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Think of your enterprise AI strategy like a rocket. You can have the best design and the brightest minds behind it, but without the right fuel, it’ll never leave the launchpad. The fuel that AI needs is data, and the good news is that enterprises certainly no longer have to worry about finding enough AI data. Now, it’s about getting the right data and using it in the right ways. Datasets are growing, both in volume and complexity. We’re capturing data in ways we wouldn’t have thought possible before. Take manufacturing, for example: 15 years ago, factories tracked production levels and nothing else. Today, sensors are everywhere, streaming a constant flow of data. It’s a whole new world of possibilities. This trend toward more data from more sources is happening across every industry, and it will continue into the future. The question is, how do we turn that data into business results? AI provides the game-changer. I truly believe that AI will impact every enterprise, and that it will be just as disruptive as the internet was. AI has the power to transform everything about the way we live our lives. Exciting, right? But first, enterprises need a future-proof AI data strategy. That starts with deploying infrastructure on a flexible platform that’s positioned to support data growth. Deploying in AI-ready data centers We all know that AI requires data centers. But not all data centers are created equally, particularly not when it comes to AI. To start, AI isn’t just one thing happening in one place: it’s a series of interconnected processes happening in locations throughout the world, with data constantly moving between them. Therefore, you don’t need an AI-ready data center; you need an interconnected platform of AI-ready data centers. Specifically, you may need different types of data centers for different AI workloads: Hyperscale data centers are primarily used by service providers for training large language models (LLMs) that require extremely high compute capacity. Colocation data centers are often used by enterprises for private models that use smaller training datasets. Edge data centers are used for inference workloads, which need to be distributed close to data sources to keep latency low and ensure the most current data possible. The right digital infrastructure partner brings it all together—hyperscale, colocation, and edge data centers—on a single global platform. Organizations can also access the network infrastructure needed to tie those data centers together and keep data moving between them. AI-ready data centers are both powerful and interconnected The need for powerful hardware to support AI workloads is a given. It’s also clear that enterprises would struggle to get that inside legacy on-premises data centers that weren’t built with the needs of AI hardware in mind. In contrast, leading colocation providers have consistently invested in AI innovations for years, and they will continue to do so. Enterprises can capitalize on those investments just by deploying inside high-performance colocation data centers. This also saves them the cost and complexity of attempting to modernize their own data centers. Being AI-ready also means ensuring connectivity and data access, which you can’t do if your data center is isolated. This is where reliable, low-latency network infrastructure comes into play. Again, this is something high-performance data centers provide, and conventional data centers do not. AI takes more than just hardware AI workloads have grown to require extreme power density, which means that an AI-ready data center must also provide advanced cooling capabilities. Liquid cooling is now required to support modern AI hardware like GPUs. This is yet another example of a technology commonly found inside high-performance colocation data centers. Also, the energy requirements of AI workloads will further complicate sustainability efforts for many enterprises. It’s more important than ever to work with an operator that’s pursuing innovations in both renewable energy and data center efficiency. Ensure scalable, secure connectivity Reliable, high-performance network infrastructure is essential to move growing AI datasets quickly and securely, whether that means moving them between data centers or to ecosystem partners. Most enterprises include multiple public cloud providers in their AI ecosystems, but they also rely on specialized partners such as GPU as a Service (GPUaaS) and data hosting providers. No matter who they’re connecting with, enterprises need to do it on their own terms. This means maintaining control over data to avoid latency and high egress costs. Also, using direct interconnection instead of the public internet allows for higher performance and better security. You can move your data where it needs to go using a dedicated connection that’s not publicly accessible. Thus, you can make public cloud and XaaS part of your AI strategy while minimizing the risks. Modernizing your multicloud architecture With the right multicloud networking capabilities, you can ensure the flexibility to move workloads between clouds whenever the need arises. This is important, as your cloud needs will inevitably change over time. To meet your compliance requirements, you’ll need control over where your data moves and who can access it. If you put your data into cloud native storage, it could mean giving up that control. Due to public cloud costs and complexity, you may find it difficult to get that data back out of the cloud, thus limiting your future infrastructure flexibility. Also, major public clouds are global by design, which means you likely can’t use them for workloads with specific data residency and sovereignty requirements. Instead, deploying your own storage environment in proximity to multiple clouds and service providers allows you to access services on demand while also maintaining control over your data. This means that you can avoid vendor lock-in, meet your regulatory requirements, and ensure workload portability. Scale network infrastructure as you scale AI datasets As AI datasets grow ever-larger, scaling network infrastructure without sacrificing performance will be another essential aspect of a future-proof AI data architecture. A software-defined interconnection solution such as Equinix Fabric® can help. It not only ensures high performance, but it also makes it easy to scale as needed. Instead of needing to add new physical connections, you’ll be able to add capacity to your existing virtual connections with the click of a button.

