage infrastructure. Synchronous replication is presented as the gold standard for zero-data-loss scenarios (RPO of zero) because it requires all replicas to acknowledge a write before completion; however, this introduces significant write latency. Conversely, asynchronous replication optimizes for performance and long-distance fault tolerance by propagating changes in the background, which decouples write speed from network latency but risks losing data not yet synchronized. Beyond timing, the content details architectural models like active-passive, where one primary site handles writes, and active-active, where multiple sites simultaneously serve traffic. The article also addresses consistency models such as strong, causal, and session consistency, emphasizing that the choice depends on specific application requirements. By aligning replication strategies with Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), the guide argues that organizations can build a resilient infrastructure capable of surviving data center failures while balancing cost, bandwidth, and performance.

When Should a DevOps Agent Act Without Human Approval?

The article titled "When Should a DevOps Agent Act Without Human Approval?" by Bala Priya C. outlines a comprehensive framework for navigating the transition from manual oversight to autonomous operations in DevOps. Central to this transition is a six-point autonomy spectrum, ranging from basic observation at Level 0 to full autonomy at Level 5. The author highlights that determining the appropriate level of independence for an agent depends on four critical factors: the reversibility of the action, the potential blast radius, the quality of incoming signals, and time sensitivity. For most organizations, the author suggests maintaining agents within Levels 1 through 3, where humans remain primary decision-makers or provide explicit approval for suggested actions. Level 4, which involves agents executing tasks and then notifying humans with a defined override window, should be reserved for narrowly defined, low-risk activities. Full Level 5 autonomy is only recommended after an agent has established a consistent, documented track record of success at lower levels. To manage these shifts safely, the article emphasizes the necessity of robust guardrails, including progressive rollouts, granular approval gates, and high signal-quality thresholds. This structured approach ensures that automation enhances operational efficiency without compromising the security or stability of the production environment, ultimately allowing engineers to focus on higher-value strategic innovation and developmental work.

8 guiding principles for reskilling the SOC for agentic AI

The article "8 guiding principles for reskilling the SOC for agentic AI" outlines a strategic roadmap for Security Operations Centers (SOCs) transitioning toward an AI-driven future. The first principle, embracing the agentic imperative, highlights that moving at "machine speed" is essential to counter advanced adversaries effectively. Leadership plays a critical role by setting a tone of rapid experimentation and "failing fast" to foster internal innovation. While cultural resistance—particularly fears regarding job displacement—is common, the article suggests addressing this by redefining roles around high-value tasks such as AI safety and governance. Hands-on training in secure sandboxes is vital for building practitioner confidence and "model intuition," allowing analysts to recognize when AI outputs are structurally flawed. Crucially, the "human-in-the-loop" principle ensures that non-deterministic AI remains under human oversight through clear escalation paths and audit trails. Beyond technology, the shift requires rethinking organizational structures to move from siloed disciplines to holistic, outcome-based orchestration. Ultimately, fostering collaboration between humans and machines allows analysts to relocate from "inside the process" to a supervisory position above it. By reimagining the operating model, CISOs can transform chaotic environments into calm, efficient hubs where agentic AI handles automated triage while humans provide strategic judgment and effective long-term accountability.

New DORA Report Claims Strong Engineering Foundations Drive AI RoI

The May 2026 InfoQ article summarizes Google Cloud's DORA report, "ROI of AI-Assisted Software Development," which offers a structured framework for calculating financial returns from AI adoption. The research argues that AI acts primarily as an amplifier; rather than repairing flawed processes, it magnifies existing organizational strengths and weaknesses. Consequently, achieving sustainable ROI necessitates robust engineering foundations, including quality internal platforms, disciplined version control, and clear workflows. A central concept introduced is the "J-Curve of value realization," where organizations typically face a temporary productivity dip due to the "tuition cost of transformation"—incorporating learning curves, verification taxes for AI-generated code, and essential process adaptations. Despite this initial drop, the report models a substantial first-year ROI of 39% for a typical 500-person organization, with a payback period of approximately eight months. However, leaders are cautioned against an "instability tax," as increased delivery speed may overwhelm manual review gates and elevate failure rates if not balanced with automated testing and continuous integration. Looking ahead, the research predicts compounding gains in years two and three, potentially reaching a 727% return as teams transition toward autonomous agentic workflows. Ultimately, the report emphasizes that AI’s true value lies in clearing systemic bottlenecks and unlocking latent human creativity, rather than pursuing simple headcount reduction.

Compliance Without Chaos In Modern Delivery

The article "Compliance Without Chaos In Modern Delivery" emphasizes transforming compliance from a disruptive, quarterly hurdle into a seamless, integrated component of the software delivery lifecycle. Rather than treating audits as high-stakes oral exams, the author advocates for building automated controls directly into existing engineering workflows. This "Policy as Code" approach effectively eliminates the ambiguity of "folklore" policies by enforcing rules through CI/CD gates, such as mandatory pull request reviews, automated testing, and artifact traceability. To maintain a state of continuous readiness, teams should implement automated evidence collection, ensuring that audit trails for changes, access, and security checks are generated as a natural byproduct of daily development work. The piece also highlights the importance of robust access management, favoring short-lived privileges and group-based permissions over static, high-risk credentials. Furthermore, continuous monitoring is described as essential for identifying silent failures in critical areas like encryption, log retention, and vulnerability status before they escalate into major incidents. By maintaining an updated evidence map and an "audit-ready pack" year-round, organizations can achieve a "boring" compliance posture. Ultimately, the goal is to shift from reactive manual efforts to a disciplined, automated machine that consistently proves security and regulatory adherence without sacrificing delivery speed or engineering focus.

Ask a Data Ethicist: What Are the Legal and Ethical Issues in Summarizing Text with an AI Tool?

The use of AI tools for text summarization introduces significant legal and ethical challenges that organizations must navigate carefully. Legally, the primary concern revolves around copyright infringement, as these tools are often trained on large datasets containing proprietary data without explicit consent, potentially leading to complex intellectual property disputes. Furthermore, privacy risks emerge when users input sensitive or personally identifiable information into external AI systems, potentially violating strict regulations like the GDPR or CCPA. From an ethical standpoint, the article highlights the danger of algorithmic bias, where AI might inadvertently emphasize or distort certain viewpoints based on inherent flaws in its training data. Hallucinations represent another critical ethical risk, as AI can generate plausible-looking but factually incorrect summaries, leading to the spread of misinformation. To mitigate these systemic issues, the author emphasizes the importance of implementing robust data governance frameworks and maintaining a consistent "human-in-the-loop" approach. This ensures that summaries are rigorously reviewed for accuracy and fairness before being utilized in professional decision-making processes. Transparency regarding the use of automated tools is also paramount to maintaining public and stakeholder trust. Ultimately, while AI summarization offers immense efficiency, its deployment requires a balanced strategy that prioritizes legal compliance and ethical integrity.

