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Turning AI into Measurable Outcomes with Private Cloud
2026-02-10 18:59:26| The Webmail Blog
Turning AI into Measurable Outcomes with Private Cloud jord4473 Tue, 02/10/2026 - 11:59 AI Insights Turning AI into Measurable Outcomes with Private Cloud February 12, 2026 By Amine Badaoui, Senior Manager AI/HPC Product Engineering, Rackspace Technology Link Copied! Recent Posts Turning AI into Measurable Outcomes with Private Cloud February 12th, 2026 How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 Getting Started With AI: A Practical Path Forward February 5th, 2026 Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 Related Posts AI Insights Turning AI into Measurable Outcomes with Private Cloud February 12th, 2026 Cloud Insights How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 AI Insights Getting Started With AI: A Practical Path Forward February 5th, 2026 Cloud Insights Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 AI Insights Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 As AI moves from pilots to production, outcomes matter more than models. This article explores how private cloud supports governed, cost-predictable AI at enterprise scale. Every board is now asking, What is our AI strategy? Far fewer can answer the harder question: What has AI actually delivered for our margins, growth, risk profile and customer experience? Most enterprises have accumulated pilots and proofs of concept, yet only a small number translate those efforts into measurable P&L impact. The difference is not who has the most advanced model. It is who treats AI as a business outcome engine, supported by an operating and infrastructure model they control. Increasingly, private cloud is emerging as that control plane, enabling organizations to scale AI with more predictable cost, stronger governance and clearer business impact. The value gap: lots of AI, little business impact Across industries, AI initiatives often begin with curiosity and technology. Someone wants to do something with generative AI, build a chatbot or experiment with copilots. Teams spin up environments, connect a model and produce an impressive demo. Then the project stalls. The issue is usually straightforward: the effort started with tools, not outcomes. No KPI was defined, no accountable owner was assigned, and no core process was redesigned. When budgets tighten or priorities shift, these initiatives are often the first to be deprioritized because leadership cannot clearly connect them to growth, cost efficiency or risk reduction. At the same time, senior leaders are becoming more deliberate about where AI runs. Public cloudonly deployment models can introduce data sovereignty challenges, regulatory complexity and unpredictable cost. For workloads tied to critical data, revenue streams or compliance obligations, many organizations are increasingly turning to private cloud and private AI not as legacy patterns, but as strategic control planes for delivering measurable outcomes. Why serious AI is moving to private cloud For business-critical AI, where it runs is often as important as what it does. Private cloud gives organizations greater control where it matters most: Data control: Sensitive customer, financial, health and operational data remains within a defined perimeter, aligned with residency and sovereignty requirements. Risk control: Identity, access and audit controls are enforced consistently across workloads, making AI-driven decisions easier to explain to regulators, customers and internal audit teams. Economic control: Capacity can be shaped and rightsized, helping to avoid the surprise invoice problem that can emerge during unconstrained AI experimentation. This allows AI to be embedded directly into day-to-day workflows such as claims, onboarding, underwriting, care management and shop-floor operations, rather than operating as a disconnected sidecar. For C-suite and line-of-business leaders, private AI on private cloud is increasingly viewed as a governance and value-creation strategy, not simply an infrastructure choice. From pilots to an AI outcome portfolio A more useful way for leaders to think about AI is as a portfolio of outcome bets aligned to the levers they already manage. At a high level, this portfolio tends to cluster around three themes: Protect the downside Reduce leakage, strengthen compliance and improve safety and resilience. Improve the run Increase productivity, lower cost to serve, reduce errors and shorten cycle times. Grow the business Launch new AI-enabled products, personalize experiences and open new revenue streams. Private cloud helps by giving this portfolio a consistent foundation. The same platform defines who can access which data, which models are approved, how workloads are monitored and how cost is tracked. Leaders establish a clear view of which AI initiatives exist, which are delivering value and where to double down or divest. A four-step framework for outcome-first private AI Executives do not need to become AI engineers. They need a simple way to connect business priorities, processes and AI capabilities. A practical framework can be expressed in four steps. 