Seamless, secure, and predictive: how AI-built platforms will rewire multicloud networking by 2026

The next wave of multicloud: from stitched clouds to a unified AI fabric

By 2026, multicloud networking will no longer be about just connecting AWS, Azure, Google Cloud, and private data centers with a maze of VPNs, SD-WAN tunnels, and security appliances. It will increasingly be orchestrated by AI-native Platform-as-a-Service (PaaS) layers that treat the network as programmable, observable, and continuously optimized fabric. These platforms will not simply run AI workloads; they will be built with AI at their core to design, deploy, secure, and tune the multicloud network itself.

Analyst firms already describe “AI-native development platforms” as a top strategic trend for 2026, predicting that generative AI will fundamentally reshape how software and digital infrastructure are built and operated. Gartner forecasts that AI-native platforms will allow small, highly skilled platform teams to support much larger portfolios of applications by pairing engineers with AI copilots and guardrailed automation. Network World As multicloud networking grows more complex, those same AI-native principles are now being applied directly to the network and connectivity layer.

This article looks at how AI-native PaaS platforms will transform multicloud networking by 2026, why this matters to IT and security leaders, and what a next-generation, AI-driven network stack will look like in practice. It is the first in a six-part series examining the most important PaaS technologies reshaping cloud and multicloud strategies: AI-native development platforms, confidential computing, multi-agent systems, domain-specific AI models, AI-driven preemptive cybersecurity, and deeper hybrid and multicloud integration.

From cloud-native to AI-native: what changes for the network

Cloud-native architectures gave enterprises containers, Kubernetes, microservices, and service meshes. They made it easier to design highly distributed systems, but they also shifted massive complexity onto networking and security teams. Policy-based routing, east–west traffic encryption, distributed firewalls, and API security all became part of the everyday multicloud vocabulary.

AI-native platforms go a step further by embedding generative AI, multi-agent orchestration, and advanced analytics into the development and operations toolchain. Instead of a human-only team writing YAML, Terraform, and network policies manually, AI-native PaaS layers bring in copilots that understand topology, intent, and business policy. They can generate network configurations, verify them against security baselines, simulate outcomes, and continuously adjust routing and access rules as conditions change.

Analysts note that these AI-native platforms are not just “AI features” bolted onto existing cloud consoles. They’re being designed so that AI plays a central role across the lifecycle: design, build, test, deploy, secure, and operate. Network World For multicloud networking, that means the platform can learn from real traffic, performance, and security telemetry, and then propose or implement changes automatically.

Why multicloud networking needs AI-native PaaS

Multicloud is now the default strategy rather than an exception. Flexera’s 2024 State of the Cloud data, cited in a TierPoint analysis, shows that 89 percent of organizations run multicloud, with 73 percent using hybrid models that mix public cloud and on-premises infrastructure. TierPoint, LLC Gartner separately predicts that 90 percent of organizations will adopt hybrid cloud approaches by 2027, with spending on cloud infrastructure and platform services rising sharply as enterprises consolidate onto integrated cloud application platforms. Gartner

That level of adoption brings three hard problems for networking teams.

First, policy fragmentation explodes. Each cloud has its own security groups, routing constructs, load balancers, and identity models. Stitching them together consistently, and verifying that they conform to zero-trust principles, requires enormous design and review effort.

Second, observability becomes noisy and disconnected. Logs, flow records, API metrics, and security alerts are scattered across tools and cloud providers. Without a unified, AI-assisted view, subtle performance regressions or slow-burning attacks can go unnoticed.

Third, change velocity is relentless. Application teams are spinning up new services, regions, and data products on a weekly or even daily basis. Keeping the network aligned with this rate of change—while preserving compliance and cost control—pushes traditional, ticket-driven operations to the breaking point.

AI-native PaaS platforms promise to address these pain points by acting as a “network brain” for the multicloud environment. They consume topology data, policies, and telemetry from across clouds; encode business intent (for example, “payments workloads must never traverse the public internet”); and then generate, deploy, and adjust network configurations automatically. Over time, AI models learn which policies yield the best outcomes across performance, security, cost, and compliance.

Inside an AI-native multicloud networking stack

By 2026, an AI-native PaaS designed for multicloud networking will typically contain several tightly integrated layers.

At the top sits an intent-and-design layer. This is where architects and NetOps teams describe the target state of the network in human language: which regions must connect, which data domains must remain isolated, which partners require access, and what service-level objectives must be met. Generative AI copilots guide users as they express this intent, translating natural language into declarative policies and high-level topology blueprints.

Below that is a policy and compliance engine that transforms intent into specific cloud constructs: VPCs and VNets, peering relationships, private links, transit gateways, SD-WAN overlays, and service mesh policies. This engine uses a mix of rules-based logic and reinforcement learning to evaluate trade-offs among performance, cost, and regulatory requirements. It can simulate how changes will ripple across the multicloud fabric before they are deployed.

