The cybersecurity landscape has changed. Again. Not long ago, defenders were focused on keeping up with increasingly sophisticated malware, ransomware groups, and phishing campaigns. Today, we’re facing something faster: AI-assisted adversaries capable of creating novel attack paths, adapting in real time, and operating at machine speed. The challenge is more than just detecting additional threats. It is also about keeping pace with threats that are evolving faster than traditional security operations were ever designed to handle. That’s why ProCern has partnered with AiStrike. Together, we’re helping organizations rethink what Managed Detection and Response (MDR) should look like in an era where both attackers and defenders have access to AI. The result is a modern security model that combines AI-driven scale with human expertise, giving organizations deeper visibility, more effective response capabilities, and measurable operational improvements. As discussed in our recent webinar, “Modern MDR to Address Modern Threats,” the future of security isn’t AI versus humans. It’s AI and humans working together. The Problem with Traditional MDR Most security operations centers were built around a simple reality: Humans made the decisions. Alerts arrived. Analysts investigated them. Escalations happened. Detections were tuned manually. Threat hunting required time and expertise. That model worked when threats moved at human speed. Today, attackers can leverage AI to create custom attacks, adjust tactics, and test defenses in ways that dramatically increase both speed and complexity. Traditional approaches often struggle under the weight of: Alert fatigue caused by overwhelming volumes of low-value notifications Security tools that operate in isolation Rising SIEM and data storage costs Detection rules that can’t keep up with rapidly changing threats Limited visibility into how decisions are made Human judgment remains invaluable, but it can no longer be the bottleneck for every security decision. Instead, organizations need ways to deploy human expertise strategically while allowing machines to handle the scale. Why ProCern Chose AiStrike At ProCern, we’ve spent years operating a 24×7 SOC and helping organizations navigate real-world security challenges. We know firsthand that adding more alerts, dashboards, or expensive tooling doesn’t automatically improve security outcomes. What stood out about AiStrike was its ability to address several persistent security challenges simultaneously: Correlating data from multiple security platforms Investigating alerts automatically and at scale Showing the evidence behind decisions Enabling human review when it matters most Improving detections continuously over time Instead of replacing security analysts, AiStrike helps elevate them. Repetitive investigation work can be automated, while ProCern’s security experts focus on validation, context, business impact, and response decisions. That combination creates something more powerful than either component alone. Moving From Alerts to Actionable Intelligence One of the most compelling portions of the demo showcased how AiStrike transforms alerts from disconnected events into comprehensive attack narratives. Instead of asking an analyst to manually gather information from multiple systems, the platform automatically correlates telemetry from sources such as endpoint tools, Windows logs, SIEM platforms, and network security products into a unified investigation. The platform then: Builds an attack timeline Generates a verdict Summarizes relevant evidence Documents investigative reasoning Presents all findings for review The result is dramatically faster understanding without sacrificing transparency. Human-in-the-Loop Security Many AI-powered platforms promise autonomous outcomes but provide limited insight into how decisions were reached. ProCern and AiStrike take a different approach. The platform explicitly shows: Questions investigated Evidence collected Alternative hypotheses considered Logic behind recommendations This creates a transparent workflow where security teams can validate outcomes rather than simply accept them. Human expertise remains critical. AI can gather context at remarkable speed, but analysts still provide the business understanding and institutional knowledge necessary for high-confidence response decisions. Detection Engineering at Machine Speed Security teams often spend countless hours tuning detections, troubleshooting rules, and reducing false positives. AiStrike approaches detection engineering differently. During the demonstration, the platform analyzed a problematic detection rule, identified where the failure occurred, evaluated possible causes, and generated recommendations to resolve the issue. It documented every step in the process, allowing analysts to review and approve changes before implementation. Rather than treating detection engineering as a quarterly project, AiStrike enables continuous improvement. For organizations battling alert fatigue, this capability can be transformative. Better detections mean fewer distractions and more focus on what actually matters. Smarter Data Pipelines, Lower Costs When it comes to data, security leaders face a difficult balancing act. They need more telemetry to investigate threats effectively, yet storing and processing every log can become prohibitively expensive. Through ProCern’s data pipeline approach and related technologies showcased during the webinar, organizations gain more control over how security data is collected, filtered, enriched, and routed. Instead of sending every event to a high-cost SIEM environment, data can be ranked and directed to the appropriate destination based on its