1. Start with the outcome, then the process Begin every AI conversation with a business outcome: What are we trying to change? Which KPI will show that we have succeeded? Over what timeframe? Examples might include Cut onboarding time from 10 days to three, or Increase cross-sell conversion in our top two segments. Once the outcome is clear, map the process that drives it today. Where does time, friction or error accumulate? Which steps depend heavily on reading, writing or routing information? These are often the areas where AI can realistically provide assistance. 2. Choose the right private AI pattern With outcome and process in view, the focus shifts to the pattern of AI required. At a business level, three patterns cover most use cases: Smarter decisions Models that score, predict or classify fraud detection, risk scoring, demand forecasting or next-best-offer selection. Smarter content and interactions Generative models that draft responses, summarize documents or support agents and employees as copilots. Smarter workflows Agent-like systems that stitch together multiple steps: retrieving information, invoking systems and proposing or executing actions. All of these patterns can run on a private cloud AI platform, close to the systems and data they depend on. The technical details matter to architects; what matters to leaders is whether the pattern fits the process, the risk tolerance and the available data. 3. Build governance and trust from day one Governance cannot be bolted on at the end. For private AI to earn the right to scale, three elements must be designed upfront: Data and access Which data sources are in scope? Who can use them? How are permissions granted and revoked? Private cloud allows these rules to be enforced centrally. Guardrails and responsibility What decisions can AI make or automate, and where must a human remain in the loop? How will bias, hallucination and other failure modes be monitored and addressed? Transparency and auditability How will AI-assisted decisions be explained to regulators, customers and employees? Can you show who did what, when and using which model? When these questions are addressed early, AI initiatives are far more likely to clear risk, legal and compliance hurdles and to survive first contact with real-world complexity. 4. Measure value in short, sharp loops Finally, value must be measured as systematically as cost. For each use case, teams should be able to articulate: The baseline: Current cost, cycle time, error rate, revenue or satisfaction score The target: The improvement expected over a defined period The feedback loop: How performance will be tracked and how quickly the team can respond Short, time-boxed experiments, measured in weeks and months instead of years, allow leadership to make clear decisions. If a private AI initiative moves a KPI in the right direction, invest and scale it. If it does not, adjust the approach or stop it and learn. Because this work runs on a shared private cloud platform, components, patterns and learnings can be reused across the portfolio, compounding value over time. Outcomefirst stories: what good looks like Three brief examples below illustrate how an outcome-first approach to private AI plays out in pratice. Customer service: A service organization deploys AI copilots for agents and virtual assistants for customers, all running on a private cloud platform. Routine queries are handled automatically, while agents receive suggested replies and next-best actions informed by the full history of the relationship. Average handling time declines, first-contact resolution improves and sensitive interaction data remains within the organizations environment. Operations and risk: A financial or insurance organization builds private AI models to scan transactions and documents for anomalies and potential issues. Cases are automatically prioritized and routed to specialists, with full traceability for every recommendation. Investigation time shrinks, losses are reduced and regulatory reviews become more straightforward because decisions are explainable. Product and innovation: A product team uses a standardized private AI platform to experiment with new capabilities such as intelligent search, personalized offers and document automation. Because data, models and guardrails already operate within a governed environment, teams can move from idea to pilot to production more efficiently. Time to market shortens, and multiple business units reuse the same platform and patterns. In each case, the headline is not the model or the technology. It is the business metric that moved, and the fact that the organization retained control over data, risk and cost. A call to action for Csuite and lineofbusiness leaders The question for leadership is no longer Should we use AI? It is, How do we turn AI into measurable, durable business value on our terms? Three moves can help