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Consumer Goods and Services


Northern Ireland energy prices 'could stay high into winter'

2026-04-22 13:43:17| BBC News | Business | UK Edition

NI Affairs Committee told even if conflict ends immediately it will take time for supply chains to return to normal.


Category: Consumer Goods and Services
 

Three ways the latest inflation figures affect you

2026-04-22 13:39:25| BBC News | Business | UK Edition

How high could inflation get? And what could it mean for borrowers and savers around the country?


Category: Consumer Goods and Services
 

Three ways the latest inflation figures affect you

2026-04-22 13:39:25| BBC News | Business | UK Edition

How high could inflation get? And what could it mean for borrowers and savers around the country?


Category: Consumer Goods and Services
 

Telecommunications


Turning Cloud Environments Into Operational Advantage in Healthcare

2026-04-17 00:15:22| The Webmail Blog

Turning Cloud Environments Into Operational Advantage in Healthcare caro2698 Thu, 04/16/2026 - 17:15 Cloud Insights Turning Cloud Environments Into Operational Advantage in Healthcare April 22, 2026 by Rich Fletcher, Global Healthcare Marketing Director, Rackspace Technology Link Copied! Recent Posts Turning Cloud Environments Into Operational Advantage in Healthcare April 22nd, 2026 What our research reveals about cloud maturity in UK healthcare April 17th, 2026 Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 Related Posts Cloud Insights Turning Cloud Environments Into Operational Advantage in Healthcare April 22nd, 2026 Cloud Insights What our research reveals about cloud maturity in UK healthcare April 17th, 2026 AI Insights Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 AI Insights Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 Cloud Insights The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 Healthcare eaders turn cloud adoption into measurable efficiency through performance optimization, modern architecture and disciplined governance. Note: All statistics cited in this article are drawn from the 2026 Research Report, From Cloud Adoption to Cloud Advantage in Healthcare. Healthcare organizations have moved decisively into the cloud era, yet outcomes remain uneven. Adoption is widespread, but only a small segment of organizations translate cloud investments into measurable operational efficiency. According to the Rackspace 2026 Healthcare Cloud Report, just 15% of healthcare organizations qualify as Cloud Leaders, meaning cloud adoption is fully integrated into business strategy and aligned with organizational objectives. This distinction is significant in that it reflects a shift from cloud as infrastructure to cloud as an operational and strategic enabler. The gap between adoption and advantage is where efficiency is either created or lost. Cloud adoption and cloud value are not the same Most healthcare organizations have made meaningful progress in cloud adoption. Many cite improved reliability, flexibility and security as primary drivers. Adoption alone, however, does not deliver efficiency. The report highlights a mixed financial reality: 59% of organizations say cloud costs meet or fall below expectations, while 40% report higher-than-expected costs. This divergence reflects execution maturity. Organizations that treat cloud as a lift-and-shift exercise often carry forward inefficiencies from legacy environments. Without architectural redesign, governance and ongoing optimization, cloud environments can introduce new layers of complexity. Cloud Leaders take a different approach to cloud operations and optimization. They operate cloud as a continuous optimization model. Teams align workloads to the most appropriate environments, monitor consumption and refine architectures over time. The outcome extends beyond cost control to improved operational performance. Performance optimization as a core efficiency lever Operational efficiency in healthcare is closely tied to system performance. Latency, availability and responsiveness shape clinical workflows and influence patient outcomes. Cloud Leaders treat performance as a strategic priority. The data reinforces this pattern. Seventy-six percent of Cloud Leaders cite optimizing performance and minimizing latency as a primary driver for cloud adoption, compared to 32% of other organizations. This focus supports tangible operational improvements: Faster access to patient data Fewer delays in clinical decision-making Improved user