UK chief executives make AI priority but delay plans

A recent report from Dataiku, based on a Harris Poll survey of nine hundred global chief executives, indicates that UK leaders are positioning artificial intelligence as a paramount corporate priority while simultaneously exercising significant caution in its implementation. The study, which focused on organizations with annual revenues exceeding five hundred million dollars, revealed that eighty-one percent of UK CEOs rank AI strategy as a top or high priority, a figure that notably surpasses the global average of seventy-three percent. However, this high level of ambition is tempered by a growing fear of financial waste; seventy-seven percent of British respondents expressed greater concern about over-investing in the technology than under-investing, compared to sixty-five percent of their international peers. This fiscal wariness has led to tangible delays in project rollouts across the country. Specifically, fifty-one percent of UK executives admitted to postponing AI initiatives due to regulatory uncertainty, a sharp increase from twenty-six percent just one year prior. As questions regarding return on investment and governance persist, a widening gap has emerged between boardroom aspirations and practical execution. UK leaders are increasingly weighing their expenditures more carefully, shifting from rapid adoption toward a more calculated approach that prioritizes oversight and navigates the evolving legislative landscape to avoid costly mistakes.

Open Innovation and AI will define the next generation of manufacturing: Annika Olme, CTO, SKF

Annika Olme, the CTO of SKF, emphasizes that the future of manufacturing lies at the intersection of open innovation and advanced technology like Artificial Intelligence. She highlights how SKF is transitioning from being a traditional bearing manufacturer to a digital-first, data-driven leader. By fostering a culture of deep collaboration with startups, academia, and technology partners, the company accelerates the development of smart solutions that optimize industrial processes globally. AI and machine learning are central to this evolution, particularly in predictive maintenance, which allows customers to anticipate failures and reduce downtime significantly. Olme also underscores the critical role of sustainability, noting that digital transformation is intrinsically linked to circularity and energy efficiency. By leveraging sensors and real-time data analysis, SKF helps various industries minimize waste and lower their carbon footprint. The “Smart Factory” vision involves integrating these technologies into every stage of the product lifecycle, from design to end-of-use recycling. Ultimately, the goal is to create a seamless synergy between human ingenuity and machine intelligence, ensuring that manufacturing remains both competitive and environmentally responsible. This holistic approach to innovation not only boosts productivity but also redefines how global industrial leaders address modern challenges like climate change, resource scarcity, and supply chain volatility.

Daily Tech Digest - December 22, 2025

Quote for the day:

"Life isn’t about getting and having, it’s about giving and being." -- Kevin Kruse

Browser agents don’t always respect your privacy choices

A key issue is the location of the language model. Seven out of eight agents use off device models. This means detailed information about the user’s browser state and each visited webpage is sent to servers controlled by the service provider. When the model runs on remote servers, users lose control over how search queries and sensitive webpage content are processed and stored. While some providers describe limits on data use, users must rely on service provider policies. Browser version age is another factor. Browsers release frequent updates to patch security flaws. One agent was found running a browser that was 16 major versions out of date at the time of testing. ... Agents also showed weaknesses in TLS certificate handling. Two agents did not show warnings for revoked certificates. One agent also failed to warn users about expired and self signed certificates. Trusting connections with invalid certificates leaves agents open to machine-in-the-middle attacks that allow attackers to read or alter submitted information. ... Agent decision logic sometimes favored task completion over protecting user information, leading to personal data disclosure. This resulted in six vulnerabilities. Researchers supplied agents with a fictitious identity and observed whether that information was shared with websites under different conditions. Three agents disclosed personal information during passive tests, where the requested data was not required to complete the task.

What CISOs should know about the SolarWinds lawsuit dismissal

For many CISOs, the dismissal landed not as an abstract legal development, but as something deeply personal. ... Even though the SolarWinds case sparked a deeper recognition that cybersecurity responsibility should be a shared responsibility across enterprises, shifting policy priorities and future administrations could once again put CISOs in the SEC’s crosshairs, they warn. ... The judge’s reasoning reassured many security leaders, but it also exposed a more profound discomfort about how accountability is assigned inside modern organizations. “The area that a lot of us were really uncomfortable about was the idea that an operational head of security could be personally responsible for what the company says about its cybersecurity investments,” Sullivan says. He adds, “Tim didn’t have the CISO title before the incident. And so there was just a lot there that made security people very concerned. Why is this operational person on the hook for representations?” But even if he had had the CISO role before the incident, the argument still holds, according to Sullivan. “Historically, the person who had that title wasn’t a quote-unquote ‘chief’ in the sense that they’re not in the little room of people who run the company,” Sullivan says. ... If the SolarWinds case clarified anything, it’s that relief is temporary and preparation is essential. CISOs have a window of opportunity to shore up their organizational and personal defenses in the event the political pendulum swings and makes CISOs litigation targets again.

Global uncertainty is reshaping cloud strategies in Europe

Europe has been debating digital sovereignty for years, but the issue has gained new urgency amid rising geopolitical tensions. “The political environment is changing very fast,” said Ollrom. A combination of trade disputes, sanctions that affect access to technology, and the possibility of tariffs on digital services has prompted many European organizations to reconsider their reliance on US hyperscaler clouds. ... What was once largely a public-sector concern now attracts growing interest across a wide range of private organizations as well. Accenture is currently working with around 50 large European organizations on digital-sovereignty-related projects, said Capo. This includes banks, telcos, and logistics companies alongside clients in government and defense. ... Another worry is the possibility that cloud services will be swept up in future trade disputes. If the EU imposes retaliatory tariffs on digital services, the cost of using hyperscaler cloud platforms could hike overnight, and organizations heavily dependent on them may find it hard to switch to a cheaper option. There’s also the prospect that organizations could lose access to cloud services if sanctions or export restrictions are imposed, leaving them temporarily or permanently locked out of systems they rely on. It’s a remote risk, said Dario Maisto, a senior analyst at Forrester, but a material one. “We are talking of a worst-case scenario where IT gets leveraged as a weapon,” he said.

What the AWS outage taught CIOs about preparedness

For many organizations, the event felt like a cyber incident even though it wasn’t, but it raised a difficult question for CIOs about how to prepare for a disruption that lives outside your infrastructure, yet carries the same operational and reputational consequences as a security breach. ... Beyond strong cloud architecture, “Preparedness is the real differentiator,” he says. “Even the best technology teams can’t compensate for gaps in scenario planning, coordination, and governance.” ... Within Deluxe, disaster recovery tests historically focused on applications the company controlled, while cyber tabletops focused on simulated intrusions. The AWS outage exposed the gap between those exercises and real-world conditions. Shifting its applications from AWS East to AWS West was swift, and the technology team considered the recovery a success. Yet it was far from business as usual, as developers still couldn’t access critical tools like GitHub or Jira. “We thought we’d recovered, but the day-to-day work couldn’t continue because the tools we depend on were down,” he says. ... In a well-architected hybrid cloud setup, he says resilience is more often a coordination problem than a spending problem, and distributing workloads across two cloud providers doesn’t guarantee better outcomes if the clouds rely on the same power grid, or experience the same regional failure event. ... Jayaprakasam is candid about the cultural challenge that comes with resilience work.