shift the conversation: Set a 1224-month AI outcome agenda: Identify a small set of enterprise-level KPIs cost to serve, churn, time to market, loss ratio, patient outcomes and frame AI initiatives against them. Create a cross-functional AI value council: Bring together business, technology, data, risk and operations leaders who jointly own both the upside and the downside of AI. Treat private AI on private cloud as a strategic capability: Invest, govern and report on it the way you would any core platform, from ERP to CRM with clear ownership, clear metrics and clear accountability. Done well, AI stops being a scatter of pilots and becomes a disciplined, outcome-driven program. Private cloud becomes the control plane that allows organizations to decide where and how that value is created securely, predictably and on their terms. Learn how Rackspace Private Cloud AI supports outcome-driven AI initiatives with predictable performance and governance. Tags: AI Insights
Category: Telecommunications
How Proactive Threat Hunting Stopped INC Ransom Before the Alert
2026-02-06 20:15:19| The Webmail Blog
How Proactive Threat Hunting Stopped INC Ransom Before the Alert jord4473 Fri, 02/06/2026 - 13:15 Cloud Insights How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9, 2026 by Craig Fretwell, Global Head of Cybersecurity Operations, Rackspace Technology Link Copied! Recent Posts How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 Getting Started With AI: A Practical Path Forward February 5th, 2026 Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 How to Keep Azure Cloud Costs Under Control with Continuous Optimization January 26th, 2026 Related Posts Cloud Insights How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 AI Insights Getting Started With AI: A Practical Path Forward February 5th, 2026 Cloud Insights Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 AI Insights Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 Cloud Insights How to Keep Azure Cloud Costs Under Control with Continuous Optimization January 26th, 2026 A real-world threat hunting engagement shows how INC Ransom activity was uncovered early, before alerts fired and before ransomware could take hold. Modern security operations rely heavily on automated detection. Alerts, analytics and automated responses play a critical role in identifying known threats and responding at speed. But even the most mature security operations center cannot account for every possible adversary behavior. That gap is where proactive threat hunting becomes essential. Threat hunting is designed to surface malicious activity that does not yet meet the threshold of an incident. This is the kind of activity that blends into normal operations, avoids known detection logic or unfolds slowly over time. If you rely only on alerts, this behavior is easy to miss. A recent threat hunting engagement conducted by the Rackspace Cyber Defense Center demonstrates exactly why this capability matters. Safeguarding critical emergency communications The environment in question belonged to a government services organization that supports critical emergency communications. Availability, reliability and trust were non-negotiable. Any service disruption, particularly one caused by ransomware, would have had immediate operational and public safety implications. Like many organizations operating critical services, this environment relied on standard preventative controls and alerting to identify known threats. At the time of the engagement, there were no active incidents, no high-severity alerts and no visible signs of compromise. That was precisely the point. The absence of alerts did not indicate the absence of risk. It created an opportunity to look deeper for adversary behavior that had not yet reached an alerting threshold. A proactive, analyst-led threat hunt As part of a scheduled, analyst-led threat hunting exercise, the Rackspace Cyber Defense Center conducted a focused review of identity, endpoint and network telemetry collected over the prior month. The hunt assumed potential compromise and intentionally looked beyond alert-based detections. If youre responsible for a mature security environment, this type of threat hunt may feel counterintuitive. There was no incident to respond to and no alert demanding investigation. Instead, analysts worked from the premise that not all adversary activity announces itself. The goal was to identify behaviors that should not exist, even when controls appear to be working as expected. Rather than responding to known indicators, analysts searched for adversary behaviors aligned to the MITRE ATT&CK framework. This included techniques commonly associated with ransomware activity, such as credential abuse, unauthorized remote access, lateral movement and early-stage prepositioning. This hunt was not driven by an incident. Instead, it was driven by intent and the understanding that early-stage adversary behavior is often easiest to find before it becomes an alert. Focusing on the INC Ransom threat group The threat