The deployment and automation layer integrates with infrastructure-as-code repositories, CI/CD pipelines, and native cloud APIs. Here, AI-native capabilities appear as code-generation and validation features. The platform can propose Terraform or CloudFormation updates, check them against a library of best practices and past incidents, and then push them into controlled rollout pipelines.

Finally, the observability and feedback layer ingests metrics, logs, traces, and security events from every cloud and edge location. Rather than simply visualizing data, an AI-native platform correlates patterns across domains—linking packet drops, DNS anomalies, and IAM changes into coherent narratives. This telemetry continuously trains models that forecast capacity, predict bottlenecks, and identify misconfigurations.

Gartner’s forecasts for cloud application platforms show strong growth in integrated IaaS and PaaS offerings (CIPS), driven in part by the need to unify development and operations across hybrid and multicloud estates. Gartner AI-native networking capabilities are increasingly being bundled into these platforms, rather than sold as standalone point products.

Use cases: from cloud on-ramps to sovereign edge architectures

As AI-native PaaS capabilities mature, multicloud networking teams are experimenting with several high-value use cases.

One early pattern is the automated cloud on-ramp. Enterprises that open new markets or launch new SaaS regions often face weeks of manual work to design secure connectivity between a new cloud region, existing data centers, and global partners. In an AI-native world, architects describe the area, latency constraints, and regulatory boundaries. The platform generates a baseline topology, proposes encrypted paths, configures DNS and routing, and sets up monitoring—often in hours rather than weeks.

Another fast-emerging scenario is sovereign and regulated edge connectivity. Financial services, government agencies, and healthcare providers increasingly rely on multicloud services but must ensure that sensitive workloads remain within specific jurisdictions and enclaves. Confidential computing—where workloads run inside hardware-backed trusted execution environments—is gaining traction as a way to protect data in use across untrusted infrastructure. Network World AI-native platforms link those enclaves with policy-aware networking, automatically steering sensitive traffic through routes and environments that meet sovereignty and confidentiality requirements.

A third use case combines multicloud networking with domain-specific AI models. Cloud providers and independent vendors are pushing industry-specific generative AI models for sectors such as manufacturing, healthcare, and financial services. Gartner When these domain-focused models are deployed across regions and providers, the underlying network must be optimized for low-latency model access and compliant data flows. AI-native PaaS layers can learn the traffic patterns of each domain model, then adjust routing and caching strategies to deliver predictable response times while keeping sensitive data localized.

Security by design: preemptive, AI-driven cyber defense for multicloud

Security is where AI-native networking may have the most visible impact. Traditional security models often rely on post-facto analysis: logs are collected, signatures updated, and policies adjusted after incidents occur. As adversaries automate and weaponize AI, that reactive model is no longer sufficient.

Surveys from the Cloud Security Alliance show that a strong majority of security professionals—63 percent—believe AI can substantially improve detection and response capabilities, even as many worry that attackers will also benefit from AI. Cloud Security Alliance That ambivalence is pushing organizations toward architectures that embed AI into security controls from the start, particularly for cloud and multicloud environments.

Gartner’s technology trend analysis describes “preemptive cybersecurity” as a fast-growing approach: by 2030, preemptive solutions could account for half of security spending, up from less than 5 percent in 2024. Network World For multicloud networking, preemptive security will be delivered through AI-native PaaS layers that continuously inspect traffic flows, configuration drift, and identity relationships.

These platforms don’t just raise alerts; they propose or implement remediations in real time. When an AI model detects a suspicious east–west traffic spike between two cloud regions, the platform can tighten microsegmentation rules, re-route traffic through deeper inspection points, or enforce step-up authentication for affected services. Zero-trust principles—“never trust, always verify”—are operationalized by AI agents that analyze context at line speed.

As organizations deploy confidential computing to protect AI workloads and sensitive data in use, AI-native network platforms also play a role in enforcing isolation. They can ensure that connections into and out of confidential enclaves adhere to strict cryptographic and policy requirements, bridging the gap between application security and network posture. anjuna.io

Market dynamics: hyperscalers, security giants, and neutral platforms

The race to deliver AI-native multicloud networking is drawing in hyperscalers, security vendors, and neutral edge and connectivity providers.

Hyperscale cloud platforms are expanding their cloud application platform offerings with deeper PaaS capabilities, integrated AI services, and cross-cloud frameworks. Gartner’s analysis of public cloud spending notes that cloud infrastructure and platform services are converging into full-featured platforms that support distributed, hybrid, and multicloud environments with cross-cloud integration. Gartner: These vendors are embedding AI copilots directly into network and security consoles, turning multicloud design and troubleshooting into prompt-driven workflows.