experience for clinicians and staff Organizations with less-mature cloud strategies often contend with fragmented environments that introduce latency and inefficiencies. Modern architecture enables scalable efficiency Architecture remains one of the clearest differentiators between Cloud Leaders and their peers. Leaders design cloud environments intentionally to support scalability, resilience and efficiency. Hybrid cloud has emerged as a dominant model in healthcare, offering flexibility to balance performance, cost and compliance requirements. The report shows hybrid cloud accounts for 19% of workloads overall, rising to 31% among Cloud Leaders, compared to 17% for less-mature organizations. This reflects a more mature operating model. Cloud Leaders: Place workloads based on sensitivity and performance needs Integrate public and private environments with consistency Reduce dependence on any single platform The result is an adaptable infrastructure that supports both operational demands and innovation. Eliminating legacy constraints unlocks efficiency Legacy infrastructure continues to limit operational efficiency across healthcare. These systems restrict interoperability, increase maintenance overhead and slow transformation efforts. The report underscores the scale of the challenge: 75% of healthcare organizations say legacy infrastructure limits their ability to modernize. By contrast, Cloud Leaders operate with fewer legacy constraints. Only 10% report that legacy systems significantly limit modernization efforts, compared to 44% of less-mature organizations. Legacy environments often accumulate technical debt over time, creating operational drag and limiting agility. Addressing these dependencies as part of a broader cloud strategy helps reduce that burden and unlock efficiency gains: Improved data integration across systems Faster deployment of new applications Lower maintenance and support costs Efficiency extends beyond infrastructure Operational efficiency in healthcare extends beyond IT systems into clinical workflows, administrative processes and decision-making. Cloud maturity begins to unlock broader value, particularly through artificial intelligence. The report shows that 44% of healthcare organizations have realized improved operational efficiency from AI initiatives. These gains include automation of routine administrative tasks, accelerated data analysis and reduced clinician workload. AI adoption remains uneven across healthcare organizations. Cloud maturity continues to shape outcomes, as organizations with more advanced cloud environments are better positioned to deploy and scale AI due to stronger data integration, governance and infrastructure capabilities. This relationship creates a reinforcing cycle between cloud and AI capabilities: Cloud enables data accessibility and scalability AI applies that data to automate and optimize processes Efficiency gains support further cloud investment Cloud Leaders already operate within this cycle, while others continue building the necessary foundations. Governance and strategy drive outcomes Across performance, architecture, legacy modernization and AI adoption, a consistent pattern emerges: operational efficiency depends on how technology is managed. Cloud Leaders distinguish themselves through stronger governance discipline and operational control, including: Alignment between cloud strategy and business objectives Financial governance that maintains cost control Intentional architecture design Ongoing modernization of legacy systems These capabilities support a more controlled, predictable and efficient operating model. The executive imperative: From adoption to advantage For healthcare executives, the conversation has evolved. Cloud adoption is now baseline. Cloud advantage defines differentiation. Achieving cloud advantage requires a deliberate shift: From migration toward optimization From infrastructure focus toward measurable outcomes From isolated initiatives toward an integrated strategy Organizations that make this transition reduce operational friction, improve system performance and support innovation at scale. In a sector shaped by rising costs, workforce pressures and increasing demand, operational efficiency remains foundational. Cloud Leaders show that with the right strategy, architecture and governance, cloud delivers consistent and scalable efficiency. Read more in the 2026 Research Report: From Cloud Adoption to Cloud Advantage in Healthcare Tags: Privat Cloud Cloud Insights Healthcare