Winning the density war: The shift from RPPs to scalable busway infrastructure in next-gen facilities

“Four or five years ago, we were seeing sub-ten-kilowatt racks, and today we're being asked for between 100 and 150 kilowatts, which makes a whole magnitude of difference,” says Osian. “And this trend is going to continue to rise, meaning we have to mobilize for tomorrow’s power challenges, today.” Rising power demands also require higher available fault currents to safely handle larger, more dynamic surges in the circuit. Supporting equipment must be more resilient and reliable to maintain safe and efficient distribution. With change happening so quickly, adopting a long-term strategy is essential. This requires building critical infrastructure with adaptability and flexibility at its core. ... A modular approach offers another tactical advantage: speed. With a traditional RPP setup, getting power physically hooked up from A to B on a per-rack basis is time and resource-consuming, especially at first installation. By reducing complexity with a plug-and-play modular design slotted in directly over the racks, the busway delivers the swift reinforcements modern facilities need to stay ahead. ... “One of the advancements we've made in the last year is creating a way for users to add a circuit from outside the arc flash boundary. While the Starline busway is already rated for live insertion – meaning it’s safe out of the box – we’ve taken safety to the next level with a device called the Remote Plugin Actuator. It allows a user to add a circuit to the busway without engaging any of the electrical contacts directly.”

Building a data-driven, secure and future-ready manufacturing enterprise: Technology as a strategic backbone

A central pillar of Prince Pipes and Fittings’ digital strategy is data democratisation. The organisation has moved decisively away from static reports towards dynamic, self-service analytics. A centralised data platform for sales and supply chain allows business users to create their own dashboards without dependence on IT teams. Desai further states, “Sales teams, for instance, can access granular data on their smartphones while interacting with customers, instantly showcasing performance metrics and trends. This empowerment has not only improved responsiveness but has also enhanced user confidence and satisfaction. Across functions, data is now guiding actions rather than merely describing outcomes.” ... Technology transformation at Prince Pipes and Fittings has been accompanied by a conscious effort to drive cultural change. Leadership recognised early that democratising data would require a mindset shift across the organisation. Initial resistance was addressed through structured training programs conducted zone-wise and state-wise, helping users build familiarity and confidence with new platforms. ... Cyber security is treated as a business-critical priority at Prince Pipes and Fittings. The organisation has implemented a phase-wise, multi-layered cyber security framework spanning both IT and OT environments. A simple yet effective risk-classification approach i.e. green, yellow, and red, was used to identify gaps and prioritise actions. ... Equally important has been the focus on human awareness.

The Next Fraud Problem Isn’t in Finance. It’s in Hiring: The New Attack Surface

The uncomfortable truth is that the interview has become a transaction. And the “asset” being transferred is not a paycheck. It’s access: to systems, data, colleagues, customers, and internal credibility. ... Payment fraud works because the system is trying to be fast. The same is true in hiring. Speed is rewarded. Friction is avoided. And that creates a predictable failure mode: an attacker’s job is to make the process feel normal long enough to get to “approved.” In payments, fraudsters use stolen cards and compromised accounts. In hiring, they can use stolen faces, voices, credentials, and employment histories. The mechanics differ, but the objective is identical: get the system to say yes. That’s why the right question for leaders is not, “Can we spot a deepfake?” It’s, “What controls do we have before we grant access?” ... Many companies verify identity late, during onboarding, after decisions are emotionally and operationally “locked.” That’s the equivalent of shipping a product and hoping the card wasn’t stolen. Instead, introduce light identity proofing before final rounds or before any access-related steps. ... In payments, the critical moment is authorization. In hiring, it’s when you provision accounts, ship hardware, grant repository permissions, or provide access to customer or financial systems. That moment deserves a deliberate gate: confirm identity through a known-good channel, verify references without relying on contact info provided by the candidate, and run a final live verification step before credentials are issued.

Agent autonomy without guardrails is an SRE nightmare

Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI. ... When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system. ... Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.

Where Architects Sit in the Era of AI

In the emerging AI-augmented ecosystem, we can think of three modes of architect involvement: Architect in the loop, Architect on the loop, and Architect out of the loop. Each reflects a different level of engagement, oversight, and trust between an Architect and intelligent systems. ... What does it mean to be in the loop? In the Architect in the Loop (AITL) model, the architect and the AI system work side by side. AI provides options, generates designs, or analyzes trade-offs, but humans remain the decision-makers. Every output is reviewed, contextualized, and approved by an architect who understands both the technical and organizational context. This is where the Architect is sat in the middle of AI interactions ... What does it mean to be on the loop? As AI matures, parts of architectural decision-making can be safely delegated. In the Architect on the Loop (AOTL) model, the AI operates autonomously within predefined boundaries, while the architect supervises, reviews, and intervenes when necessary. This is where the architect is firmly embedded into the development workflow using AI to augment and enhance their own natural abilities. ... What does it mean to be out of the loop? In the AOOTL model, we see a world where the architect is no longer required in the traditional fashion. The architectural work of domain understanding, context providing, and design thinking is simply all done by AI, with the outputs of AI being used by managers, developers, and others to build the right systems at the right time.

Cloud Migration of Microservices: Strategy, Risks, and Best Practices

The migration of microservices to the cloud is a crucial step in the digital transformation process, requiring a strategic approach to ensure success. The success of the migration depends on carefully selecting the appropriate strategy based on the current architecture's maturity, technical debt, business objectives, and cloud infrastructure capabilities. ... The simplest strategy for migrating to the cloud is Rehost. This involves moving applications as is to virtual machines in the cloud. According to research, around 40% of organizations begin their migration with Rehost, as it allows for a quick transition to the cloud with minimal costs. However, this approach often does not provide significant performance or cost benefits, as it does not fully utilize cloud capabilities. Replatform is the next level of complexity, where applications are partially adapted. For example, databases may be migrated to cloud services like Amazon RDS or Azure SQL, file storage may be replaced, and containerization may be introduced. Replatform is used in around 22% of cases where there is a need to strike a balance between speed and the depth of changes. A more time-consuming but strategically beneficial approach is Refactoring (or Rearchitecting), in which the application undergoes a significant redesign: microservices are introduced, Kubernetes, Kafka, and cloud functions (such as Lambda and Azure Functions) are utilized, as well as a service bus.