security value. This creates two significant benefits: Reduced operational costs Improved signal-to-noise ratio In other words, spend less money while making analysts more effective. That’s a rare combination. AI with Guardrails One of the most important lessons organizations are learning about AI is that governance matters. AiStrike’s architecture includes approval workflows, audit trails, performance measurement, and role-based controls. Security teams can decide exactly where automation ends and human approval begins. For example: Low-risk activities can be automated. Medium-risk actions can require SOC review. High-impact actions can require multiple approvals. This allows organizations to gain the speed advantages of AI without surrendering control. A New Model for Modern MDR The major takeaway from our partnership with AiStrike is that with Modern MDR, instead of replacing analysts with artificial intelligence, what’s important is giving talented security professionals better tools to do their jobs. Together, ProCern and AiStrike enable organizations to: Investigate every alert with greater depth Reduce alert fatigue Improve detection quality Lower operational costs Gain complete visibility into decision-making Scale security operations without scaling headcount linearly Organizations that combine machine-speed analysis with human judgment, machine-scale investigation with expert oversight, and AI-driven efficiency with complete transparency are setting themselves up for success. That’s the vision behind the ProCern and AiStrike partnership, and we’re excited to help organizations put it into practice. Keeping up
Artificial intelligence is already embedded in how work gets done. From writing code and analyzing data to summarizing meetings and drafting content, AI tools promise speed, efficiency, and scale. But as more organizations rush to adopt AI, many are discovering a hard truth: AI introduces new risks just as quickly as it delivers new value. We are still in the early days of AI in the workplace. Policies are evolving, regulations are catching up, and many organizations are learning, sometimes the hard way, through trial and error. In this environment, a single careless prompt or unchecked output can create compliance violations, security incidents, or reputational damage. Before going all‑in on AI at work, it’s worth understanding where the real risks lie, and what it actually takes to protect yourself and your organization as AI becomes part of everyday operations. A Baseline Rule: Client and Customer Data Do Not Belong in Public AI. Ever. Let’s start with a principle that should not be negotiable: Client, customer, or sensitive internal data should never be entered into public AI systems. This isn’t just a compliance concern. It’s a business reality. Public, consumer-grade AI tools are not designed to act as secure extensions of your internal systems. Even when vendors make assurances about data handling, the risk profile is fundamentally different from approved enterprise platforms. This applies to: Customer and client records Personally identifiable information (PII) Financial data Proprietary documents Internal communications or strategy materials If the data matters to your customers, or to your company’s future, it does not belong in a public AI prompt. Full stop. This is where many organizations stumble: employees are trying to be helpful and efficient, but the tools they’re using were never meant to handle regulated or sensitive information. Good intentions don’t reduce risk exposure. Information Compliance: The Risk You Already Know (But Might Still Be Ignoring) If you work in healthcare, finance, government, or any regulated industry, you’re already familiar with compliance frameworks like HIPAA, GDPR, or industry‑specific data protection rules. These regulations don’t disappear just because AI is involved. Uploading sensitive data such as patient records, customer information, internal financials, or proprietary documents into a third‑party AI tool can violate: Regulatory requirements Contractual obligations Non‑disclosure agreements Even well‑intentioned employees can put their jobs and companies at risk if they treat public AI tools like secure internal systems. Enterprise AI platforms can mitigate some of this risk, but only when they are properly configured, approved, and governed. Personal chatbot accounts and browser‑based tools rarely offer the same guarantees. Rule of thumb: If you wouldn’t email the data to an external vendor, you shouldn’t upload it into an AI tool. Data Privacy: When Convenience Becomes Exposure Most AI tools are owned and operated by third parties. Many rely on user interactions to improve their models, which creates real concerns about how data is stored, reused, or retained. Even when providers claim they do not train models on user data, organizations must still account for: Data residency requirements Retention policies Access controls Auditability This is why some organizations have restricted or banned specific AI tools altogether. A more sustainable approach is to establish clear AI usage policies that define: Which tools are approved What data is allowed What data is strictly prohibited For individual employees, a few best practices go a long way: Use company‑approved or enterprise AI accounts Read (yes, actually read) privacy policies Follow internal AI usage guidelines Never upload sensitive PDFs, images, or datasets without explicit approval Shadow AI: The Risk No One Thinks They Have One of the fastest‑growing risks isn’t malicious behavior—it’s unsanctioned AI use. Employees often adopt AI tools quietly: Browser extensions Meeting transcription bots Free trials Personal accounts used for work