Category: Telecommunications
 

What our research reveals about cloud maturity in UK healthcare

2026-04-16 16:16:37| The Webmail Blog

What our research reveals about cloud maturity in UK healthcare jord4473 Thu, 04/16/2026 - 09:16 Cloud Insights What our research reveals about cloud maturity in UK healthcare April 17, 2026 by Rich Fletcher, Global Healthcare Marketing Director, Rackspace Technology Link Copied! Recent Posts What our research reveals about cloud maturity in UK healthcare April 17th, 2026 Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 AI Agents Are the Actor Your Kubernetes Governance Didnt Plan For April 8th, 2026 Related Posts Cloud Insights What our research reveals about cloud maturity in UK healthcare April 17th, 2026 AI Insights Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 AI Insights Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 Cloud Insights The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 AI Insights AI Agents Are the Actor Your Kubernetes Governance Didnt Plan For April 8th, 2026 UK healthcare organisations are advancing cloud adoptionwhile navigating legacy systems, integration challenges and evolving workload strategies. Earlier this year, we partnered with Coleman Parkes Research to survey 75 NHS IT and digital leaders across the UK, examining how cloud is being used across healthcare and where it is delivering consistent value. The data shows clear progress. Cloud is established across the NHS, supported by formal strategies, growing governance maturity and sustained investment. It also highlights a more specific challenge. Organisations are operating within complex, distributed environments, where outcomes depend less on access to cloud and more on how effectively those environments are integrated, managed and aligned to operational priorities. Cloud strategy adoption is increasing while maturity and alignment vary Cloud adoption is advancing across the NHS, though strategy and governance maturity vary between organisations. Forty percent report having a formal cloud strategy, and 20% say cloud operations are well managed within their IT strategy. At the same time, none describe cloud as fully integrated into business strategy. Where that alignment is still developing, the impact is practical. Teams are managing fragmented environments, balancing cost visibility across multiple platforms and working around integration constraints between cloud services and existing clinical systems. Decisions about workload placement, performance optimisation and security controls are often made within those constraints rather than through a fully aligned strategy. From what we see, organisations that have progressed further in aligning cloud with operational priorities tend to have clearer governance, more predictable cost management and greater control over how systems and data interact across environments. Workload placement continues to shift Our research shows a high level of movement in where workloads are placed. Thirty-seven percent of organisations have moved workloads between cloud providers, and 29% have moved workloads from public cloud back to on-premises environments. These decisions sit at the intersection of several pressures. Data security and regulatory requirements define where sensitive workloads can run. Existing clinical systems influence how easily new platforms can integrate. Operational teams need visibility and control across environments that have evolved over time rather than being designed as a whole. In that context, workload placement becomes an ongoing process rather than a one-time decision. Organisations are adjusting environments to meet current requirements while trying to maintain flexibility for the future. Legacy architectures continue to shape cloud outcomes Legacy architecture and technical debt continue to influence how cloud capabilities are deployed and integrated. Fifty-two percent of organisations say legacy systems significantly limit their ability to modernise, and 70% report moderate to high levels of technical debt. These constraints are most visible in cybersecurity, compliance and data integration. Electronic patient record (EPR) systems provide a clear example. Adoption is widespread, with 85% of organisations using EPR platforms. However, confidence in interoperability remains limited, with only 20% reporting strong confidence in how those systems connect with others. This creates a structural challenge. Cloud introduces new capabilities, but the value of those capabilities depends on how well systems exchange data and operate together. Where integration is complex, progress depends on incremental change rather than large-scale transformation. Outcomes correlate with the quality of integration and control Across our findings, a consistent pattern emerges. Organisations that have integrated cloud into their operating model report stronger control over cost, clearer governance and more predictable performance. Where integration is weaker, teams spend more time managing complexity across systems and environments. The challenge now is to operate cloud environments as part of a coherent, well-governed model that supports clinical and operational priorities, with integration, cost management and control addressed as part of the design rather than through ongoing adjustment. Strengthening the foundation already in place Cloud adoption continues to advance across the NHS, with organisations building capability at different stages of maturity. The next stage of progress depends on how effectively those environments function together in practice. That involves reducing technical debt where it limits integration, designing architectures that support consistent data flow across systems and ensuring that security and compliance requirements are addressed within the design of those environments rather than after the fact. It also requires closer alignment between cloud strategy and the priorities that drive day-to-day operations across clinical and administrative teams. These factors determine whether cloud delivers isolated improvements or sustained, system-wide value. The opportunity now is to ensure cloud environments operate with the consistency, integration and control required to support the next phase of digital care. For a deeper view of the UK findings, including AI adoption and cyber resilience, explore the full NHS-focused Rackspace Healthcare survey report. Tags: Private Cloud Cloud Insights Healthcare