Daily Tech Digest - December 16, 2025

Quote for the day:

"Worry less, smile more. Don't regret, just learn and grow." -- @Pilotspeaker

The battle for agent connectivity: Can MCP survive the enterprise?

"MCP is the UI for agents. The future of asking to book an and have a pizza available when you arrive at the hotel only works if we have the connectivity," said Dag Calafell III, director of Technology Innovation at MCA Connect, an IT consultancy for manufacturers. But while seamless connectivity might be the Holy Grail for consumer apps, critics argue that it is irrelevant -- or even dangerous -- for the enterprise. ... Notably, MCP has significant backing from prominent companies, including Google, , Microsoft and its creator, Anthropic. Indeed, Calafell argued that while there are competitors out there, "MCP is winning" precisely because it has seen significant adoption by large software providers. Still, MCP clearly has significant issues -- mostly because it's in its infancy. MCP's rapidly evolving specification, uneven tooling, unclear security and governance controls, and lack of standardized memory, debugging, and orchestration make it better for experimentation than reliable enterprise use today. ... "There is room to innovate with a security-first 'MCP-like' standard that is resource aware, with trusted catalogues, privileges, scopes, etc. These would either be built on top of MCP, a sort of MCP v2, or introduced as part of a new protocol," said , co-founder and CTO at . And, of course, there remains an evolving trend that the AI industry will take an entirely different direction.

Digital Twin in Railways: A Practical Solution to Managing Complex Rail Systems

In the context of railways, are being deployed to improve asset lifecycle management, predictive maintenance, and infrastructure planning. By integrating inputs from IoT devices and advanced analytics platforms, these models help engineers monitor structural health, detect anomalies, and plan maintenance before failures occur. ... As the scale and complexity of rail networks continue to grow, the use of digital twins offers a unified, comprehensive view of interconnected assets, which empowers rail operators with faster decision-making and better coordination across departments. This technology is gradually becoming a core component of smart railway ecosystems. ... The architecture of a digital twin in railway systems is built upon the integration of multiple digital technologies, including Building Information Modelling (BIM), the Internet of Things (IoT), Geographic Information Systems (GIS), and data analytics platforms. Together, these technologies create a unified framework that connects the physical and digital environments of railway infrastructure and operations. ... The integration of operational data, including train movements, energy consumption, and passenger flows, allows operators to simulate different scenarios and optimise timetables, headways, and energy use. In dense networks such as urban metro systems, this contributes to improved punctuality and efficient energy utilisation.

Stop mimicking and start anchoring

It’s a fundamental truth that most are ignoring in their rush to emulate Big Tech playbooks. The result is a systematic misallocation of resources based on a fundamental misunderstanding of how value creation works across industries. ... the strategic value of IT should be measured by how effectively it addresses industry-specific value creation. Different industries have vastly different technology intensity and value-creation dynamics. In our view, CIOs must therefore resist trend-driven decisions and view IT investment through their industry’s value-creation to sharpen competitive edge. To understand why IT strategies diverge across industries shaped by sectoral realities and maturity differences, we need to examine how business models shape the role of technology. ... funding business outcomes rather than chasing technology fads is easier said than done. It’s difficult to unravel the maze created by the relentless march of technological hype versus the grounded reality of business. But the role of IT is not universal; its business relevance changes from one industry to another. ... Long-term value from emerging technologies comes from grounded application, not blind adoption. In the race to transform, the wisest CIOs will be those who understand that the best technology decisions are often the ones that honour, rather than abandon the fundamental nature of their business. The future belongs not to those who adopt the most tech, but to those who adopt the right tech for the right reasons.

Build vs buy is dead — AI just killed it

Ssomething fundamental has changed: AI has made building accessible to everyone. What used to take weeks now takes hours, and what used to require fluency in a programming language now requires fluency in plain English.When the cost and complexity of building collapse this dramatically, the old framework goes down with them. It’s not build versus buy anymore. It’s something stranger that we haven't quite found the right words for. ... And it's not some future state. This is already happening. Right now, somewhere, a customer rep is using AI to fix a product issue they spotted minutes ago. Somewhere else, a finance team is prototyping their own analytical tools because they've realized they can iterate faster than they can write up requirements for engineering. Somewhere, a team is realizing that the boundary between technical and non-technical was always more cultural than fundamental. The companies that embrace this shift will move faster and spend smarter. They’ll know their operations more deeply than any vendor ever could. They'll make fewer expensive mistakes, and buy better tools because they actually understand what makes tools good. The companies that stick to the old playbook will keep sitting through vendor pitches, nodding along at budget-friendly proposals. They’ll debate timelines, and keep mistaking professional decks for actual solutions. Until someone on their own team pops open their laptop, says, “I built a version of this last night. Want to check it out?,”

Quantum Tech Hits Its “Transistor Moment,” Scientists Say

“This transformative moment in quantum technology is reminiscent of the transistor’s earliest days,” said lead author David Awschalom, the Liew Family Professor of molecular engineering and physics at the University of Chicago, and director of the Chicago Quantum Exchange and the Chicago Quantum Institute. “The foundational physics concepts are established, functional systems exist, and now we must nurture the partnerships and coordinated efforts necessary to achieve the technology’s full, utility-scale potential. How will we meet the challenges of scaling and modular quantum architectures?” ... Although advanced prototypes have demonstrated system operation and public cloud access, their raw performance remains early in development. For example, many meaningful applications, including large-scale quantum chemistry simulations, could require millions of physical qubits with error performance far beyond what is technologically viable today. ... “While semiconductor chips in the 1970s were TLR-9 for that time, they could do very little compared with today’s advanced integrated circuits,” he said. “Similarly, a high TRL for quantum technologies today does not indicate that the end goal has been achieved, nor does it indicate that the science is done and only engineering remains. Rather, it reflects a significant, yet relatively modest, system-level demonstration has been achieved—one that still must be substantially improved and scaled to realize the full promise.”

Before you build your first enterprise AI app

Model weights are becoming undifferentiated heavy lifting, the boring infrastructure that everyone needs but no one wants to manage. Whether you use Anthropic, OpenAI, or an open weights model like , you are getting a level of intelligence that is good enough for 90% of enterprise tasks. The differences are marginal for a first version. The “best” model is usually just the one you can actually access securely and reliably. ... We used to obsess over the massive cost of training models. But for the enterprise, that is largely irrelevant. AI is all about inference now, or the application of knowledge to power applications. In other words, AI will become truly useful within the enterprise as we apply models to governed enterprise data. The best place to build up your AI muscle isn’t with some moonshot agentic system. It’s a simple retrieval-augmented generation (RAG) pipeline. What does this mean in practice? Find a corpus of boring, messy documents, such as HR policies, technical documentation, or customer support logs, and build a system that allows a user to ask a question and get an answer based only on that data. This forces you to solve the hard problems that actually build a moat for your company. ... When you build your first application, design it to keep the human in the loop. Don’t try to automate the entire process. Use the AI to generate the first draft of a report or the first pass at a SQL query, and then force a human to review and execute it.