hunt focused on tradecraft associated with INC Ransom, a globally active ransomware and data extortion group that has been operating since at least mid-2023. The group has been linked to attacks against public sector organizations and critical services, often relying on credential compromise, Living off the Land techniques and the abuse of legitimate remote access tools before moving to encryption or extortion. If you are responsible for defending a complex environment, this kind of activity may sound familiar. These techniques are designed to blend in. They rely on tools and access patterns that can appear legitimate, especially in environments with diverse users and administrative workflows. At the time of the hunt, there were no dedicated detections in place tuned specifically to INC Ransoms early-stage behaviors. That gap proved critical. It meant adversary activity could progress quietly, without triggering alerts, unless someone was actively looking for it. What the hunt uncovered before impact The threat hunt did not surface a single obvious indicator. Instead, it revealed a pattern of early-stage adversary behavior unfolding across identity, endpoint and network telemetry. Individually, each signal was subtle. Taken together, they pointed to an active intrusion progressing toward ransomware execution. Because analysts werent constrained by alert thresholds, they were able to identify these behaviors early, before encryption, data exfiltration or service disruption occurred. The findings fell into several key areas. Identity and authentication abuse Analysis of authentication telemetry revealed cleartext authentication events associated with a legitimate user account. This activity deviated from established baselines and suggested potential credential exposure. Correlation with logon timing and source infrastructure elevated the risk assessment. Unauthorized account activity and RDP access Threat hunting analysis identified unauthorized RDP logon activity tied to an unapproved user account. The account did not align with documented access requirements or operational usage patterns. Session attributes and originating infrastructure were inconsistent with normal administrative behavior. Unauthorized remote access tooling Endpoint execution telemetry revealed the presence of an unapproved remote access tool, AnyDesk.exe. Installation and execution context indicated unauthorized use rather than sanctioned administrative activity. The organization confirmed that only approved remote access tools were permitted within the environment. Network-based pre-impact indicators Proactive network analysis identified multiple malicious external IP addresses generating high-volume inbound traffic that was initially permitted at the application layer. In addition, ransomware-related artifacts, including README.txt and README.html files, were observed originating from suspicious external infrastructure. While encryption had not yet occurred, these indicators aligned with known INC Ransom pre-impact behavior. Viewed in isolation, none of these findings would necessarily indicate an active ransomware event. Together, they revealed a clear trajectory toward impact. This is where proactive threat hunting proved decisive. By identifying low-signal behaviors early and connecting them across telemetry sources, analysts were able to surface attacker intent before the environment reached an incident threshold. Containment before disruption Once the activity was identified, containment actions were taken quickly and in close coordination with the customer. The focus was on stopping adversary progression without disrupting normal operations. Key actions included: Disabling unauthorized user accounts associated with suspicious authentication and RDP activity Blocking malicious external IP addresses at perimeter and cloud security layers Removing unauthorized remote access tooling after customer validation Sharing confirmed Indicators of Compromise to strengthen environment-wide prevention and monitoring Following containment, analysts conducted a review of subsequent telemetry to validate remediation. No continued malicious activity was observed. Most importantly, the threat was stopped before it reached impact. No ransomware encryption occurred. No data was exfiltrated. No service disruption was experienced. Closing the gaps between alerts This engagement highlights a practical reality of modern security operations. Not all malicious activity generates alerts, and not all compromises begin with a clear incident. Ransomware groups increasingly rely on low-noise techniques that unfold gradually. They abuse legitimate credentials, use approved tools and blend into normal operational workflows. In environments that depend primarily on automated detection, this activity can persist unnoticed unil attackers reach later stages such as encryption or extortion. Proactive threat hunting is designed to close these gaps. By looking for behavior that falls outside expected patterns, analysts can identify adversary activity