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2019 Prediction: Enterprises Will Use AI to Replace VPNs with Micro-Perimeters to Optimize Hybrid Cloud Application Performance

Don Boxley

Many enterprises are pursuing a hybrid IT strategy involving integrated on-premises systems and off-premises cloud/hosted resources. This pursuit will create application performance issues stemming from one key area: leveraging the public internet.

For enterprises the public Internet is both a boon and a danger. The public Internet's global reach offers an easy and cost-effective means for engaging large numbers of customers, regardless of location. However, using the public internet to connect users with business-critical workloads brings risks.

Businesses survive on speed. Customers don't like to wait, and each moment waiting has real revenue implications. Companies investing heavily in hybrid IT strategy around enterprise applications are making these investments to gain an edge, but these investments will only deliver a positive return if the applications are able to run at maximum performance allowed.

As an access path to the cloud, the performance of the public Internet can be limited by traffic and throughput impediments, which can impact the effectiveness of workloads right at peak load times. If enterprise applications struggle to deal with peak loads, this can result in the business suffering revenue loss, damage to their reputation and failing to meet the objectives of moving to a hybrid cloud strategy.

This performance issue can become even more severe as an organization seeks to improve network security by adding secure connectivity in order to reduce security exposure via the public internet by using traditional VPNs, which can cut throughput in half. But traditional VPN software solutions are obsolete for the new IT reality of hybrid and multi-cloud. They weren't designed for them. They're complex to configure, not performant, and they give users a "slice of the network," creating a lateral network attack surface.

A new class of purpose-built security software is emerging to eliminate these issues and disrupt the cloud VPN market. This new security software will enable organizations to build lightweight dynamic micro-perimeters to secure their application- and workload-centric connections between on-premises and cloud/hosted environments, with virtually no attack surface and without the performance issues of VPNs.

Because of the ease of use this new class of security software organizations will utilize at 1-2-3-100+ deployment strategy. That is, they'll deploy micro-perimeters for workload #1. Satisfied it meets the performance and security requirements, they'll deploy micro-perimeters for workload #2, and then deploy for workload #3. At that point, the organization will require micro-perimeters for every application, which could be 100s of workloads with thousands of users. This is the point organizations will turn to artificial intelligence (AI). This is where organizations will leverage their learnings in artificial intelligence to find products that can automate, manage and simplify the machine learning (ML) for each enterprise application's unique connectivity network to map out the optimal deployment of micro-perimeters. This deployment plan will enable organizations to aggressively implement micro-perimeters with the eventual goal of the AI engine deploying and updating micro-perimeters automatically.

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2019 Prediction: Enterprises Will Use AI to Replace VPNs with Micro-Perimeters to Optimize Hybrid Cloud Application Performance

Don Boxley

Many enterprises are pursuing a hybrid IT strategy involving integrated on-premises systems and off-premises cloud/hosted resources. This pursuit will create application performance issues stemming from one key area: leveraging the public internet.

For enterprises the public Internet is both a boon and a danger. The public Internet's global reach offers an easy and cost-effective means for engaging large numbers of customers, regardless of location. However, using the public internet to connect users with business-critical workloads brings risks.

Businesses survive on speed. Customers don't like to wait, and each moment waiting has real revenue implications. Companies investing heavily in hybrid IT strategy around enterprise applications are making these investments to gain an edge, but these investments will only deliver a positive return if the applications are able to run at maximum performance allowed.

As an access path to the cloud, the performance of the public Internet can be limited by traffic and throughput impediments, which can impact the effectiveness of workloads right at peak load times. If enterprise applications struggle to deal with peak loads, this can result in the business suffering revenue loss, damage to their reputation and failing to meet the objectives of moving to a hybrid cloud strategy.

This performance issue can become even more severe as an organization seeks to improve network security by adding secure connectivity in order to reduce security exposure via the public internet by using traditional VPNs, which can cut throughput in half. But traditional VPN software solutions are obsolete for the new IT reality of hybrid and multi-cloud. They weren't designed for them. They're complex to configure, not performant, and they give users a "slice of the network," creating a lateral network attack surface.

A new class of purpose-built security software is emerging to eliminate these issues and disrupt the cloud VPN market. This new security software will enable organizations to build lightweight dynamic micro-perimeters to secure their application- and workload-centric connections between on-premises and cloud/hosted environments, with virtually no attack surface and without the performance issues of VPNs.

Because of the ease of use this new class of security software organizations will utilize at 1-2-3-100+ deployment strategy. That is, they'll deploy micro-perimeters for workload #1. Satisfied it meets the performance and security requirements, they'll deploy micro-perimeters for workload #2, and then deploy for workload #3. At that point, the organization will require micro-perimeters for every application, which could be 100s of workloads with thousands of users. This is the point organizations will turn to artificial intelligence (AI). This is where organizations will leverage their learnings in artificial intelligence to find products that can automate, manage and simplify the machine learning (ML) for each enterprise application's unique connectivity network to map out the optimal deployment of micro-perimeters. This deployment plan will enable organizations to aggressively implement micro-perimeters with the eventual goal of the AI engine deploying and updating micro-perimeters automatically.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.