Deploying a trained AI model could be the easiest part of the AI lifecycle. After the heavy lifting of data collection, training, and optimization, pushing a model into production is where “the rubber hits the road”, meaning the business expects to see the benefits of invested time and resources. In reality, many AI projects fail in production because of poor performance stemming from suboptimal infrastructure conditions.
There are, broadly speaking, two paths developers can take when deploying inference: DIY, which is time and resource-consuming and requires domain expertise from several teams within the business, or the ever-so-popular “serverless inference” solution. The latter is supposed to simplify the task at hand and deliver productivity, cutting down effort to seconds, not hours. Yet most platforms offering “serverless” AI inference still feel anything but effortless. They require containers, configs, and custom scripts. They bury users in infrastructure decisions. And they often assume your data scientists are also DevOps engineers. It’s a far cry from what serverless was meant to be.
At Gcore, we believe real serverless inference means this: three clicks and ten seconds to deploy a model. That’s not a tagline—it’s the everywhere inference we built. And it’s what infrastructure leaders like Mirantis are now enabling for enterprises through partnerships with Gcore.
Why deployment UX matters more than you think
Serverless inference isn’t just a backend architecture choice. It’s a business enabler, a go-to-market accelerator, an ROI optimizer, a technology democratizer—or, if poorly executed, a blocker.
The reality is that inference workloads are a key point of interface between your AI product or service and the customer. If deployment is clunky, you’re struggling to keep up with demand. If provisioning takes too long, latency spikes, performance is inconsistent, and ultimately your service doesn’t scale. And if the user experience is unclear or inconsistent, customers end up frustrated—or worse, they churn.
Developers and data scientists don’t want to manage infrastructure. They want to bring a model and get results without becoming cloud operators in the process.
Dom Wilde, SVP Marketing, Mirantis
That’s why deployment UX is no longer a nice-to-have. It’s the core of your product.
The benchmark: 3 clicks, 10 seconds
We built Gcore Everywhere Inference to remove every unnecessary step between uploading a model and running it in production. That includes GPU provisioning, routing, scaling, isolation, and endpoint generation, all handled behind the scenes.
The result is what we believe should be the default:
- Upload a model
- Confirm deployment parameters
- Click deploy
And within ten seconds, you’re serving live inference.
For platform teams supporting AI workloads, this isn’t just a better workflow. It’s a transformation.
With Gcore, our customers can deliver not just self-service infrastructure but also inference as a product. End users can deploy models in seconds, and customers don’t have to micromanage the backend to support that.
Dom Wilde, Mirantis
Simple frontend, powerful backend
It’s worth saying: simplifying the frontend doesn’t mean weakening the backend. Gcore’s platform is built for scale and performance, offering the following:
- Multi-tenant GPU isolation
- Smart routing based on location and load
- Auto-scaling based on demand
- A unified API and UI for both automation and accessibility
What makes this meaningful isn’t just the tech, it’s the way it vanishes behind the scenes. With Gcore, Mirantis customers can deliver low-latency inference, maximize GPU efficiency, and meet data privacy requirements without touching low-level infrastructure.
Many enterprises and cloud customers worry about underutilized GPUs. Now, every cycle is optimized. The platform handles the complexity so our customers can focus on building value.
Dom Wilde, Mirantis
If it’s not 3 clicks and 10 seconds, it’s not really serverless
There’s a growing gap between what serverless inference promises and what most platforms deliver. Many cloud providers are focused on raw compute or orchestration, but overlook the deployment layer. That’s a mistake. Because when it comes to customer experience, ease of deployment is the product.
Mirantis saw that early on and partnered with Gcore to bring inference-as-a-service to CSP and enterprise customers, fast. Now, customers can launch new offerings more quickly, reduce operational overhead, and improve the user experience with a simple, elegant deployment path.
Redefine serverless AI with Gcore
If it takes a config file, a container, and a support ticket to deploy a model, it’s not serverless—it’s server-less-ish. With inference at the edge, we make AI deployment as simple as three clicks and ten seconds. And, our model catalog offers a variety of popular models so you can get started right away.
Whether you’re frustrated with slow, inefficient model deployments or looking for the most effective way to start using AI for your company, you need Gcore Everywhere Inference. Give our experts a call to discover how we can simplify your AI so you can focus on scaling and business logic.