Cloudflare reveals AI surge & Internet ‘bot wars’ in 2025

Cloudflare reported that use of AI models and AI crawling activity increased sharply. It said crawling for model training accounted for the majority of AI crawler traffic during the year. Training-related crawlers generated traffic that reached as much as seven to eight times the level of retrieval-augmented generation and search crawlers at peak. Traffic from training crawlers was also as much as 25 times higher than AI crawlers tied to direct user actions. The company said was the most widely used on its network. It was used by more than three times as many accounts as the next most popular models from providers such as OpenAI and . Cloudflare added that Google’s crawling bot remained the dominant automated actor on the Internet. It said ’s crawl volume exceeded that of all other leading AI bots by a wide margin and was the largest single source of automated traffic it observed. ... Cloudflare reported a notable shift in the sectors that face the highest volume of . Civil society and non-profit organisations became the most attacked group for the first time. The company linked this trend to the sensitivity and financial value of the data held by such organisations. This includes personal information about donors, volunteers and beneficiaries. Cloudflare’s data also showed changes in the causes of major Internet outages.

Who Owns AI Risk? Why Governance Begins with Architecture

But as AI systems grow more complex, so do their risks. Bias, opacity, data misuse, model drift, or even overreliance on AI outputs can all cause serious business, ethical, and reputational damage. This raises an uncomfortable question: who actually owns the risk of AI? ... AI doesn’t live in isolation. It consumes enterprise data, depends on cloud services, interacts with APIs, and influences real business processes.Governance, therefore, can’t rely on policies alone, it must be designed, structured, and embedded into the architecture itself. For instance, companies like Microsoft and Google have embedded AI governance directly into their architectural blueprints creating internal AI Ethics and Risk Committees that review model design before deployment. This proactive structure ensures compliance and builds trust long before a model reaches production. ... In other words, AI Governance is not a department, it’s an ecosystem of shared responsibility.Enterprise Architects connect the dots, Business Owners set the direction, Data Scientists implement, and Governance Boards oversee. But the real maturity comes when everyone in the organization, from the C-suite to the operational level, understands that AI is a shared asset and a shared risk. ... Modern enterprise architecture is no longer only about connecting systems. It’s about connecting responsibility. The moment artificial intelligence becomes part of the business fabric, architecture must evolve to ensure that governance isn’t something external or reactive, it’s embedded in the very design of every AI-enabled solution.

The 5 power skills every CISO needs to master in the AI era

According to the , nearly 40% of core job skills will change by 2030, driven primarily by AI, data and automation. For security professionals, this means that expertise in network defense, forensics and patching — while still essential — is no longer enough to create value. The real impact comes from how we interpret, communicate and apply what AI enables. ... The biggest myth in security is that technical mastery equals longevity. In truth, the more we automate, the more we value human differentiation. Success in the next decade won’t depend on how much code you can write — but on how effectively you can connect, translate and lead across systems and silos. When I look at the most resilient organizations today, they share one trait: They see cybersecurity not as a control function, but as a strategic enabler. And their leaders? They’re fluent in both algorithms and empathy. The future of cybersecurity belongs to those who build bridges — not just firewalls. Cybersecurity is no longer a war between humans and machines — it’s a collaboration between both. The organizations that succeed will be the ones that combine AI’s precision with human empathy and creative foresight. As AI handles scale, leaders must handle meaning. And that’s the true essence of power skills. The future of cybersecurity belongs to those who can blend AI’s precision with human expertise — and lead with both.

Manufacturing is becoming a test bed for ransomware shifts

“Manufacturing depends on interconnected systems where even brief downtime can stop production and ripple across supply chains,” said , Director of Threat Research, . “Attackers exploit this pressure: despite encryption rates falling to 40%, the median ransom paid still reached $1 million. While half of manufacturers stopped attacks before encryption, recovery costs average $1.3 million and leadership stress remains high. Layered defenses, continuous visibility, and well-rehearsed response plans are essential to reduce both operational impact and financial risk,” Rose continued. Teams were able to stop attacks before encryption in a larger share of cases, which likely contributed to the decline. Early detection helped reduce disruption, although strong detection did not guarantee a smooth recovery. ... IT and security leaders in manufacturing see progress in some areas but ongoing gaps in others. Detection appears to be improving. Recovery is becoming steadier. Payment rates are declining. But operational weaknesses persist. Skills shortages, aging protections, and limited visibility into vulnerabilities continue to contribute to compromises. These factors shape outcomes as much as attacker capability. The findings also show a need for stronger internal support. Security teams are absorbing organizational and emotional strain that can affect long term performance. Manufacturing operations depend on stable systems, and teams cannot maintain stability without workloads they can manage.

Daily Tech Digest - August 10, 2025

Quote for the day:

"Don't worry about being successful but work toward being significant and the success will naturally follow." -- Oprah Winfrey

The Scrum Master: A True Leader Who Serves

Many people online claim that “Agile is a mindset”, and that the mindset is more important than the framework. But let us be honest, the term “agile mindset” is very abstract. How do we know someone truly has it? We cannot open their brain to check. Mindset manifests in different behaviour depending on culture and context. In one place, “commitment” might mean fixed scope and fixed time. In another, it might mean working long hours. In yet another, it could mean delivering excellence within reasonable hours. Because of this complexity, simply saying “agile is a mindset” is not enough. What works better is modelling the behaviour. When people consistently observe the Scrum Master demonstrating agility, those behaviours can become habits. ... Some Scrum Masters and agile coaches believe their job is to coach exclusively, asking questions without ever offering answers. While coaching is valuable, relying on it alone can be harmful if it is not relevant or contextual. Relevance is key to improving team effectiveness. At times, the Scrum Master needs to get their hands dirty. If a team has struggled with manual regression testing for twenty Sprints, do not just tell them to adopt Test-Driven Development (TDD). Show them. ... To be a true leader, the Scrum Master must be humble and authentic. You cannot fake true leadership. It requires internal transformation, a shift in character. As the saying goes, “Character is who we are when no one is watching.”

Vendors Align IAM, IGA and PAM for Identity Convergence

The historic separation of IGA, PAM and IAM created inefficiencies and security blind spots, and attackers exploited inconsistencies in policy enforcement across layers, said Gil Rapaport, chief solutions officer at CyberArk. By combining governance, access and privilege in a single platform, the company could close the gaps between policy enforcement and detection, Rapaport said. "We noticed those siloed markets creating inefficiency in really protecting those identities, because you need to manage different type of policies for governance of those identities and for securing the identities and for the authentication of those identities, and so on," Rapaport told ISMG. "The cracks between those silos - this is exactly where the new attack factors started to develop." ... Enterprise customers that rely on different tools for IGA, PAM, IAM, cloud entitlements and data governance are increasingly frustrated because integrating those tools is time-consuming and error-prone, Mudra said. Converged platforms reduce integration overhead and allow vendors to build tools that communicate natively and share risk signals, he said. "If you have these tools in silos, yes, they can all do different things, but you have to integrate them after the fact versus a converged platform comes with out-of-the-box integration," Mudra said. "So, these different tools can share context and signals out of the box."