Category: Telecommunications
 

Defending at Machine Speed: Rethinking Security Operations in the AI Era

2026-04-15 20:25:27| The Webmail Blog

Defending at Machine Speed: Rethinking Security Operations in the AI Era jord4473 Wed, 04/15/2026 - 13:25 AI Insights Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15, 2026 by Craig Fretwell, Senior Manager, IT Security, Rackspace Technology Link Copied! function copyFunction() { // Get the text field var copyText = document.getElementById("copyInput"); // Select the text field copyText.select(); copyText.setSelectionRange(0, 99999); // For mobile devices // Copy the text inside the text field navigator.clipboard.writeText(copyText.value); // Alert the copied text // alert("Copied the text: " + copyText.value); showNotification() } function showNotification() { var notificationEl = document.querySelector('span.notification-message'); //console.log('test1'); notificationEl.classList.add('notify'); setTimeout(function() { notificationEl.classList.remove('notify'); }, 1000); } Recent Posts Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 AI Agents Are the Actor Your Kubernetes Governance Didnt Plan For April 8th, 2026 The New Operating Model for AI-native Platforms April 7th, 2026 Related Posts AI Insights Defending at Machine Speed: Rethinking Security Operations in the AI Era April 15th, 2026 AI Insights Software Valuations, AI Pressure and the Infrastructure Question Platforms Cant Ignore April 13th, 2026 Cloud Insights The Cyber Resilience Bill Changes the Question. Are UK Organisations Actually Ready? April 9th, 2026 AI Insights AI Agents Are the Actor Your Kubernetes Governance Didnt Plan For April 8th, 2026 AI Insights The New Operating Model for AI-native Platforms April 7th, 2026 Cyberattacks now move at machine speed. This blog explores how AI reduces investigative friction, improves SOC response consistency and helps you defend before exposure occurs. Not in a hypothetical sense. In practical terms, an adversary may already be inside an environment, moving quietly without detection. The clock is already running. The window is open. The question is how muchtime remains before anyone starts paying attention. This is the pressure modern security operations teams manage every day. Not the breach at the moment of entry. Not the alert that fires when a rule trips.  The response, the investigation, the judgment call made under time pressure with incomplete information.  That is where outcomes are determined. And as the threat landscape accelerates, the margin for getting it wrong is shrinking faster than most organizations have adapted to. AI sits at the center of that acceleration, on both sides of it. The risk environment is changing in ways that are no longer theoretical, and the security organizations that understand what that actually demands of them operationally are the ones pulling ahead. What is harder to find is a clear-eyed answer to what that means in practice, and what to do about it without chasing capability for its own sake or building dependencies that introduce new fragility. Mean time to exposure and why it changes everything Most breach narratives focus on the wrong moment. The entry point matters, certainly. But the entry is not where organizations succeed or fail under pressure. That happens in the gap between compromise and consequence, the period between the moment an adversary gains access and the moment that access produces real, measurable harm. That gap has a name worth using: Mean Time to Exposure, or MTTE. Not to be confused with how long it takes to detect a breach, MTTE is specifically the time between initial compromise and the point at which the damage becomes real and visible to the world. Before stolen data surfaces on a leak site. Before a regulator is notified not by you, but by a journalist who got there first. That window was once significant. Just six years ago, Mandiant research placed global median dwell time at around 78 days. That was enough operational runway to detect, investigate, contain, and manage the narrative before consequences became permanent. That window has already compressed.  Today the global median sits at around ten days. For ransomware incidents it can be as low as five. The board that thought it had weeks to manage disclosure no longer has them. The legal team preparing notification strategy finds the data already in circulation before filings are filed.  Every response playbook built on the assumption of meaningful dwell time becomes a liability the moment that assumption breaks. A 78-day MTTE becoming a ten-day MTTE is not a hypothetical. It has already happened. And it is one that immediately resonates with any executive who has ever sat in a breach response call wondering how much had already left the building before anyone noticed. What adversaries are actually doing with AI Here is where most security conversations go wrong. The instinct, when facing a threat that appears to be accelerating, is to reach for the most compelling explanation. AI-powered attacks. Automated adversary tooling. Machine-speed intrusions. These narratives are everywhere right now, and the honest answer is that some of them are very real, and more specific than most people realize. We cannot always see directly inside an adversarys toolkit.  What we can do is observe the tactics, techniques, and patterns that surface in observability data, and what those patterns increasingly suggest is that AI is enabling a level of precision and speed that changes the nature of the threat. It goes well beyond automation. Consider what becomes possible when AI is applied to stolen data. Tools configured with the right prompts could detect fear, embarrassment, or deception within internal communications.  