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From budget strain to AI gain: Watch how studios are building smarter with AI
Game development is in a pressure cooker. Budgets are ballooning, infrastructure and labor costs are rising, and players expect more complexity and polish with every release. All studios, from the major AAAs to smaller indies, are feeling the strain.But there is a way forward. In a recent webinar, Sean Hammond, Territory Manager for the UK and Nordics at Gcore, explained how AI is reshaping game development workflows and how the right infrastructure strategy can reduce costs, speed up production, and create better player experiences.Scroll on to watch key moments from Sean's talk and explore how studios can make AI work for them.Rising costs are threatening game developmentGame revenue has slowed, but development costs continue to rise. Some AAA titles now surpass $100 million in development budgets. The complexity of modern games demands more powerful servers, scalable infrastructure, and larger teams, making the industry increasingly unsustainable.Personnel and infrastructure costs are also climbing. Developers, artists, and QA testers with specialized skills are in high demand, as are technologies like VR, AR, and AI. Studios are also having to invest more in cybersecurity to protect player data, detect cheating, and safeguard in-game economies.AI is revolutionizing GameDev, even without a perfect use caseWhile the perfect use case for AI in gaming may not have been found yet, it’s already transforming how games are built, tested, and personalized.Sean highlighted emerging applications, including:Smarter QA testingAI-driven player personalizationReal-time motion and animationAccelerated environment and character designMultilingual localizationAdaptive game balancingStudios are already applying these technologies to reduce production timelines and improve immersion.The challenge of secure, scalable AI adoptionOf course, AI adoption doesn’t come without its challenges. Chief among them is security. Public models pose risks: no studio wants their proprietary assets to end up training a competitor’s model.The solution? Deploy AI models on infrastructure you trust so you’re in complete control. That’s where Gcore comes in.Gcore Everywhere Inference reduces compute costs and infrastructure bloat by allowing you to deploy only what you need, where you need it.The future of gaming is AI at scaleTo power real-time player experiences, your studio needs to deploy AI globally, close to your users.Gcore Everywhere Inference lets you deploy models worldwide at the edge with minimal latency because data is not routed back to central servers. This means fast, responsive gameplay and a new generation of real-time, AI-driven features.As a company originally built by gamers, we’ve developed AI solutions with gaming studios in mind. Here’s what we offer:Global edge inference for real-time gameplay: Deploy your AI models close to players worldwide, enabling fast, responsive player experiences without routing data to central servers.Full control over AI model deployment and IP protection: Avoid public APIs and retain full ownership of your assets with on-prem options, preventing your proprietary data from being available to competitors.Scalable, cost-efficient infrastructure tailored to gaming workloads: Deploy only what you need to avoid overprovisioning and reduce compute costs without sacrificing performance.Enhanced player retention through AI-driven personalization and matchmaking: Real-time inference powers smarter NPCs and dynamic matchmaking, improving engagement and keeping players coming back for more.Deploy models in 3 clicks and under 10 seconds: Our developer-friendly platform lets you go from trained model to global deployment in seconds. No complex DevOps setup required.Final takeawayAI is advancing game development fast, but only if it’s deployed right. Gcore offers scalable, secure, and cost-efficient AI infrastructure that helps studios create smarter, faster, and more immersive games.Want to see how it works? Deploy your first model in just a few clicks.Check out our blog on how AI is transforming gaming in 2025
How AI-enhanced content moderation is powering safe and compliant streaming
As streaming experiences a global boom across platforms, regions, and industries, providers face a growing challenge: how to deliver safe, respectful, and compliant content delivery at scale. Viewer expectations have never been higher, likewise the regulatory demands and reputational risks.Live content in particular leaves little room for error. A single offensive comment, inappropriate image, or misinformation segment can cause long-term damage in seconds.Moderation has always been part of the streaming conversation, but tools and strategies are evolving rapidly. AI-powered content moderation is helping providers meet their safety obligations while preserving viewer experience and platform performance.In this article, we explore how AI content moderation works, where it delivers value, and why streaming platforms are adopting it to stay ahead of both audience expectations and regulatory pressures.Real-time problems require real-time solutionsHuman moderators can provide accuracy and context, but they can’t match the scale or speed of modern streaming environments. Live streams often involve thousands of viewers interacting at once, with content being