earlier, validate whether controls are working as intended and uncover blind spots that automated detections do not address. In this case, threat hunting surfaced adversary behavior that would likely have remained invisible until the environment reached an incident threshold. How Rackspace helps Threat hunting is a core part of Rackspace Managed XDR and is delivered through the Rackspace Cyber Defense Center powered by Microsoft Sentinel. It is not treated as a one-off exercise or an escalation step. It is an ongoing, analyst-led capability designed to work alongside detection and response. If you rely primarily on alerts to understand risk in your environment, threat hunting provides a necessary counterbalance. Analysts actively search for emerging adversary behavior that automated logic may miss, using evidence drawn from identity, endpoint and network telemetry. By combining deep security expertise with continuous analysis across these data sources, Rackspace helps you identify risk earlier, validate whether controls are operating as intended and strengthen cyber resilience without waiting for an alert to fire. Take the next step with a Microsoft Sentinel Visibility & Resilience Check to identify detection gaps and improve visibility between alerts. Tags: Cloud Insights
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Getting Started With AI: A Practical Path Forward
2026-02-04 21:44:25| The Webmail Blog
Getting Started With AI: A Practical Path Forward jord4473 Wed, 02/04/2026 - 14:44 AI Insights Getting Started With AI: A Practical Path Forward February 5, 2026 By Madhavi Rajan, Head of Product Strategy, Research and Operations, Rackspace Technology Link Copied! Recent Posts Getting Started With AI: A Practical Path Forward February 5th, 2026 Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 How to Keep Azure Cloud Costs Under Control with Continuous Optimization January 26th, 2026 Using Agentic AI to Modernize VMware Environments on AWS January 22nd, 2026 Related Posts AI Insights Getting Started With AI: A Practical Path Forward February 5th, 2026 Cloud Insights Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 AI Insights Redefining Detection Engineering and Threat Hunting with RAIDER January 27th, 2026 Cloud Insights How to Keep Azure Cloud Costs Under Control with Continuous Optimization January 26th, 2026 AI Insights Using Agentic AI to Modernize VMware Environments on AWS January 22nd, 2026 AI success starts with focus, not hype. This article outlines a phased approach to AI adoption, from improving operations to enhancing customer experiences and unlocking new revenue. Starting with AI can feel overwhelming. Headlines often focus on massive investments by global enterprises building or consuming frontier models at scale. For most organizations, however, that level of GPU-heavy infrastructure is neither required nor practical. If youre not running large-scale production models, the broader AI ecosystem doesnt need to dictate where you begin. Across the cloud landscape, organizations are at very different stages of AI adoption. While Fortune 100 companies invest billions in in-house development, many organizations in the Russell 2000 and beyond are focused on building practical capabilities that help them stay competitive. The question most leaders ask is straightforward: Where do I begin my AI journey? A useful way to answer that question is to think in phases. Most organizations move through three broad stages of AI adoption: operational efficiency, customer-facing experiences and new revenue streams. The level of investment required depends on several factors. These include compute, network and storage needs, the type of models in use, workload volume, organizational readiness and the phase of adoption. Understanding these variables early helps teams focus on use cases that deliver value without unnecessary complexity. Phase 1: Operational efficiency Organizations of all sizes struggle with inefficiencies caused by fragmented data and disconnected systems. These silos slow decision-making and can create costly errors. In some cases, businesses continue paying vendors months after a contract has ended simply because systems do not talk to each other. Using AI to improve operational efficiency across functions such as IT, finance, HR, supply chain, procurement and sales is often the lowest-risk, highest-impact starting point. These use cases are internal, measurable and closely tied to day-to-day productivity. The challenge is not a lack of data, but where that data lives. Critical information is often trapped in separate systems and supported by institutional knowledge that does not scale. When introducing AI, you need to be clear about intent. The goal is not to replace roles, but to remove friction so people can focus on higher-value work. Many established enterprises carry years of technical debt across product, operations, customer success and go-to-market systems. Simply buying an AI copilot rarely solves that problem. Off-the-shelf tools alone cannot bridge disconnected