tasks This “Shadow AI” creates blind spots for IT and security teams. Data can leave the organization without logging, monitoring, or controls, increasing exposure without anyone realizing it. Shadow AI is rarely an employee problem—it’s a governance problem. Organizations that want AI adoption without chaos must make approved tools easy to access and policies easy to understand. Hallucinations: When AI Sounds Confident and Is Completely Wrong Large language models don’t understand truth; they predict language. That’s why AI hallucinations (fabricated facts, citations, or explanations) are such a persistent issue. We’ve already seen real‑world consequences: Fake legal cases cited in court filings Invented books and sources published by news outlets Confidently incorrect technical guidance AI can be a powerful drafting and ideation tool, but it cannot be trusted to self‑validate its own output. The only reliable safeguard is human review. If AI output affects customers, finances, legal decisions, or compliance, it must be verified before use. Ownership and Accountability: AI Doesn’t Take the Blame One of the most dangerous assumptions about AI is that responsibility shifts along with the work. It doesn’t. If AI produces an error, a biased outcome, or a compliance violation, the organization and the human decision‑makers are still accountable. “The AI told me to” is not a defensible position in audits, lawsuits, or performance reviews. Clear ownership matters: Who approves AI use cases? Who reviews AI outputs? Who is accountable when something goes wrong? Organizations that succeed with AI treat it as an accelerator—not a replacement—for human judgment. Direct Attacks: AI as a New Entry Point AI systems rely on APIs, integrations, data pipelines, and infrastructure. Each of these components can become an attack vector. Threats include: Data breaches involving AI‑connected systems Data poisoning that corrupts AI outputs Sabotage through manipulated inputs or integrations AI doesn’t replace the need for strong cybersecurity fundamentals, but rather increases the importance of them. Phishing attacks, credential theft, and misconfigurations can expose AI systems just as easily as email or file shares. Bias and Discrimination: When Automation Scales Harm AI systems reflect the data they are trained on and that data often contains historical bias. When AI is used in areas like hiring, lending, or decision‑making, biased outputs can: Harm individuals Damage trust Trigger legal and regulatory consequences
When organizations talk about AI readiness, the conversation often starts and ends with technology. Data platforms. Infrastructure. Tools. According to McKinsey’s State of AI in 2025 report, most organizations are still in pilot phases of AI utilization. A majority say they are using AI in at least one business function but at the enterprise level the majority are still experimenting or piloting stages with only one-third starting to scale their AI programs. Many AI initiatives struggle not because of technical limitations, but because the business isn’t fully prepared to lead the change. AI readiness is not an IT checkbox. It’s a business strategy decision. The common misconception about AI readiness It’s easy to assume that once the right tools are in place, AI success will follow. Technology is only one part of the equation when setting up your AI strategy. In fact, 86% of organizations delayed AI deployments by up to a year due to security and quality concerns. Often organizations say that enhancing their customer insights and personalization is their top AI but yet there is a 5.8% gap between what they hope to achieve and what they actually do. AI initiatives often fail when: Business goals are unclear Leadership expectations are misaligned Adoption isn’t planned Governance is an afterthought Teams don’t know how AI fits into daily work The future of your AI readiness depends on the actions you take today. Prioritizing data management and information governance, defining clear goals, and building a foundation of trust across your departments through training and setting clear expectations on how AI will be used in your organization. Most important is setting up safeguards to protect against potential risks and negative consequences. Why treating AI as “just IT” causes predictable problems When AI readiness is owned solely by IT, organizations often encounter: Misaligned expectations between business and technical teams Solutions that work technically but aren’t adopted Difficulty justifying ROI Unclear accountability for outcomes Increased risk as AI scales These challenges slow progress and erode confidence. A more practical way to approach AI readiness Business‑led AI readiness doesn’t require some magic tool or platform. It starts with structure. Effective organizations take a phased approach: Align leadership around goals and constraints Assess current capabilities across data, people, and operations Identify and prioritize high‑impact use cases Build a realistic roadmap for adoption and management This approach turns AI from a wish list into a manageable, outcome‑driven initiative. Making AI readiness actionable AI readiness is most powerful when it creates shared understanding. It brings business and IT together around what matters, what’s possible, and what comes next. When readiness is treated as a strategic exercise and not a technical audit, organizations are better positioned to invest confidently, adopt responsibly, and scale successfully. A structured readiness effort helps organizations move from curiosity to capability that is grounded in strategy, not hype. Reach out to ProCern today to see how we can assist with an AI Readiness Assessment.