generated every second through audio, video, chat, or on-screen graphics.Manual review systems struggle to keep up with this pace. In some cases, content can go viral before it is flagged, like deepfakes that circulated on Facebook leading up to the 2025 Canadian election. In others, delays in moderation result in regulatory penalties or customer churn, like X’s 2025 fine under the EU Digital Services Act for shortcomings in content moderation and algorithm transparency. This has created a demand for scalable solutions that act instantly, with minimal human intervention.AI-enhanced content moderation platforms address this gap. These systems are trained to identify and filter harmful or non-compliant material as it is being streamed or uploaded. They operate across multiple modalities—video frames, audio tracks, text inputs—and can flag or remove content within milliseconds of detection. The result is a safer environment for end users.How AI moderation systems workModern AI moderation platforms are powered by machine learning algorithms trained on extensive datasets. These datasets include a wide variety of content types, languages, accents, dialects, and contexts. By analyzing this data, the system learns to identify content that violates platform policies or legal regulations.The process typically involves three stages:Input capture: The system monitors live or uploaded content across audio, video, and text layers.Pattern recognition: It uses models to identify offensive content, including nudity, violence, hate speech, misinformation, or abusive language.Contextual decision-making: Based on confidence thresholds and platform rules, the system flags, blocks, or escalates the content for review.This process is continuous and self-improving. As the system receives more inputs and feedback, it adapts to new forms of expression, regional trends, and platform-specific norms.What makes this especially valuable for streaming platforms is its low latency. Content can be flagged and removed in real time, often before viewers even notice. This is critical in high-stakes environments like esports, corporate webinars, or public broadcasts.Multi-language moderation and global streamingStreaming audiences today are truly global. Content crosses borders faster than ever, but moderation standards and cultural norms do not. What’s considered acceptable in one region may be flagged as offensive in another. A word that is considered inappropriate in one language might be completely neutral in another. A piece of nudity in an educational context may be acceptable, while the same image in another setting may not be. Without the ability to understand nuance, AI systems risk either over-filtering or letting harmful content through.That’s why high-quality moderation platforms are designed to incorporate context into their models. This includes:Understanding tone, not just keywordsRecognizing culturally specific gestures or idiomsAdapting to evolving slang or coded languageApplying different standards depending on content type or target audienceThis enables more accurate detection of harmful material and avoids false positives caused by mistranslation.Training AI models for multi-language support involves:Gathering large, representative datasets in each languageTeaching the model to detect content-specific risks (e.g., slurs or threats) in the right cultural contextContinuously updating the model as language evolvesThis capability is especially important for platforms that operate in multiple markets or support user-generated content. It enables a more respectful experience for global audiences while providing consistent enforcement of safety standards.Use cases across the streaming ecosystemAI moderation isn’t just a concern for social platforms. It plays a growing role in nearly every streaming vertical, including the following:Live sports: Real-time content scanning helps block offensive chants, gestures, or pitch-side incidents before they reach a wide audience. Fast filtering protects the viewer experience and helps meet broadcast standards.Esports: With millions of viewers and high emotional stakes, esports platforms rely on AI to remove hate speech and adult content from chat, visuals, and commentary. This creates a more inclusive environment for fans and sponsors alike.Corporate live events: From earnings calls to virtual town halls, organizations use AI moderation to help ensure compliance with internal communication guidelines and protect their reputation.Online learning: EdTech platforms use AI to keep classrooms safe and focused. Moderation helps filter distractions, harassment, and inappropriate material in both live and recorded sessions.On-demand entertainment: Even outside of live broadcasts, moderation helps streaming providers meet content standards and licensing obligations across global markets. It also ensures user-submitted content (like comments or video uploads) meets platform guidelines.In each case, the shared goal is to provide a safe and trusted streaming environment for users, advertisers, and creators.Balancing automation with human oversightAI moderation is a powerful tool, but it shouldn’t be the only one. The best systems combine automation with clear review workflows, configurable thresholds, and human input.False positives and edge cases are inevitable. Giving moderators the ability to review, override, or explain decisions is important for both quality