Security companies, particularly those specializing in cloud-native application protection, zero-trust networking, and secure access service edge, are also repositioning themselves as AI-native networking platforms. Many are integrating AI agents trained on massive corpora of security incidents so they can autonomously propose network policy changes, quarantine risky assets, or adjust access rules across multiple clouds.

Meanwhile, neutral platforms—content delivery networks, edge compute providers, and cloud-agnostic networking services—see an opportunity to become the “dial tone” for AI-native multicloud connectivity. Their value proposition is to abstract away the proprietary differences of hyperscaler networks and expose programmable, AI-enhanced networking and security services that span vendors.

In this competitive environment, enterprises will need to balance the benefits of deep integration with a single ecosystem against the risk of lock-in. AI-native PaaS platforms that preserve interoperability, expose open APIs, and support third-party AI models will likely be favored by organizations with strong multicloud and data-sovereignty mandates.

Challenges: governance, skills, and the risk of over-automation

As attractive as AI-native multicloud networking sounds, it comes with real risks and execution challenges.

Governance is at the top of the list. If AI agents can propose and push changes to routing tables, firewall rules, or identity policies, organizations must define clear guardrails and approval workflows. Survey data from the Cloud Security Alliance suggests that 74 percent of organizations plan to create dedicated teams to govern secure AI use, underscoring how critical governance is as AI moves into core infrastructure. Cloud Security Alliance The same rigor must apply when AI tools control the network fabric itself.

Skills and culture are another stumbling block. Network engineers, cloud architects, and security teams will need to become comfortable working alongside AI copilots that generate configurations and remediation plans. Rather than handing over control entirely, teams will need to cultivate a “human in the loop” mindset—treating AI as a powerful assistant that still requires oversight and validation.

Over-automation poses its own danger. If organizations allow AI systems to make sweeping network changes based on incomplete or noisy telemetry, they risk introducing new failure modes or cascading outages. The most successful adopters will be those who start with constrained, well-observed use cases, gradually expanding AI autonomy as confidence and monitoring improve.

Finally, there is the question of trust and transparency. As AI-native platforms become the brain of the multicloud network, stakeholders—from regulators to internal auditors—will demand explanations for why specific network paths were chosen, why certain connections were blocked, or why traffic was re-routed through particular jurisdictions. That means explainable AI and robust audit trails will be essential features of any credible AI-native PaaS offering.

Closing thoughts and looking forward

Multicloud networking in 2026 will be shaped less by the raw mechanics of tunnels and transit gateways and more by the intelligence of the platforms orchestrating them. AI-native PaaS layers are emerging as the control plane that unifies design, deployment, security, and optimization across clouds, edge locations, and confidential enclaves.

In this first article of the series, we explored how AI-native development platforms will transform multicloud networking from a reactive, ticket-driven discipline into an intent-based, AI-assisted practice. These platforms promise to reduce toil, accelerate secure connectivity across new regions and workloads, and enable preemptive security decisions that keep pace with the speed of cloud-native development.

The story, however, is bigger than AI-native platforms alone. Over the coming years, confidential computing will harden data-in-use across untrusted infrastructure, multi-agent systems will orchestrate complex cross-cloud workflows, domain-specific AI models will demand specialized network paths, AI-driven cybersecurity will move protection from reactive to anticipatory, and deeper hybrid and multicloud integration will turn today’s patchwork into a more cohesive fabric.

For CIOs, CISOs, and NetOps leaders, the imperative is clear. Now is the time to experiment with AI-native tooling in contained domains, build governance practices, and modernize network observability so you can trust the insights your AI systems act on. Those who get this right will not only tame multicloud complexity but turn their network into a strategic asset—adaptive, predictive, and tightly aligned with business goals.

Reference sites

AI dominates Gartner’s top strategic technology trends for 2026 – Network World – https://www.networkworld.com/article/4076316/ai-dominates-gartners-top-strategic-technology-trends-for-2026.html

Gartner Forecasts Worldwide Public Cloud End-User Spending to Total $723 Billion in 2025 – Gartner – https://www.gartner.com/en/newsroom/press-releases/2024-11-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-total-723-billion-dollars-in-2025

Embracing AI in Cybersecurity: 6 Key Insights from CSA’s 2024 State of AI and Security Survey Report – Cloud Security Alliance – https://cloudsecurityalliance.org/articles/embracing-ai-in-cybersecurity-6-key-insights-from-csa-s-2024-state-of-ai-and-security-survey-report

Confidential Computing Wrapped: Your Industry Update As We Enter 2025 – Anjuna – https://www.anjuna.io/blog/confidential-computing-wrapped-your-industry-update-as-we-enter-2025

The Future of Hybrid Cloud Adoption: Expert Insights for 2025 – TierPoint – https://www.tierpoint.com/blog/hybrid-cloud-adoption/

Benoit Tremblay, Author, IT Security Management, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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