Artificial intelligence is no longer a future discussion but a priority for many organizations. But while interest in AI is high, results often fall short. Not usually because the deployment doesn’t work, but because organizations aren’t fully ready to apply it in ways that deliver real business value. According to the Cisco AI Readiness Index 2025, only a small fraction of businesses (8-13%) are fully prepared to deploy and scale AI effectively yet nearly 78% of organizations have reported using AI. On top of that, many companies lack the governance needed when implementing AI practices. Before investing in pilots, platforms, or large‑scale initiatives, it’s worth taking a step back. An AI readiness self‑check helps leadership teams surface gaps, align expectations, and determine whether the organization is prepared to move from experimentation to execution. This isn’t about scoring your organization for the sake of a number. It’s about clarity. Why an AI readiness self‑check matters AI initiatives often stall when organizations jump straight to tools. Readiness, however, spans much more than technology. It includes strategy, data, governance, skills, operating models, and leadership alignment. A quick self‑check helps answer a critical question: Are we positioned to turn AI into outcomes or are we just exploring? Below are ten practical questions every executive team should be able to answer before scaling AI. The AI Readiness Self‑Check: 1. Do we have clearly defined business outcomes for AI? Strong readiness starts with intent. Teams should be able to articulate what they want AI to improve. Is it efficiency, accuracy, speed, risk reduction, customer satisfaction, or something else? If success can’t be described in business terms, it will be difficult to measure or sustain. 2. Have we identified which workflows are best suited for AI? Not every process will benefit from automation or intelligence. Organizations that succeed with AI focus on specific, high‑impact workflows rather than applying AI broadly. Readiness means knowing where AI fits and why. 3. Is leadership aligned with priorities and expectations? AI initiatives touch multiple parts of the business. Without leadership alignment on goals, risk tolerance, and investment expectations, projects often lose momentum. Executive consensus is one of the strongest predictors of AI success. 4. Do we understand the quality and availability of our data? AI depends on usable, accessible, and trustworthy data. Readiness requires clarity around data ownership, consistency, and relevance to the intended use cases. Many organizations underestimate this step. 5. Do we have governance in place for AI decisions? As AI becomes embedded in operations, organizations need to know who approves use cases, monitors performance, and manages risk. Governance doesn’t slow innovation; it enables responsible scaling. 6. Do we have the right mix of skills to support AI over time? AI readiness is not just a data science problem. It requires collaboration across business, IT, security, and operations. Organizations should assess whether they have the skills to deploy, manage, and evolve AI. Think beyond the launch step and into the future. 7. Is there a plan for how AI will be used in your daily operations? AI creates value only when it’s adopted. Readiness includes understanding how AI outputs will be used, by whom, and how decisions or workflows will change as a result. Without this, AI often becomes “shelfware.” 8. Can our infrastructure and support model handle AI at scale? Whether AI is deployed on‑premises, in the cloud, or in a hybrid model, organizations need confidence that their environment can support growth, performance, and reliability over time. Readiness considers future scale, not just current capability. 9. Do we know how success will be measured? AI initiatives should have clear success metrics tied to business impact and adoption. Beyond the technical aspects of AI, what metrics will be important for your organization to measure when it comes to utilizing AI. For example, quicker response time for customers or decreased time spent on reporting. Readiness means defining what “working” looks like before deployment begins. 10. Do we have a roadmap beyond the first win? Early success is important, but sustained value comes from sequencing initiatives over time. Organizations that plan beyond the first use case are better positioned to mature their AI capabilities. A roadmap turns momentum into strategy. Interpreting your answers If several of these questions were difficult to answer or brought up topics that you did not consider when utilizing AI. It’s a sign that your organization may want to think through its AI initiatives a bit more. A structured readiness approach can help translate these open questions into priorities, actions, and sequencing. Turning insight into action An AI readiness assessment is a starting point. The real value comes from converting insight into a clear path forward. You want to align leadership, validate assumptions, and build an actionable roadmap. Organizations that approach readiness thoughtfully tend to move faster, spend more wisely, and see stronger long‑term results from AI. If your answers raised questions around alignment, data, or next steps, a structured AI readiness assessment can help transform uncertainty into a practical, business‑led roadmap. Reach out to ProCern today to inquire about our AI Readiness Assessment and taking the first steps towards using artificial intelligence at your organization.
Your data center is the central nervous system of your enterprise, orchestrating the flow of information that powers everything from everyday operations to complex computational processes. AI is pushing that system in ways traditional infrastructure wasn’t built to handle. Training models and processing large datasets require constant, high-speed data exchange between systems. If your network can’t keep up, workloads get sluggish, timelines stretch, and expensive compute resources sit idle waiting on data. To support these demands, your data center networks need to be designed differently. From how traffic moves across the environment to how systems scale and communicate, the underlying architecture has a direct impact on performance. What It Takes to Support AI Workloads at the Network Level Network performance is all about speed, consistency, and the ability to handle sustained demand without bottlenecks. AI workloads amplify these requirements, placing continuous pressure on the network as data moves between compute, storage, and GPUs. Supporting these workloads comes down to a few core network capabilities: High Throughput Across the Network: AI workloads move large volumes of data between systems. Your network needs to be able to handle that volume, so the data can move quickly without creating backlogs Low and Predictable Latency: Small delays add up quickly when systems are constantly exchanging data. Consistent, low latency keeps workloads running efficiently and prevents performance dips across the environment. Efficient Internal (East-West) Traffic Flow: Most AI traffic moves laterally within your data center between servers, GPUs, and storage. The network needs to be built for this internal flow, with traffic distributed efficiently across the environment. Distributed Processing and Services: As traffic increases, sending everything through centralized systems can slow things down. Modern networks handle functions like security and traffic management closer to where data is moving, reducing unnecessary routing and maintaining consistent performance. Scalable Architecture: AI workloads grow quickly. Your network needs to expand alongside them without requiring major redesigns or adding unnecessary complexity. Network Visibility and Control: As traffic increases, it becomes harder to identify where slowdowns are happening. Your network needs to make traffic patterns visible, so issues can be addressed before they impact performance. Network Efficiency at Scale: As multiple systems run at the same time, small inefficiencies can add up quickly. Your network needs to keep everything running smoothly across shared infrastructure. Supporting AI workloads puts pressure on every part of your network. It’s less about raw speed and more about maintaining consistent performance, low latency, and stability under sustained demand. Modern data center networking approaches are built around this need, bringing traffic handling, security, and visibility closer to where data is moving. With platforms like HPE Aruba’s CX 10000 series, ProCern can help design networks that embed services directly into the infrastructure, reducing bottlenecks and keeping performance consistent across high-demand environments. To learn more about how Aruba data center networking supports AI workloads, contact us here.