control and user trust.Likewise, giving users a way to appeal moderation decisions or report issues ensures that moderation doesn’t become a black box. Transparency and user empowerment are increasingly seen as part of good platform governance.Looking ahead: what’s next for AI moderationAs streaming becomes more interactive and immersive, moderation will need to evolve. AI systems will be expected to handle not only traditional video and chat, but also spatial audio, avatars, and real-time user inputs in virtual environments.We can also expect increased demand for:Personalization, where viewers can set their own content preferencesIntegration with platform APIs for programmatic content governanceCross-platform consistency to support syndicated content across partnersAs these changes unfold, AI moderation will remain central to the success of modern streaming. Platforms that adopt scalable, adaptive moderation systems now will be better positioned to meet the next generation of content challenges without compromising on speed, safety, or user experience.Keep your streaming content safe and compliant with GcoreGcore Video Streaming offers AI Content Moderation that satisfies today’s digital safety concerns while streamlining the human moderation process.To explore how Gcore AI Content Moderation can transform your digital platform, we invite you to contact our streaming team for a demonstration. Our docs provide guidance for using our intuitive Gcore Customer Portal to manage your streaming content. We also provide a clear pricing comparison so you can assess the value for yourself.Embrace the future of content moderation and deliver a safer, more compliant digital space for all your users.Try AI Content Moderation for free
Deploy GPT-OSS-120B privately on Gcore
OpenAI’s release of GPT-OSS-120B is a turning point for LLM developers. It’s a 120B parameter model trained from scratch, licensed for commercial use, and available with open weights. This is a serious asset for serious builders.Gcore now supports private GPT-OSS-120B deployments via our Everywhere Inference platform. That means you can stand up your own endpoint in minutes, run inference at scale, and control the full stack, without API limits, vendor lock-in, or hidden usage fees. Just fast, secure, controlled deployment on your terms. Deploy now in three clicks or read on to learn more.Why GPT-OSS-120B is big news for buildersThis model changes the game for anyone developing AI apps, platforms, or infrastructure. It brings GPT-3-level reasoning to the open-source ecosystem and frees developers from closed APIs.With GPT-OSS-120B, you get:Full access to model weights and architectureSelf-hosting for maximum data control and privacySupport for fine-tuning and model editingOffline deployment for secure or air-gapped useMassive cost savings at scaleYou can deploy in any Gcore region (or leverage Gcore’s three-click serverless inference on your own infrastructure), route traffic through your own stack, and fully control load, latency, and logs. This is LLM deployment for real-world apps, not just playground prompts.How to deploy GPT-OSS-120B with Gcore Everywhere InferenceGcore Everywhere Inference gives you a clean path from open model to production endpoint. You can spin up a dedicated deployment in just three clicks. We offer configuration options to suit your business needs:Choose your location (cloud or on-prem)Integrate via standard APIs (OpenAI-compatible)Control usage, autoscale, and costsDeploying GPT-OSS-120B on Gcore takes just three clicks in the Gcore Customer Portal.There are no shared endpoints. You get dedicated compute, low-latency routing, and full control and observability.You can also bring your own trained variant if you’ve fine-tuned GPT-OSS-120B elsewhere. We’ll help you host it reliably, close to your users.Use cases: where GPT-OSS-120B fits bestCommercial GPTs still outperform OSS models on some general tasks, but GPT-OSS-120B gives you control, portability, and flexibility where it counts. Most importantly, it gives you the ability to build privacy-sensitive applications.Great fits include:Internal dev tools and copilotsRetrieval-augmented generation (RAG) pipelinesSecure, private enterprise assistantsData-sensitive, on-prem AI workloadsModels requiring full customization or fine-tuningIt’s especially relevant for finance, healthcare, government, and legal teams operating under strict compliance rules.Deploy GPT-OSS-120B todayWant to learn more about GPT-OSS-120B and why Gcore is an ideal provider for deployment? Get all the information you need on our dedicated page.And if you’re ready to deploy in just three clicks, head on over to the Gcore Customer Portal. GPT-OSS-120B is waiting for you in the Application Catalog.Learn more about deploying GPT-OSS-120B on Gcore
Announcing new tools, apps, and regions for your real-world AI use cases