data or deliver meaningful ROI. Real value comes from applying AI on top of an organizations own data and processes. Consider a typical services business. Supply chain data lives in one system, customer records in a CRM and contracts in a homegrown application. The result is a collection of dashboards that offer limited insight into utilization, customer health or revenue trends. AI can act as an intelligence layer across these systems. It can surface which customers are growing, highlight utilization patterns and support scenario modeling. ROI becomes tangible through faster insights, fewer spreadsheets and better decisions. Speed to value also matters. How quickly do teams see results once a model is deployed? In one finance organization, analysts reduced time spent wrangling spreadsheets by roughly 40% with the help of an AI assistant. That time shifted to scenario modeling and analysis, where human judgment delivers the most value. Completing this phase gives organizations a clearer view of what their AI workloads require and how those capabilities can eventually extend to customer-facing value. Phase 2: Customer-facing experiences As AI matures, personalization becomes a key driver of customer retention. Buying AI tools does not equal adoption. AI must deliver specific business outcomes to matter. While automation can support customization, true personalization requires context, judgment and empathy. This applies across both B2C and B2B environments. In financial services, for example, some organizations use AI to assemble client intelligence that includes recent activity, potential opportunities and emerging risks. That insight allows teams to personalize interactions, anticipate needs and identify growth opportunities earlier. Continuous monitoring of customer consumption patterns helps organizations anticipate change. When paired with alerting and recommendations, customer-facing teams can deliver more relevant outreach, predict demand shifts and align offerings more closely to customer goals. This is especially valuable in subscription and recurring revenue models. With the right foundation, teams can enter every customer interaction better informed and more precise. Data, process insight and market context come together, enabling employees to move beyond routine tasks and focus on deeper, strategic engagement. Phase 3: Embedding AI into what you sell The first two phases help organizations improve how they operate and serve customers. The third phase is where AI becomes transformational, embedded into what you sell and directly driving new revenue. Success at this stage looks different by industry. In financial services, AI may streamline onboarding or fraud response while improving the customer experience. In other sectors, AI may become a differentiated product or service in its own right. This shift often requires new business models. Many AI-native companies tie pricing to outcomes rather than consumption alone. In these cases, AI is not just an internal capability, but a core part of the value proposition. Sustaining that value depends on culture and decision-making. AI influences the full lifecycle, from product development to billing and supply chain operations. Real impact only emerges when teams align across functions. While AI excitement dominated recent conversations, the next phase will be defined by how effectively you translate AI into practical execution and measurable outcomes. How Rackspace Technology can help Turning AI ambition into results requires the right foundation, governance and operational support. Rackspace Technology helps organizations design, deploy and manage AI solutions that align to real business goals, whether the focus is efficiency, customer experience or new growth opportunities. With deep expertise across hybrid cloud, data platforms and AI operations, Rackspace provides a structured path from experimentation to production. Learn more about how Rackspace supports AI initiatives here. Tags: AI Insights
Category: Telecommunications
Community Impact 2025: A Global Year of Giving Back
2026-02-02 16:49:06| The Webmail Blog
Community Impact 2025: A Global Year of Giving Back jord4473 Mon, 02/02/2026 - 09:49 Culture & Talent Community Impact 2025: A Global Year of Giving Back February 13, 2026 by Lindsey Stich, Talent Management Program Manager, Rackspace Technology Link Copied! Recent Posts Community Impact 2025: A Global Year of Giving Back February 13th, 2026 Turning AI into Measurable Outcomes with Private Cloud February 12th, 2026 How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 Getting Started With AI: A Practical Path Forward February 5th, 2026 Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 Related Posts Culture & Talent Community Impact 2025: A Global Year of Giving Back February 13th, 2026 AI Insights Turning AI into Measurable Outcomes with Private Cloud February 12th, 2026 Cloud Insights How Proactive Threat Hunting Stopped INC Ransom Before the Alert February 9th, 2026 AI Insights Getting Started With AI: A Practical