Public sector organizations sit at the crossroads of mission, service, and trust. From state agencies and county governments to school districts and public safety departments, these institutions hold some of the most sensitive data and deliver services communities depend on every day. And yet, they’re under constant cyber pressure. With attackers growing bolder, budgets stretched thin, and legacy systems humming along like old cars held together with optimism and duct tape, the threat landscape isn’t slowing down, and public sector teams need a defense strategy that’s equal parts resilient, efficient, and downright clever. Enter Managed Detection and Response (MDR): a modern, always-on approach that strengthens cyber resilience without requiring agencies to build their own 24/7 security operations center. Let’s explore how it works, why it matters, and how it keeps services running smoothly even when threats strike. Defending Public Services in a Relentless Threat Landscape Government environments operate under constraints that private-sector organizations might raise an eyebrow at: limited staff, aging infrastructure, strict regulations, and workloads stretched across sprawling environments. Meanwhile, attackers have discovered that public sector agencies are prime targets. Disrupting government systems doesn’t just cause financial damage, it jeopardizes public trust and critical services. Common issues include: Ransomware and phishing attacks targeting endpoints, shared services, and remote workers. Difficulty staffing a 24/7 SOC, especially with competitive cybersecurity hiring markets. Hybrid environments blending legacy on‑prem systems, cloud workloads, and remote access. Compliance pressures including NIST, CJIS, CMMC, and state-level mandates. The need for accountability, auditability, and transparent incident response measures. Modern attackers increasingly use fileless and “living off the land” techniques that mimic legitimate administrative activity. Traditional perimeter defenses simply can’t keep up. Agencies need something better, something proactive, adaptive, and built for mission assurance. MDR That Pairs Machine Speed With Human Judgment ProCern’s AI-Flex MDR gives public sector teams the power of a full-scale, always-on security operation, without the headcount, hardware, or complexity of running one in-house. Think of it as a wise, fast, ever-awake co-pilot for your security stack. Core MDR capabilities designed for government environments: 24/7 Behavioral Monitoring Constant visibility across endpoints, servers, and cloud workloads to detect abnormal activity fast, even the subtle stuff attackers hope you’ll miss. AI-Assisted Threat Intelligence Identifies fileless attacks, credential abuse, and advanced persistent threats that hide in normal system traffic. Human-in-the-Loop Validation Before major actions (like isolating an endpoint), a trained analyst reviews the situation to ensure accuracy and avoid disruption to public services. Rapid Containment & Recovery Isolate threats, roll back malicious changes, and terminate unauthorized sessions, all aligned with your agency’s risk tolerance. Audit-Ready Documentation Detailed, structured reporting that supports compliance, leadership briefings, and regulatory reviews. This hybrid model ensures decisions are fast, accurate, and grounded in real-world mission priorities. When MDR Stops an Attack Mid‑Move Picture this: A state agency notices suspicious behavior coming from a remote employee’s laptop. Unknown to the user, an attacker has stolen credentials and begun poking around internal systems, even attempting to move toward infrastructure holding sensitive citizen data. Here’s how MDR stops the threat in its tracks: Behavioral analytics flag unusual activity from the remote device. The incident escalates to a human analyst for validation and context. MDR isolates the compromised endpoint, without taking down public-facing services. The system blocks lateral movement and terminates unauthorized sessions. Any malicious changes are rolled back, and verification confirms no data was exfiltrated. A full incident report is delivered for audits, briefings, and required disclosures. The threat is neutralized before it becomes a disruption, a headline, or a legislative hearing. Why MDR Delivers Real Value for Public Sector Teams Public sector agencies adopting MDR see benefits that go far beyond improved security. Service Continuity & Public Trust Rapid detection and containment keep essential services running, without unexpected downtime or citizen-facing outages. Regulatory & Audit Readiness Clear, consistent documentation simplifies compliance and ensures traceability across NIST, CJIS, CMMC, and state frameworks. Cost Efficiency Agencies gain enterprise-level protection without hiring a full SOC staff or purchasing specialized tools. IT Team Enablement With continuous monitoring handled by MDR, internal teams can focus on modernization, digital transformation, and mission delivery. Cyber Resilience Is Mission Resilience For public sector organizations, cybersecurity is an essential part of protecting communities and upholding public trust. With the right MDR solution, agencies can outpace modern threats, ensure continuity of critical services, and meet strict compliance demands without stretching teams beyond their limits. By combining AI-powered detection with expert human oversight, MDR delivers a decisive, accountable, and mission-aligned approach to cyber defense. It keeps attackers out, keeps systems resilient, and keeps public services running the way communities expect: reliably, securely, and without drama.