In today’s escalating cyber threat landscape, traditional backup strategies are insufficient to protect the most critical assets. Modern threat actors have evolved beyond simply encrypting operational systems to systematically targeting backup infrastructures, leaving organizations with no safety net for recovery. According to Rubrik Zero Lab’s “The State of Data Security in 2025” report, 74% of organizations discovered their backup or recovery systems were at least partially compromised during attacks, with over a third (35%) suffering complete compromise. This trend underscores the critical need for cyber resilience strategies that specifically protect backup environments from sophisticated threats while enabling rapid recovery capabilities. As organizations continue their digital transformation journeys, backup systems have become prime targets for attackers due to their value as the last line of defense. Most security frameworks fail to adequately safeguard these systems against ransomware and other sophisticated techniques. Threat actors exploit this oversight, using advanced methods to corrupt backup catalogs, encrypt backup data stores, and manipulate recovery environments, making restoration of operations nearly impossible after an attack. The consequences can be devastating: extended downtime, significant revenue losses, and reputational damage that can persist for years. For instance, IBM’s Cost of a Data Breach Report 2024 found that 70% of organizations experienced a significant or very significant disruption to business resulting from a breach, and only 12% reported that they had fully recovered. For those who were able to recover completely, more than three-quarters said it took them more than 100 days. Adding to the complexity, Rubrik telemetry reveals that 27% of high-risk sensitive files contain critical digital data such as API keys, usernames, and account numbers—exactly the information threat actors seek to hijack identities and infiltrate systems. This protection gap demands a fundamental shift from traditional backup methodologies to zero-trust data security architectures that isolate backup environments, implement immutable storage, and provide continuous validation of recovery readiness. Forward-thinking organizations across industries are recognizing this critical vulnerability and taking decisive action to modernize their backup security posture. These companies are implementing comprehensive data protection strategies that go beyond traditional backup approaches, integrating advanced cyber resilience capabilities that can withstand sophisticated attacks while ensuring rapid recovery. As the architect behind the world’s most pervasive compute platform, Arm understands that securing intellectual property is paramount to innovation. The company implemented robust data protection and governance measures through strategic partnerships, ensuring the technology that powers everything from edge devices to cloud infrastructure is safeguarded. The world’s leading global travel retail and F&B player embarked on an ambitious digital transformation to modernize legacy systems and strengthen cyber resilience. Avolta implemented comprehensive SaaS data protection featuring logical airgap, immutability, and rapid data restoration capabilities. This transformation ensures business continuity for their 20,000+ collaborating corporate users while providing the visibility and automation that reduces recovery times and provides the flexibility needed to quickly deploy new business initiatives across their global operations. This global technology leader that designs and manufactures trucks, and provides financial services and information technology, selected advanced data protection as their worldwide standard to support growth across four continents. PACCAR’s implementation focuses on identifying clean recovery points and establishing effective cyber attack response mechanisms. This resilient foundation allows the company to concentrate on core strategic initiatives, including modernizing security operations and advancing next-generation vehicle technologies. With 150 years of risk management experience, Zurich North America moved proactively to improve capabilities and address the New York State Department of Financial Services Cybersecurity Regulation guidance by replacing complex legacy systems with cutting-edge data security technology. Their transformation includes automated ransomware investigation, immutable backups, logical airgap protection, and rapid recovery across hybrid cloud environments. This modernization strengthened cyber resilience while reducing legacy infrastructure complexity and accelerating innovation in the cloud-first economy. Recognizing technology’s critical role throughout their business operations, Domino’s proactively implemented robust immutable data protection and accelerated recovery capabilities. Their strategic approach ensures rapid threat response, minimal downtime, and secure global operations that protect thousands of franchisees and team members who depend on uninterrupted systems for daily operations. These organizations demonstrate that proactive backup security transformation is not just possible but essential for maintaining competitive advantage in today’s threat landscape. For CIOs and security leaders, the message is clear: backup systems can no longer be treated as passive repositories but must be architected as active components of a comprehensive cyber defense strategy. The cost of inaction—measured in downtime, recovery expenses, and reputational damage—far exceeds the investment required to implement modern, resilient backup security frameworks. The evolution from traditional backup to cyber-resilient data protection represents a fundamental shift in how organizations approach business continuity. Those who act decisively today will be positioned to weather tomorrow’s increasingly sophisticated cyber threats while maintaining the operational agility needed for continued growth and innovation. For more information, visit here. Anneka Gupta, Chief Product Officer, Rubrik Rubrick Anneka brings more than a decade of product and SaaS expertise with a track record of driving revenue growth, navigating expansions to new markets, and overseeing diversity, inclusion, and belonging initiatives. She joins Rubrik from LiveRamp where she was the President and Head of Product and Platforms leading product development and go-to-market operations and strategy. Anneka also sits on the board of directors for Tinuiti. source