The Importance of Technology Due Diligence in Mergers and Acquisitions

The primary reason for conducting technology due diligence is to uncover any potential risks that could derail the deal or disrupt operations post-acquisition. This includes identifying outdated software, unresolved security vulnerabilities, and the potential for data breaches. By spotting these risks early, you can make informed decisions and create risk mitigation strategies to protect your company. ... A key part of technology due diligence is making sure that the target company’s technology assets align with your business’s strategic goals. Whether it’s cloud infrastructure, software solutions, or hardware, the technology should complement your existing operations and provide a foundation for long-term growth. Misalignment in technology can lead to inefficiencies and costly reworks. ... Rank the identified risks based on their potential impact on your business and the likelihood of their occurrence. This will help prioritize mitigation efforts, so that you’re addressing the most critical vulnerabilities first. Consider both short-term risks, like pending software patches, and long-term issues, such as outdated technology or a lack of scalability. ... Review existing vendor contracts and third-party service provider agreements, looking for any liabilities or compliance risks that may emerge post-acquisition—especially those related to data access, privacy regulations, or long-term commitments. It’s also important to assess the cybersecurity posture of vendors and their ability to support integration.

From terabytes to insights: Real-world AI obervability architecture

The challenge is not only the data volume, but the data fragmentation. According to New Relic’s 2023 Observability Forecast Report, 50% of organizations report siloed telemetry data, with only 33% achieving a unified view across metrics, logs and traces. Logs tell one part of the story, metrics another, traces yet another. Without a consistent thread of context, engineers are forced into manual correlation, relying on intuition, tribal knowledge and tedious detective work during incidents. ... In the first layer, we develop the contextual telemetry data by embedding standardized metadata in the telemetry signals, such as distributed traces, logs and metrics. Then, in the second layer, enriched data is fed into the MCP server to index, add structure and provide client access to context-enriched data using APIs. Finally, the AI-driven analysis engine utilizes the structured and enriched telemetry data for anomaly detection, correlation and root-cause analysis to troubleshoot application issues. This layered design ensures that AI and engineering teams receive context-driven, actionable insights from telemetry data. ... The amalgamation of structured data pipelines and AI holds enormous promise for observability. We can transform vast telemetry data into actionable insights by leveraging structured protocols such as MCP and AI-driven analyses, resulting in proactive rather than reactive systems.

MCP explained: The AI gamechanger

Instead of relying on scattered prompts, developers can now define and deliver context dynamically, making integrations faster, more accurate, and easier to maintain. By decoupling context from prompts and managing it like any other component, developers can, in effect, build their own personal, multi-layered prompt interface. This transforms AI from a black box into an integrated part of your tech stack. ... MCP is important because it extends this principle to AI by treating context as a modular, API-driven component that can be integrated wherever needed. Similar to microservices or headless frontends, this approach allows AI functionality to be composed and embedded flexibly across various layers of the tech stack without creating tight dependencies. The result is greater flexibility, enhanced reusability, faster iteration in distributed systems and true scalability. ... As with any exciting disruption, the opportunity offered by MCP comes with its own set of challenges. Chief among them is poorly defined context. One of the most common mistakes is hardcoding static values — instead, context should be dynamic and reflect real-time system states. Overloading the model with too much, too little or irrelevant data is another pitfall, often leading to degraded performance and unpredictable outputs.

AI is fueling a power surge - it could also reinvent the grid

Data centers themselves are beginning to evolve as well. Some forward-looking facilities are now being designed with built-in flexibility to contribute back to the grid or operate independently during times of peak stress. These new models, combined with improved efficiency standards and smarter site selection strategies, have the potential to ease some of the pressure being placed on energy systems. Equally important is the role of cross-sector collaboration. As the line between tech and infrastructure continues to blur, it’s critical that policymakers, engineers, utilities, and technology providers work together to shape the standards and policies that will govern this transition. That means not only building new systems, but also rethinking regulatory frameworks and investment strategies to prioritize resiliency, equity, and sustainability. Just as important as technological progress is public understanding. Educating communities about how AI interacts with infrastructure can help build the support needed to scale promising innovations. Transparency around how energy is generated, distributed, and consumed—and how AI fits into that equation—will be crucial to building trust and encouraging participation. ... To be clear, AI is not a silver bullet. It won’t replace the need for new investment or hard policy choices. But it can make our systems smarter, more adaptive, and ultimately more sustainable.

AI vs Technical Debt: Is This A Race to the Bottom?

Critically, AI-generated code can carry security liabilities. One alarming study analyzed code suggested by GitHub Copilot across common security scenarios – the result: roughly 40% of Copilot’s suggestions had vulnerabilities. These included classic mistakes like buffer overflows and SQL injection holes. Why so high? The AI was trained on tons of public code – including insecure code – so it can regurgitate bad practices (like using outdated encryption or ignoring input sanitization) just as easily as good ones. If you blindly accept such output, you’re effectively inviting known bugs into your codebase. It doesn’t help that AI is notoriously bad at certain logical tasks (for example, it struggles with complex math or subtle state logic, so it might write code that looks legit but is wrong in edge cases. ... In many cases, devs aren’t reviewing AI-written code as rigorously as their own, and a common refrain when something breaks is, “It is not my code,” implying they feel less responsible since the AI wrote it. That attitude itself is dangerous, if nobody feels accountable for the AI’s code, it slips through code reviews or testing more easily, leading to more bad deployments. The open-source world is also grappling with an influx of AI-generated “contributions” that maintainers describe as low-quality or even spam. Imagine running an open-source project and suddenly getting dozens of auto-generated pull requests that technically add a feature or fix but are riddled with style issues or bugs.

The Future of Manufacturing: Digital Twin in Action

Process digital twins are often confused with traditional simulation tools, but there is an important distinction. Simulations are typically offline models used to test “what-if” scenarios, verify system behaviour, and optimise processes without impacting live operations. These models are predefined and rely on human input to set parameters and ask the right questions. A digital twin, on the other hand, comes to life when connected to real-time operational data. It reflects current system states, responds to live inputs, and evolves continuously as conditions change. This distinction between static simulation and dynamic digital twin is widely recognised across the industrial sector. While simulation still plays a valuable role in system design and planning, the true power of the digital twin lies in its ability to mirror, interpret, and influence operational performance in real time. ... When AI is added, the digital twin evolves into a learning system. AI algorithms can process vast datasets - far beyond what a human operator can manage - and detect early warning signs of failure. For example, if a transformer begins to exhibit subtle thermal or harmonic irregularities, an AI-enhanced digital twin doesn’t just flag it. It assesses the likelihood of failure, evaluates the potential downstream impact, and proposes mitigation strategies, such as rerouting power or triggering maintenance workflows.