What is Linux? Strictly speaking, Linux is the kernel, or core of Linux distributions. I like to think of the Linux kernel like the base plate for Legos. It’s where all other pieces attach to. A distribution, or “distro” for short, is a complete operating system including a kernel, packages, package managers, and everything else needed. In other words, distros are pre-assembled building block sets depending on user preference and needs. Red Hat, SUSE, and Ubuntu are examples of some of the more popular distros. There are too many others to list here, but here are some resources to give you an idea. Resource One Resource Two Linux Use Cases As a long time Linux user, I can understand why it didn’t take off as a general purpose operating system like MS Windows. There are just too many choices, differences between those choices, and perceived lack of standardization. There’s also the reputation of being difficult to use. Why then would anyone want to use Linux, instead of Microsoft Windows or Mac OS X for example? Like the number of distros, the number of Linux use cases is also very extensive, so I’ll cover just a few popular ones. IoT Open source software is typically free, though some commercially backed distros such as RHEL do charge subscription fees. Linux runs on many different types of hardware such as IoT devices, personal computers, networking equipment, load balancers, supercomputers, and just about anything it seems. It can run on very low end or less common hardware. This makes it perfect for IoT devices, where processing power may be limited, and cost needs to be kept down. Related: Linux Logical Volume Manager Overview “The Cloud” It also powers very high end hardware, including much of what powers “the cloud.” Servers powering the internet need a reliable operating system that can run continuously without downtime, while maintaining a high level of security. There are far fewer circumstances in which Linux based OSes require a reboot. Though I wouldn’t recommend it for most use cases, it is certainly possible for Linux devices to run continuously for YEARS without a reboot. It is also much easier to avoid viruses and malware. This is great for web servers, databases, load balancers, routers, switches, firewalls, storage servers, virtual machine hypervisors, and many other pieces of critical IT infrastructure. Containers The trend to “containerize” everything has taken the world by storm. Though MS Windows containers are now an option, until recently Linux was your only option. It is a much more mature platform for containers. There is much better documentation and support for containers on Linux. It is much lighter weight which allows for much denser deployments, as well as portability. One example of a popular container OS is Alpine Linux . ” It is built around musl libc and busybox. This makes it smaller and more resource efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux environment but a large selection of packages from the repository.” AI AI, machine learning, and deep learning are also getting a lot of attention these days. Linux offers a number of advantages in this space, including better integration with containers. There are many examples and lots of documentation to help someone building an AI project on Ubuntu for example. Want to train your model in the cloud, but deploy at the edge to a low powered IoT device and/or container? You will likely have a much easier time, along with better and more predictable results on Linux. If you are considering an IoT or AI project, and/or the infrastructure required to support it, ProCern has the expertise. Schedule an assessment today, we’d love to help!
In the dynamic world of network technology, Artificial Intelligence (AI) and Machine Learning (ML) are more than just buzzwords; they’re forces propelling us toward a future where networks have the power to self-regulate and self-improve. With advanced algorithms that can learn from data, identify patterns, and make decisions with minimal human intervention, these groundbreaking technologies are revolutionizing the way networks are managed, offering unprecedented levels of efficiency, security, and performance optimization. With AI and ML-powered network management, businesses benefit from: Efficient and superior network performance for a seamless user experience Today’s modern network infrastructure requires intelligent connectivity. To keep up, networks must be able to learn from data to optimize performance, secure data and communications, and streamline operations. AI and ML are the driving forces behind this evolution. With the ability to handle massive data flows, these technologies offer a sophisticated analytical engine that can analyze traffic to anticipate and resolve bottlenecks, prioritize data delivery, and automatically adapt to network conditions on a continuous basis. With the ability to self-adjust, predict needs, and thwart security threats autonomously, AI and ML offer a proactive approach to network management that facilitates seamless operations, which leads to reduced latency and a superior user experience. Proactive troubleshooting and maintenance Before AI and ML, network teams would scramble to identify and rectify problems reactively as they arose, disrupting business and placing strain on resources. AI and ML technology is revolutionizing this approach, with the power to analyze trends and identify anomalies in real-time—transforming the network into a self-optimizing entity that forecasts and mitigates potential issues before they occur. The resulting efficiencies give network teams more breathing room to focus on strategic initiatives that support business goals rather than putting out fires. Related: How Intelligent Insights and AI are Simplifying Network Management Dynamic network security that puts sophisticated cyber threats in their place As cyber threats become more complex, traditional security measures can’t keep up. Cyberattacks are too sophisticated and evolving too quickly. AI and ML introduce dynamic and intelligent network security capable of identifying, detecting, and even predicting and preventing advanced, previously unseen attacks. With a security posture that is both resilient and adaptive, businesses can focus on growth and innovation, confident that their network security is equipped to handle new and highly sophisticated threats as they pop up. A more manageable framework to support modern connectivity Modern networks are complex, supporting a myriad of interconnected devices and applications, each with its own demands for bandwidth, security, and latency. At the same time, they must accommodate the rapid pace of digital advancement and the growing expectations for uninterrupted connectivity. AI and ML simplify this complexity by automating routine tasks such as configuration, deployment, and network device management, allowing IT staff to concentrate on strategic initiatives. This highly streamlined approach transforms network management from a daily grind into a strategic asset. Navigating the intricate web of today’s modern network infrastructure requires a shift from manual processes to automated intelligence. AI and ML stand as pillars in this transformation, offering a more manageable framework that simplifies intricate processes of configuring, deploying, and managing network devices. With an AI-driven network management solution like HPE Aruba Networking SD-WAN, ProCern can help you harness the full potential of AI and ML to transform your network infrastructure, ensuring optimal performance, enhanced security, and streamlined management that aligns with the evolving complexities of your modern connectivity needs. For more information about HPE Aruba Networking SD-WAN, contact us here.