Path Forward February 5th, 2026 Cloud Insights Effective Housekeeping With Rackspace Managed Snapshot Cleanup January 29th, 2026 In 2025, Rackers worldwide turned compassion into action, volunteering time, supporting critical causes and strengthening communities through global impact programs. At Rackspace, Community Impact isnt just a program its who we are. It reflects our core value of compassion and our belief that when Rackers give back, we strengthen the communities where we live and work while deepening our connection to one another and to our mission. Every hour and every dollar Rackers give is a testament to our shared belief that we can make a difference together. Our collective generosity not only changes lives in our communities but also strengthens the bonds that make Rackspace truly special, said Michelle Peterson, President of the Rackspace Foundation. Throughout 2025, Rackers around the world turned compassion into action. From volunteering time to supporting critical causes, Rackers showed whats possible when we lead with the heart. Here are a few highlights: Rack Gives Back Volunteer Time Off Rack Gives Back is our flagship volunteer program, providing Rackers 40 hours of Volunteer Time Off (VTO) each year to support the causes they care about most. Over the course of the year, Rackers dedicated 17,341 hours equal to 2,167 days supporting local nonprofits, global service events and meaningful community initiatives. From food banks and school partnerships to disaster relief and mentorship programs, Rackers used their VTO to give back in ways that were meaningful to them and impactful for their communities. Rackspace Foundation In 2025, the Rackspace Foundation expanded its impact globally, extending support to educationfocused organizations outside of the United States and deepening its commitment to strengthening communities wherever Rackers live and work. Contributions to The Rackspace Foundation totaled $223K for the year. The funds support building stronger communities through education, with a special focus on science, technology, engineering, and math (STEM). By investing in local schools and education-focused organizations, the Foundation helps create opportunities for future innovators while reinforcing Rackspaces commitment to learning, access, and long-term community impact. Racker2Racker This year marked the launch of Racker2Racker, further expanding our Community Impact efforts by creating a new way for Rackers to support one another during times of unexpected financial hardship. Funded by Rackers, for Rackers, Racker2Racker is a peer-to-peer assistance program designed to ensure no one has to navigate difficult moments alone. In its first year, 275 donors raised $16,342, demonstrating the strength of our culture and the care Rackers have for one another. By adding Racker2Racker to our portfolio of Community Impact programs, we extended our impact inward as well as outward reinforcing that compassion at Rackspace starts with taking care of our own. Making a Difference Where It Matters Most Throughout the year, Rackers united around impactful events that supported communities across the globe. Marc Nourani Memorial Food Drive: Rackers served 400 families (1,400 individuals total) distributing 39,870 pounds of food and included a $12,500 company donation. Tech or Treat: Rackers raised $30K to support STEM education for the next generation of technologists. Kerr County Flood Relief: Rackers provided $35,800 in aid to families impacted by severe flooding. SOS Childrens Villages: Rackers in India hosted Independence Day celebrations. Child Rights and You: Rackers in India mentored teens in Pune on digital safety and future aspirations. Foundation for Excellence: Rackers in India supported students seeking higher education in STEM. Cancer Patients Aid Association: Rackers in India supported cancer patients who do not have access to traditional cancer treatments due to funding constraints. The Poppy Appeal: Rackers in the UK raised funds which support the Royal British Legions vital work supporting serving personnel, veterans and their families. Munich Food Bank: Rackers in Germany supported an effort that collected more than 396,800 pounds of food and helped distribute it across 30 distribution points, reaching 22,000 people in need. Hacker School: Rackers in Germany helped promote digital literacy for children, supporting efforts to reduce inequality by mentoring students, teaching basic programming skills, and introducing them to career paths in IT. Looking Ahead 2025 was a year of impact and inspiration. With the continued evolution of our Community Impact programs including the globalization of the Rackspace Foundation and the addition of Racker2Rackers were building momentum for even greater contributions in 2026. Giving back is how we put our values into action every day. Thank you for showing the world what Rackers can do when we come together. Learn more about Rackspaces culture here. Tags: Culture & Talent
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