“Most of the time, AI is touching confidential data or business-critical data,” Aerni says. “Then the thinking about the architecture and what the workload should be public vs. private, or even on-prem, is becoming a true question.” The public cloud still provides maximum scalability for AI projects, and in recent years, CIOs have been persuaded by the number of extra capabilities available there, he says. “In some of the conversations I had with CIOs, let’s say five years ago, they were mentioning, ‘There are so many features, so many tools,’” Aerni adds. “Now when I’m having the same conversation, they say, ‘Actually, I’m not using those tools that much now.’ They are all looking for stability and predictability.” source

For development, they use Azure’s machine learning services to train the AI on vast amounts of imaging data and medical literature. The resulting solution can analyze medical images and patient data to identify patterns and suggest possible diagnoses, serving as a second opinion for doctors. By using Azure’s cloud, the hospital guarantees data security and compliance with health regulations during this AI analysis. Phase 4: Implementation and adoption Step 7: Implement the agentic solution in phased rollouts. Instead of a big bang deployment, start with a pilot program or a controlled rollout in one department or location. This allows the team to validate the solution in a real-world setting, measure results and work out any issues on a small scale before broader implementation. Monitor the pilot’s performance against the success criteria defined in the roadmap (Phase 2). Step 8: Drive user adoption through change management. Train employees and end-users on the new AI tool – not just how to use it, but how it benefits them. Communicate success stories and efficiency gains to build buy-in. It’s important to address concerns or resistance: some staff might fear AI will replace their jobs, so clarify that the AI is there to assist and elevate their roles. Executive champions should continuously reinforce the transformation vision. If needed, adjust workflows to best integrate the AI into daily operations. Example: A large retail company rolling out an AI-powered inventory management system might first pilot it in a single flagship store. In this pilot, store managers and inventory clerks use the new system to forecast demand and automate re-ordering. Early results show reduced stockouts and waste, confirming the solution’s value. The company then gradually expands the implementation to more stores, region by region. Throughout this process, it holds training sessions for store staff on the new system and highlights that the AI helps ensure popular products are always in stock (improving sales and easing employees’ workload). By phasing the adoption, the retailer also fine-tunes the system’s algorithms with data from each new store rollout and it addresses employee feedback, ensuring high adoption rates and minimal disruption to operations. Phase 5: Monitoring and optimization Step 9: Continuously monitor the performance of the agentic solution. Define key metrics (KPIs) that align with the project’s goals – e.g., processing time reduction, error rate, customer satisfaction scores, cost savings – and track them in real time if possible. Use analytics dashboards to observe how the AI is performing and where there might be bottlenecks or drifts in accuracy. This phase often benefits from setting up an AI Operations (AIOps) or monitoring team. Step 10: Optimize and evolve the solution based on data and feedback. Treat the agentic system as a living solution that requires periodic tuning. Update the AI models with new training data as more information is gathered, adapt to changing business conditions (like new regulations or market trends) and incorporate new features or improvements identified