As the digital economy expands, the volume of data generated by businesses continues to grow exponentially. In fact, by 2025, global data creation is projected to grow to more than 180 zettabytes, up from 46.2 in 2020. This surge has pushed the boundaries of traditional data storage systems to their limits, warranting more advanced solutions. Artificial Intelligence (AI) has emerged as a revolutionary force in transforming how data is managed, analyzed, and stored, offering efficiencies that are critical in an era of such immense data growth. AI’s Role in Data Storage Innovation to Unlock New Efficiencies AI-driven predictive analytics: One of the most profound impacts of AI on data storage is its predictive analytics capabilities, with the ability to analyze patterns within vast data sets to predict and preemptively address potential system failures. This proactive approach minimizes downtime and improves data availability, which is crucial for businesses where data accessibility directly impacts operational efficiency. Additionally, predictive analytics extends the lifespan of hardware by identifying issues before they escalate, which can cut costs related to both maintenance and replacement. Enhanced data management and automation: AI streamlines complex data management tasks that traditionally require manual intervention. Automated data tiering and load balancing optimize storage resources in real-time, ensuring data is stored efficiently based on usage and value. Meanwhile, AI-enhanced snapshot management automatically creates and manages backup snapshots according to data’s criticality and usage patterns, enhancing data integrity and improving recovery times. This results in lower operational overhead and increased overall system efficiency, providing substantial cost savings and operational agility. Better security protocols: Through continuous learning, AI models can detect unusual patterns that may signify a security breach, such as ransomware attacks or unauthorized access. Once detected, AI-driven systems can initiate automatic responses to isolate threats and prevent spread, reducing the window of vulnerability. Real-time data processing: By integrating AI into storage systems, your data can be analyzed and processed at the point of storage, reducing latency and accelerating the decision-making process. This is particularly useful in industries like finance and healthcare, where real-time data analysis can provide a competitive advantage and improve patient outcomes. Energy efficiency and sustainability: AI can intelligently manage storage systems’ power consumption based on the workload to reduce unnecessary energy use, allowing you to reduce your carbon footprint and significantly lower your energy costs. Scalability and flexibility: As your business grows and your data needs inevitably evolve, AI-driven storage systems can dynamically scale up or down to meet these demands without service interruptions. AI systems can automatically adjust storage capacity and performance parameters in real time, ensuring your enterprise has the necessary resources whenever they are needed. With the ability to preemptively manage resource allocation, these AI capabilities ensure efficient utilization of storage resources, helping you avoid over-provisioning and underutilizing to optimize both cost and performance. At ProCern, we understand that the efficiency and reliability of your storage solutions can impact everything from your operational agility to your ability to adapt and grow in a competitive market. With Hewlett Packard Enterprise Alletra, powered by AI, we’ll help you establish a future-proof infrastructure that not only adapts to rapid technological changes but also scales to seamlessly meet your growing data needs.