Three updates, one shared goal: helping builders move faster with AI. Our latest releases for Gcore Edge AI bring real-world AI deployments within reach, whether you’re a developer integrating genAI into a workflow, an MLOps team scaling inference workloads, or a business that simply needs access to performant GPUs in the UK.MCP: make AI do moreGcore’s MCP server implementation is now live on GitHub. The Model Context Protocol (MCP) is an open standard, originally developed by Anthropic, that turns AI models into agents that can carry out real-world tasks. It allows you to plug genAI models into everyday tools like Slack, email, Jira, and databases, so your genAI can read, write, and reason directly across systems. Think of it as a way to turn “give me a summary” into “send that summary to the right person and log the action.”“AI needs to be useful, not just impressive. MCP is a critical step toward building AI systems that drive desirable business outcomes, like automating workflows, integrating with enterprise tools, and operating reliably at scale. At Gcore, we’re focused on delivering that kind of production-grade AI through developer-friendly services and top-of-the-range infrastructure that make real-world deployment fast and easy.” — Seva Vayner, Product Director of Edge Cloud and AI, GcoreTo get started, clone the repo, explore the toolsets, and test your own automations.Gcore Application Catalog: inference without overheadWe’ve upgraded the Gcore Model Catalog into something even more powerful: an Application Catalog for AI inference. You can still deploy the latest open models with three clicks. But now, you can also tune, share, and scale them like real applications.We’ve re-architected our inference solution so you can:Run prefill and decode stages in parallelShare KV cache across pods (it’s not tied to individual GPUs) from August 2025Toggle WebUI and secure API independently from August 2025These changes cut down on GPU memory usage, make deployments more flexible, and reduce time to first token, especially at scale. And because everything is application-based, you’ll soon be able to optimize for specific business goals like cost, latency, or throughput.Here’s who benefits:ML engineers can deploy high-throughput workloads without worrying about memory overheadBackend developers get a secure API, no infra setup neededProduct teams can launch demos instantly with the WebUI toggleInnovation labs can move from prototype to production without reconfiguringPlatform engineers get centralized caching and predictable scalingThe new Application Catalog is available now through the Gcore Customer Portal.Chester data center: NVIDIA H200 capacity in the UKGcore’s newest AI cloud region is now live in Chester, UK. This marks our first UK location in partnership with Northern Data. Chester offers 2000 NVIDIA H200 GPUs with BlueField-3 DPUs for secure, high-throughput compute on Gcore GPU Cloud, serving your training and inference workloads. You can reserve your H200 GPU immediately via the Gcore Customer Portal.This launch solves a growing problem: UK-based companies building with AI often face regional capacity shortages, long wait times, or poor performance when routing inference to overseas data centers. Chester fixes that with immediate availability on performant GPUs.Whether you’re training LLMs or deploying inference for UK and European users, Chester offers local capacity, low latency, and impressive capacity and availability.Next stepsExplore the MCP server and start building agentic workflowsTry the new Application Catalog via the Gcore Customer PortalDeploy your workloads in Chester for high-performance UK-based computeDeploy your AI workload in three clicks today!
Gcore recognized as a Leader in the 2025 GigaOm Radar for AI Infrastructure
We’re proud to share that Gcore has been named a Leader in the 2025 GigaOm Radar for AI Infrastructure—the only European provider to earn a top-tier spot. GigaOm’s rigorous evaluation highlights our leadership in platform capability and innovation, and our expertise in delivering secure, scalable AI infrastructure.Inside the GigaOm Radar: what’s behind the Leader statusThe GigaOm Radar report is a respected industry analysis that evaluates top vendors in critical technology spaces. In this year’s edition, GigaOm assessed 14 of the world’s leading AI infrastructure providers, measuring their strengths across key technical and business metrics. It ranks providers based on factors such as scalability and performance, deployment flexibility, security and compliance, and interoperability.Alongside the ranking, the report offers valuable insights into the evolving AI infrastructure landscape, including the rise of hybrid AI architectures, advances in accelerated computing, and the increasing adoption of edge deployment to bring AI closer to where data is generated. It also offers strategic takeaways for organizations seeking to build scalable, secure, and sovereign AI capabilities.Why was Gcore named a top provider?The specific areas in which Gcore stood out and earned its Leader status are as follows:A comprehensive AI platform offering Everywhere Inference and GPU Cloud solutions that support scalable AI from model development to productionHigh performance powered by state-of-the-art NVIDIA A100, H100, H200 and GB200 GPUs and a global private network ensuring ultra-low latencyAn extensive model catalogue with flexible deployment options across cloud, on-premises, hybrid, and edge environments, enabling tailored global AI solutionsExtensive capacity of cutting-edge GPUs and technical support in Europe, supporting European sovereign AI initiativesChoosing Gcore AI is a strategic move for organizations prioritizing ultra-low latency, high performance, and flexible deployment options across cloud, on-premises, hybrid, and edge environments. Gcore’s global private network ensures low-latency processing for real-time AI applications, which is a key advantage for businesses with a global footprint.GigaOm Radar, 2025Discover more about the AI infrastructure landscapeAt Gcore, we’re dedicated to driving innovation in AI infrastructure. GPU Cloud and Everywhere Inference empower organizations to deploy AI efficiently and securely, on their terms.If you’re planning your AI infrastructure roadmap or rethinking your current one, this report is a must-read. Explore the report to discover how Gcore can support high-performance AI at scale and help you stay ahead in an AI-driven world.Download the full report