post-launch. Also, establish a feedback loop with users to capture their experiences — perhaps the AI could be making decisions faster, or needs to handle a new scenario. Version upgrades and integration of emerging technologies should be planned as part of a continuous improvement roadmap. Example: A bank that has deployed AI-driven customer service agents and fraud detection systems keeps a close eye on these tools. The bank’s analytics show how quickly the AI chatbot resolves inquiries and tracks a reduction in call center volume. It also monitors the fraud detection AI in real-time, verifying how many fraudulent activities it catches and ensuring false positives are minimal. Using these insights, the bank makes adjustments: for instance, if the chatbot struggles with a certain category of questions, the AI team refines its natural language understanding. If new types of fraud emerge, data scientists feed those patterns into the fraud model to improve its accuracy. This ongoing optimization cycle helps the bank continuously improve user experience and service efficiency over time. By staying responsive to data, the bank ensures its agentic AI solutions remain effective and deliver sustained value.  source

AI alignment isn’t a checkbox; it’s a continuous discipline. Enterprises must embed oversight to keep AI safe, scalable and true to business intent. source

In this issue: There are lots of great reasons for running AI applications in your own data centers – provided they’re up to the task. AI puts new demands on virtually every aspect of data centers from servers and networks to power grids and more. Learn what it takes to build and run an AI-ready data center. source

For more robust needs, portable solutions like battery-powered Starlink mini units can provide temporary connectivity for up to four hours without external power. Looking ahead, we’re working on small cell solutions with CBRS LTE/5G capabilities that can create localized coverage zones connected via satellite backhaul.  It’s essential to acknowledge that satellite communications will complement, rather than replace, terrestrial networks within our lifetime. The physics of satellite communications — including the 90-second window during which a fast-moving LEO satellite remains in your field of view – creates inherent limitations for real-time applications.  At MetTel, we’re closely monitoring these developments and implementing relevant technologies to enhance our clients’ connectivity options. Just as we helped organizations separate reality from hype during the early days of 5G, we’re committed to providing clarity around satellite communications capabilities and practical applications for enterprise use.  source

Indeed, there still appears to be considerable uncertainty surrounding AI regulation in Europe. Swedish Prime Minister Ulf Kristersson recently called the rules confusing, Reuters reported. And according to a survey from US cloud provider AWS, two-thirds of European companies still do not understand the extent to which they are responsible for the use of AI in their organizations under the AI ​​Act. Karsten Wildberger, Germany’s new Digital Minister, also appears open to considering an extension of the implementation deadline for the AI ​​Act. In the absence of guidelines, norms, and technical standards, companies would need more time to prepare, the minister said at a meeting of European communications and digital ministers in Luxembourg in early June. Germany’s new Digital Minister Karsten Wildberg no longer rules out delays in the introduction of the next AI Act stages. Juergen Nowak – shutterstock.com Other countries are going even further. Wildberger’s colleague from Denmark, Caroline Stage Olson, has even called for a reform of all rules governing the digital space in Europe, including the General Data Protection Regulation (GDPR). source