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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...