Bridging the Gap: How Hybrid Cloud Is Redefining the Role of the Data Center

Today’s hybrid models involve more than merging public clouds with private data centers. They also involve specialized data center solutions like colocation, edge facilities and bare-metal-as-a-service (BMaaS) offerings. That’s the short version of how hybrid cloud and its relationship to data centers are evolving. ... Fast forward to the present, and the goals surrounding hybrid cloud strategies often look quite different. When businesses choose a hybrid cloud approach today, it’s typically not because of legacy workloads or sunk costs. It’s because they see hybrid architectures as the key to unlocking new opportunities ... The proliferation of edge data centers has also enabled simpler, better-performing and more cost-effective hybrid clouds. The more locations businesses have to choose from when deciding where to place private infrastructure and workloads, the more opportunity they have to optimize performance relative to cost. ... Today’s data centers are no longer just a place to host whatever you can’t run on-prem or in a public cloud. They have evolved into solutions that offer specialized services and capabilities that are critical for building high-performing, cost-effective hybrid clouds – but that aren’t available from public cloud providers, and that would be very costly and complicated for businesses to implement on their own.

AI Agents: Managing Risks In End-To-End Workflow Automation

As CIOs map out their AI strategies, it’s becoming clear that agents will change how they manage their organization’s IT environment and how they deliver services to the rest of the business. With the ability of agents to automate a broad swath of end-to-end business processes—learning and changing as they go—CIOs will have to oversee significant shifts in software development, IT operating models, staffing, and IT governance. ... Human-based checks and balances are vital for validating agent-based outputs and recommendations and, if needed, manually change course should unintended consequences—including hallucinations or other errors—arise. “Agents being wrong is not the same thing as humans being wrong,” says Elliott. “Agents can be really wrong in ways that would get a human fired if they made the same mistake. We need safeguards so that if an agent calls the wrong API, it’s obvious to the person overseeing that task that the response or outcome is unreasonable or doesn’t make sense.” These orchestration and observability layers will be increasingly important as agents are implemented across the business. “As different parts of the organization [automate] manual processes, you can quickly end up with a patchwork-quilt architecture that becomes almost impossible to upgrade or rethink,” says Elliott.

Daily Tech Digest - July 29, 2025

Quote for the day:

"Great leaders do not desire to lead but to serve." -- Myles Munroe

AI Skills Are in High Demand, But AI Education Is Not Keeping Up

There’s already a big gap between how many AI workers are needed and how many are available, and it’s only getting worse. The report says the U.S. was short more than 340,000 AI and machine learning workers in 2023. That number could grow to nearly 700,000 by 2027 if nothing changes. Faced with limited options in traditional higher education, most learners are taking matters into their own hands. According to the report, “of these 8.66 million people learning AI, 32.8% are doing so via a structured and supervised learning program, the rest are doing so in an independent manner.” Even within structured programs, very few involve colleges or universities. As the report notes, “only 0.2% are learning AI via a credit-bearing program from a higher education institution,” while “the other 99.8% are learning these skills from alternative education providers.” That includes everything from online platforms to employer-led training — programs built for speed, flexibility, and real-world use, rather than degrees. College programs in AI are growing, but they’re still not reaching enough people. Between 2018 and 2023, enrollment in AI and machine learning programs at U.S. colleges went up nearly 45% each year. Even with that growth, these programs serve only a small slice of learners — most people are still turning to other options.

Why chaos engineering is becoming essential for enterprise resilience

Enterprises should treat chaos engineering as a routine practice, just like sports teams before every game. These groups would never participate in matches without understanding their opponent or ensuring they are in the best possible position to win. They train under pressure, run through potential scenarios, and test their plays to identify the weaknesses of their opponents. This same mindset applies to enterprise engineering teams preparing for potential chaos in their environments. By purposely simulating disruptions like server outages, latency, or dropped connections, or by identifying bugs and poor code, enterprises can position themselves to perform at their best when these scenarios occur in real life. They can adopt proactive approaches to detecting vulnerabilities, instituting recovery strategies, building trust in systems and, in the end, improving their overall resilience. ... Additionally, chaos engineering can help improve scalability within the organisation. Enterprises are constantly seeking ways to grow and enhance their apps or platforms so that more and more end-users can see the benefits. By doing this, they can remain competitive and generate more revenue. Yet, if there are any cracks within the facets or systems that power their apps or platforms, it can be extremely difficult to scale and deliver value to both customers and the organisation.

Fractional CXOs: A New Model for a C-Everything World

Fractional leadership isn’t a new idea—it’s long been part of the advisory board and consulting space. But what’s changed is its mainstream adoption. Companies are now slotting in fractional leaders not just for interim coverage or crisis management, but as a deliberate strategy for agility and cost-efficiency. It’s not just companies benefiting either. Many high-performing professionals are choosing the fractional path because it gives them freedom, variety, and a more fulfilling way to leverage their skills without being tied down to one company or role. For them, it’s not just about fractional time—it’s about full-spectrum opportunity. ... Whether you’re a company executive exploring options or a leader considering a lifestyle pivot, here are the biggest advantages of fractional CxOs:Strategic Agility: Need someone to lead a transformation for 6–12 months? Need guidance scaling your data team? A fractional CxO lets you dial in the right leadership at the right time. Cost Containment: You pay for what you need, when you need it. No long-term employment contracts, no full comp packages, no redundancy risk. Experience Density: Most fractional CxOs have deep domain expertise and have led across multiple industries. That cross-pollination of experience can bring unique insights and fast-track solutions.

Cyberattacks reshape modern conflict & highlight resilience needs

Governments worldwide are responding to the changing threat landscape. The United States, European Union, and NATO have increased spending on cyber defence and digital threat-response measures. The UK's National Cyber Force has broadened its recruitment initiatives, while the European Union has introduced new cyber resilience strategies. Even countries with neutral status, such as Switzerland, have begun investing more heavily in cyber intelligence. ... Critical infrastructure encompasses power grids, water systems, and transport networks. These environments often use operational technology (OT) networks that are separated from the internet but still have vulnerabilities. Attackers typically exploit mechanisms such as phishing, infected external drives, or unsecured remote access points to gain entry. In 2024, a group linked to Iran, called CyberAv3ngers, breached several US water utilities by targeting internet-connected control systems, raising risks of water contamination. ... Organisations are advised against bespoke security models, with tried and tested frameworks such as NIST CSF, OWASP SAMM, and ISO standards cited as effective guides for structuring improvement. The statement continues, "Like any quality control system it is all about analysis of the situation and iterative improvements. Things evolve slowly until they happen all at once."