Cyber threats are becoming more sophisticated, more common, and more damaging. In fact, worldwide cybercrime costs are estimated to hit $10.5 trillion annually by 2025.* As companies increasingly operate remotely and transition to cloud-based services, their networks are becoming more dispersed and complex. As a result, standard security measures that were once considered the gold standard of cybersecurity are no longer enough. Unified Secure Access Service Edge (SASE) and Security Service Edge (SSE) solutions have emerged as a powerful alternative to traditional cybersecurity approaches. With the ability to consolidate various security functions, such as secure web gateways, firewalls, and zero-trust network access, into a single framework, these solutions streamline network security and connectivity across increasingly dispersed environments. When combined with Artificial Intelligence (AI), SASE and SSE systems offer an unprecedented ability to dynamically adapt to emerging threats and optimize security protocols in real time, ensuring robust and proactive protection tailored to the unique needs of modern enterprises. Artificial Intelligence is a Game-Changer in Unified SASE and SSE Environments The integration of AI in Unified SASE and SSE solutions marks a significant turning point in the fight against cyber threats. Artificial Intelligence revolutionizes the way security measures are implemented and managed in response to an ever-changing threat landscape, offering the ability to: Enhance detection and response: By using Artificial Intelligence to analyze vast amounts of network data in real-time, SASE and SSE systems can quickly identify potential threats across all network edges simultaneously. Support predictive security posturing: Offering advanced predictive analytics capabilities, AI technologies allow SASE and SSE systems to forecast potential security vulnerabilities and automate the necessary adjustments before a breach occurs. Automate policy management: With the ability to automate access control and risk assessments and continually adapt to user behavior and network configurations, AI brings enhanced intelligence to SASE and SSE systems to dynamically maintain security policies and access controls, securing operations without the need for manual intervention. Detect anomalies more efficiently: AI identifies unusual operations or behaviors indicating a breach or insider threat. As a result, SASE and SSE systems can differentiate between minor and severe breaches, enabling swift and targeted response actions to mitigate potential damage and safeguard sensitive data. Streamline compliance: AI-driven SASE and SSE solutions automate compliance by enforcing regulatory requirements across all access points, reducing manual checks and ensuring continuous adherence to policies. Optimize network performance: AI continuously analyzes network traffic and usage patterns within SASE and SSE frameworks, optimizing data flow and bandwidth allocation to improve performance and user experience Keeping Pace with Evolving Threat Landscape By incorporating AI, SASE and SSE solutions transform traditional security approaches into dynamic, adaptive frameworks that can keep pace with the rapidly evolving threat landscape. At ProCern, we bring advanced expertise in HPE Aruba Networking’s AI-powered SASE and SSE solutions. With the ability to dynamically optimize security protocols and network efficiency, we’ll help you boost your network’s adaptability and threat response with Aruba.
Managing your network isn’t just about dealing with your own equipment; you also need to keep an eye on third-party devices from various manufacturers, each with its own specs and requirements. With different protocols, update schedules, and security settings, these third-party devices introduce complications to network management, increasing the workload for IT teams and the risk of misconfigurations or oversights that could lead to costly downtime or security vulnerabilities.
Where Should AI Workloads Live? AI is everywhere—powering everything from self-checkout machines to advanced data analysis. But where should AI workloads actually run? Some perform best in a centralized data center, while others need to be processed right at the edge. So, what’s the answer? There isn’t one “right” answer—not exactly. Where AI runs depends on latency, bandwidth, security, and computing power. Get it right, and AI delivers speed, efficiency, and cost savings. Get it wrong, and you end up with bottlenecks, unnecessary expenses, and performance issues. The Case for AI at the Edge Some AI workloads can’t afford to wait. When milliseconds matter—like in manufacturing, healthcare, or security applications—AI needs to process data on-site, in real time. Instead of sending everything to a central data center, edge AI processes data where it’s created, reducing delays and keeping operations running even if connectivity is spotty. It also cuts bandwidth costs by limiting the amount of data that needs to be transmitted. That said, while AI at the edge offers lower latency, bandwidth efficiency, and more reliable operations, it comes with trade-offs. Storage, processing power, and security controls are often more constrained than in a traditional data center. Because of these limitations, organizations must be selective about which AI workloads run at the edge and ensure they have the right infrastructure to support them. The Role of the Data Center in AI Processing Not every AI workload belongs at the edge. Training AI models, running large-scale analytics, and handling massive datasets require the kind of high-performance computing that only a data center can provide. A centralized environment offers more processing power, stronger security, and the ability to scale AI workloads as they grow. Beyond raw computing power, data centers provide consistency. AI models rely on vast amounts of data, and a centralized infrastructure ensures better data management, compliance, and long-term storage. With IT teams under pressure to balance efficiency and cost, modern data centers are evolving to handle AI workloads without excessive power consumption or resource waste. But data centers come with challenges of their own. Latency can be an issue for AI that needs instant decision-making, and transmitting large amounts of data back and forth adds bandwidth strain. Most Businesses Need Both For many organizations, it’s not about choosing one or the other—it’s about making them work together. Some AI workloads need immediate processing on-site, while others require the scalability and power of a data center. The key is knowing which tasks belong where and ensuring your AI infrastructure is built to handle both environments seamlessly. The Right Infrastructure for AI—Wherever It Runs Balancing AI workloads across the edge and data center requires the right compute foundation—one that delivers scalability, security, and performance wherever AI runs. Hewlett Packard Enterprise Compute solutions provide the flexibility to support real-time edge processing, high-performance AI training in the data center, and everything in between. But knowing which workloads belong where—and how to deploy them effectively—isn’t always straightforward. ProCern can help you assess your AI infrastructure, determine the best workload placement, and implement the right HPE Compute solutions to ensure AI operates at peak performance—wherever it’s needed most.
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