Protecting networks at scale with AI security strategies
Network cyberattacks are no longer isolated incidents. They are a constant, relentless assault on network infrastructure, probing for vulnerabilities in routing, session handling, and authentication flows. With AI at their disposal, threat actors can move faster than ever, shifting tactics mid-attack to bypass static defenses.Legacy systems, designed for simpler threats, cannot keep pace. Modern network security demands a new approach, combining real-time visibility, automated response, AI-driven adaptation, and decentralized protection to secure critical infrastructure without sacrificing speed or availability.At Gcore, we believe security must move as fast as your network does. So, in this article, we explore how L3/L4 network security is evolving to meet new network security challenges and how AI strengthens defenses against today’s most advanced threats.Smarter threat detection across complex network layersModern threats blend into legitimate traffic, using encrypted command-and-control, slow drip API abuse, and DNS tunneling to evade detection. Attackers increasingly embed credential stuffing into regular login activity. Without deep flow analysis, these attempts bypass simple rate limits and avoid triggering alerts until major breaches occur.Effective network defense today means inspection at Layer 3 and Layer 4, looking at:Traffic flow metadata (NetFlow, sFlow)SSL/TLS handshake anomaliesDNS request irregularitiesUnexpected session persistence behaviorsGcore Edge Security applies real-time traffic inspection across multiple layers, correlating flows and behaviors across routers, load balancers, proxies, and cloud edges. Even slight anomalies in NetFlow exports or unexpected east-west traffic inside a VPC can trigger early threat alerts.By combining packet metadata analysis, flow telemetry, and historical modeling, Gcore helps organizations detect stealth attacks long before traditional security controls react.Automated response to contain threats at network speedDetection is only half the battle. Once an anomaly is identified, defenders must act within seconds to prevent damage.Real-world example: DNS amplification attackIf a volumetric DNS amplification attack begins saturating a branch office's upstream link, automated systems can:Apply ACL-based rate limits at the nearest edge routerFilter malicious traffic upstream before WAN degradationAlert teams for manual inspection if thresholds escalateSimilarly, if lateral movement is detected inside a cloud deployment, dynamic firewall policies can isolate affected subnets before attackers pivot deeper.Gcore’s network automation frameworks integrate real-time AI decision-making with response workflows, enabling selective throttling, forced reauthentication, or local isolation—without disrupting legitimate users. Automation means threats are contained quickly, minimizing impact without crippling operations.Hardening DDoS mitigation against evolving attack patternsDDoS attacks have moved beyond basic volumetric floods. Today, attackers combine multiple tactics in coordinated strikes. Common attack vectors in modern DDoS include the following:UDP floods targeting bandwidth exhaustionSSL handshake floods overwhelming load balancersHTTP floods simulating legitimate browser sessionsAdaptive multi-vector shifts changing methods mid-attackReal-world case study: ISP under hybrid DDoS attackIn recent years, ISPs and large enterprises have faced hybrid DDoS attacks blending hundreds of gigabits per second of L3/4 UDP flood traffic with targeted SSL handshake floods. Attackers shift vectors dynamically to bypass static defenses and overwhelm infrastructure at multiple layers simultaneously. Static defenses fail in such cases because attackers change vectors every few minutes.Building resilient networks through self-healing capabilitiesEven the best defenses can be breached. When that happens, resilient networks must recover automatically to maintain uptime.If BGP route flapping is detected on a peering session, self-healing networks can:Suppress unstable prefixesReroute traffic through backup transit providersPrevent packet loss and service degradation without manual interventionSimilarly, if a VPN concentrator faces resource exhaustion from targeted attack traffic, automated scaling can:Spin up additional concentratorsRedistribute tunnel sessions dynamicallyMaintain stable access for remote usersGcore’s infrastructure supports self-healing capabilities by combining telemetry analysis, automated failover, and rapid resource scaling across core and edge networks. This resilience prevents localized incidents from escalating into major outages.Securing the edge against decentralized threatsThe network perimeter is now everywhere. Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network
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