The trials of HR manufacturing: AI in blue-collar rebellion

The challenge of automation isn't just technological, it’s deeply human. How do you convince someone who has operated a ride in your park for almost two decades, who knows every sound, every turn, every lever by heart, that the new sleek control panel is an upgrade and not a replacement? That the machine learning model isn’t taking their job; it’s opening doors to something better? For many workers, the introduction of automation doesn’t feel like innovation but like erasure. A line shuts down. A machine takes over. A skill that took them years to master becomes irrelevant overnight. In this reality, HR’s role extends far beyond workflow design; it now must navigate fear, build trust, and lead people through change with empathy and clarity. Upskilling entails more than just access to platforms that educate you. It’s about building trust, ensuring relevance, and respecting time. Workers aren’t just asking how to learn, but why. Workers want clarity on their future career paths. They’re asking, “Where is this ride taking me?” As Joseph Fernandes, SVP of HR for South Asia at Mastercard, states, change management should “emphasize how AI can augment employee capabilities rather than replace them.” Additionally, HR must address the why of training, not just the how. Workers don’t want training videos; rather, they want to know what the next five years of their job look like.

What Do DevOps Engineers Think of the Current State of DevOps

The toolchain is consolidating. CI/CD, monitoring, compliance, security and cloud provisioning tools are increasingly bundled or bridged in platform layers. DevOps.com’s coverage tracks this trend: It’s no longer about separate pipelines, it’s about unified DevOps platforms. CloudBees Unify is a prime example: Launched in mid‑2025, it unifies governance across toolchains without forcing migration — an AI‑powered operating layer over existing tools. ... DevOps education and certification remain fragmented. Traditional certs — Kubernetes (CKA, CKAD), AWS/Azure/GCP and DevOps Foundation — remain staples. But DevOps engineers express frustration: Formal learning often lags behind real‑world tooling, AI integration, or platform engineering practices. Many engineers now augment certs with hands‑on labs, bootcamps and informal community learning. Organizations are piloting internal platform engineer training programs to bridge skills gaps. Still, a mismatch persists between the modern tech stack and classroom syllabi. ... DevOps engineers today stand at a crossroads: Platform engineering and cloud tooling have matured into the ecosystem, AI is no longer experimentation but embedded flow. Job markets are shifting, but real demand remains strong — for creative, strategic and adaptable engineers who can shepherd tools, teams and AI together into scalable delivery platforms.

7 enterprise cloud strategy trends shaking up IT today

Vertical cloud platforms aren’t just generic cloud services — they’re tailored ecosystems that combine infrastructure, AI models, and data architectures specifically optimized for sectors such as healthcare, manufacturing, finance, and retail, says Chandrakanth Puligundla, a software engineer and data analyst at grocery store chain Albertsons. What makes this trend stand out is how quickly it bridges the gap between technical capabilities and real business outcomes, Puligundla says. ... Organizations must consider what workloads go where and how that distribution will affect enterprise performance, reduce unnecessary costs, and help keep workloads secure, says Tanuj Raja, senior vice president, hyperscaler and marketplace, North America, at IT distributor and solution aggregator TD SYNNEX. In many cases, needs are driving a move toward a hybrid cloud environment for more control, scalability, and flexibility, Raja says. ... We’re seeing enterprises moving past the assumption that everything belongs in the cloud, says Cache Merrill, founder of custom software development firm Zibtek. “Instead, they’re making deliberate decisions about workload placement based on actual business outcomes.” This transition represents maturity in the way enterprises think about making technology decisions, Merrill says. He notes that the initial cloud adoption phase was driven by a fear of being left behind.

Beyond the Rack: 6 Tips for Reducing Data Center Rental Costs

One of the simplest ways to reduce spending on data center rentals is to choose data centers located in regions where data center space costs the least. Data center rental costs, which are often measured in terms of dollars-per-kilowatt, can vary by a factor of ten or more between different parts of the world. Perhaps surprisingly, regions with the largest concentrations of data centers tend to offer the most cost-effective rates, largely due to economies of scale. ... Another key strategy for cutting data center rental costs is to consolidate servers. Server consolidation reduces the total number of servers you need to deploy, which in turn minimizes the space you need to rent. The challenge, of course, is that consolidating servers can be a complex process, and businesses don’t always have the means to optimize their infrastructure footprint overnight. But if you deploy more servers than necessary, they effectively become a form of technical debt that costs more and more the longer you keep them in service. ... As with many business purchases, the list price for data center rent is often not the lowest price that colocation operators will accept. To save money, consider negotiating. The more IT equipment you have to deploy, the more successful you’ll likely be in locking in a rental discount.

Ransomware will thrive until we change our strategy

We need to remember that those behind ransomware attacks are part of organized criminal gangs. These are professional criminal enterprises, not lone hackers, with access to global infrastructures, safe havens to operate from, and laundering mechanisms to clean their profits. ... Disrupting ransomware gangs isn’t just about knocking a website or a dark marketplace offline. It requires trained personnel, international legal instruments, strong financial intelligence, and political support. It also takes time, which means political patience. We can’t expect agencies to dismantle global criminal networks with only short-term funding windows and reactive mandates. ... The problem of ransomware, or indeed cybercrime in general, is not just about improving how organizations manage their cybersecurity, we also need to demand better from the technology providers that those organizations rely on. Too many software systems, including ironically cybersecurity solutions, are shipped with outdated libraries, insecure default settings, complex patching workflows, and little transparency around vulnerability disclosure. Customers have been left to carry the burden of addressing flaws they didn’t create and often can’t easily fix. This must change. Secure-by-design and secure-by-default must become reality, and not slogans on a marketing slide or pinkie-promises that vendors “take cybersecurity seriously”.

The challenges for European data sovereignty

The false sense of security created by the physical storage of data in European data centers of US companies deserves critical consideration. Many organizations assume that geographical storage within the EU automatically means that data is protected by European law. In reality, the physical location is of little significance when legal control is in the hands of a foreign entity. After all, the CLOUD Act focuses on the nationality and legal status of the provider, not on the place of storage. This means that data in Frankfurt or Amsterdam may be accessible to US authorities without the customer’s knowledge. Relying on European data centers as being GDPR-compliant and geopolitically neutral by definition is therefore misplaced. ... European procurement rules often do not exclude foreign companies such as Microsoft or Amazon, even if they have a branch in Europe. This means that US providers compete for strategic digital infrastructure, while Europe wants to position itself as autonomous. The Dutch government recently highlighted this challenge and called for an EU-wide policy that combats digital dependency and offers opportunities for European providers without contravening international agreements on open procurement.