They could extract names, roles, and organizational structure in seconds. They could map relationships and surface the conversations that carry the most leverage, a CEO discussing a sensitive acquisition, a private exchange between a CIO and a whistleblower about unethical practices. What that produces is not just stolen data. It is targeted intelligence. And it turns what might have been a straightforward ransomware event into a psychologically precise extortion campaign aimed directly at the board, crafted to create maximum pressure with minimum response time.  The message arrives not as a generic demand, but as something specific and personal, designed to elevate an IT incident into a legal, reputational, and executive crisis before the security team has finished scoping the initial compromise. That is what compresses MTTE. Not just speed, but precision. Adversaries no longer need weeks to sift through what they have stolen. That work can now happen in hours, and that directly shrinks the window defenders have to act before consequences materialize. Whether every intrusion involves this level of sophistication is beside the point. The capability exists, it is being observed in practice, and defenders cannot know in the moment which scenario they are facing. That uncertainty itself demands a faster, more consistent investigative response. What AI actually changes for defenders 1. Volume is the first problem. Security operations teams are not failing because their analysts are not smart enough. They are failing because the volume of signals, alerts and contextual data exceeds any reasonable human capacity to process at speed. An analyst in a live investigation is collecting evidence, correlating events across multiple sources and forming a judgment about risk while the clock on MTTE is running. Something has to give. Usually, its speed. 2. Friction is the mechanism. This is the friction AI is designed to remove. Not the judgment. Not the accountability. The collection, correlation and assembly work that sits between an analyst and the moment they can reason about what is happening. When AI is applied thoughtfully, evidence gets assembled faster. Investigative pathways become clearer before an analyst has to decide. The analyst still makes the decision. Accountability and judgment remain human. What improves is the speed to understanding and the confidence behind the actions that follow. 3. Predictability is the outcome. In most environments, the quality of an investigation depends on who is running it. A senior analyst with deep institutional knowledge investigates differently than a junior analyst on a weekend shift. That variance is not a character flaw. It is a structural problem. It means outcomes depend on individual heroics rather than repeatable process. Every security team has that one person. The analyst who holds everything together, who knows where the bodies are buried and who everyone calls when something goes sideways. That is not a capability. That is a dependency. AI reduces that dependency by making the best analysts knowledge and process available across the team. The result is a more reliable operation, not just on good days but on every shift, across every region and for every customer. What better actually looks like All of that operational improvement, faster enrichment, consistent investigation quality, earlier alignment on risk, produces something that matters beyond the SOC floor.  Capability is the starting point. Outcomes are what prove it is working. The organizations doing this well can point to three measurable outcomes: We understood the situation sooner. We aligned on risk faster.  We acted with greater consistency. Those three things are measurable outcomes grounded in how the team performed, not in a story about what the attacker was using. They hold up under questioning from a CFO, a board or a regulator because they are observable ad real. That is the signal that separates a security organization that has genuinely operationalized AI from one that is still building toward it. The window is already running MTTE is compressing. The tools adversaries are using to extract value from what they steal are getting faster and more precise. And the organizations that close the gap between how quickly threats develop and how quickly their teams can respond are the ones that come out of incidents with their reputation, their customers, and their options intact. That gap closes through better investigative speed, more consistent outcomes across every analyst and every shift, and earlier alignment on what the risk actually is before it has time to grow.  AI, applied with discipline and clarity of purpose, is what makes that achievable at scale. The gold standard is a team that understands situations sooner, aligns faster, and acts with consistency that does not depend on who happens to be working that day.  That is not an aspirational target.  It is an operational one. And it is exactly what modern security operations, built the right way, delivers. Learn how modern security teams use Microsoft Sentinel and AI-assisted workflows to reduce investigative friction and respond to threats at machine speed. Download the e-book: Rethinking the SOC for the AI Era. See how fast your security operations really respond. Request a Microsoft Sentinel Visibility & Resilience Check to evaluate detection coverage, investigation workflows, and response readiness across your environment.  Tags: Security AI Insights Microsoft


Category: Telecommunications