Skip to main content

Juniper Networks Introduces Cloud-Native Access Assurance Service Driven by Mist AI

Juniper Networks announced the latest innovation to its AI-driven enterprise portfolio, the Juniper Mist® Access Assurance service.

This new service leverages Mist AI and a modern microservices cloud to provide a full suite of network access control (NAC) and policy management functions via the same flexible and simple framework already included in Juniper’s wired access, wireless access, indoor location, SD-WAN and secure client-to-cloud portfolio. The result is unsurpassed automation, insight and assurance for superior access control and policy enforcement, coupled with exceptional operational savings.

“Network administrators often fear their own legacy NAC solutions because they are built using on-premises overlay hardware, which makes them brittle, complex to deploy and operate, and inherently lacking in both scale and resiliency,” said Sudheer Matta, Group VP of Products at Juniper Networks. “The new Juniper Mist Access Assurance service solves this by natively integrating NAC into the network infrastructure, and by leveraging AIOps and a cloud-native architecture to seamlessly set up and operate access management, policy creation and enforcement from anywhere via a familiar interface. With our groundbreaking Mist AI and microservices cloud, Juniper customers and partners get exceptional client level insights and automation, deployment times that are reduced from months to minutes and cost-effective resiliency and scale. By integrating Mist AI with NAC, Juniper is further expanding the value of the AI-driven enterprise portfolio, while bringing much needed modernization to a key part of the network.”

Features of the new Juniper Mist Access Assurance service include:

- Granular security: Juniper Mist Access Assurance is natively integrated into the network, as opposed to traditional overlay NAC systems. It provides secure user-, role- and device-based network access aligned with Zero Trust principles enforced for guest, IoT, BYOD and corporate-managed devices. Juniper Mist Access Assurance provides granular network admission controls leveraging the secure 802.1X standard, looking at user/device identity, role and location context to allow for identity-based micro-segmentation and policy assignment.

- AI-driven automation: The Juniper solution provides automated zero-touch network access with fast, one-click device provisioning. In addition, it is 100% programmable, using open APIs for full automation and seamless integration with Juniper Secure Analytics (JSA) and other SIEM or ITSM systems and identity providers for configuration, authentication and real-time data extraction. The Juniper Mist Access Assurance service, like the rest of the Juniper AI-driven enterprise portfolio, is powered by Mist AI and the Marvis Virtual Network Assistant for proactive troubleshooting and self-driving operations to save time and money.

- Cloud-native simplicity, resiliency and scale: Juniper Mist Access Assurance greatly simplifies the administrator experience by combining cloud-based deployment, easy-to-use workflows, simplified policies, API-based automation and troubleshooting for connected devices. A unique microservices-based cloud architecture ensures support for one to tens of thousands of sites, users and devices with exceptional ease and no planned downtime. The service also provides geo-affinity to users for reliable and low-latency access control, which eradicates the need for complex network designs that require multiple on-premises NAC devices and/or load balancers across many data centers. Further, the Juniper® Cloud provides elastic scale, ensuring cost effective growth in conjunction with customer demand.

- Client-to-cloud assurance: Juniper Mist provides a unified cloud-hosted management experience for IT operations across the full network stack, including SD-WAN, SSE, wired and wireless access. By leveraging a common cloud and Mist AI engine, Juniper delivers automation and insight for superior end-to-end user experiences. This includes, for example, unified event correlation, anomaly detection, prescriptive actions and even self-driving network operations.

The Juniper Mist Access Assurance service works in conjunction with other Juniper Mist Cloud and Connected Security services, including Wired Assurance, Wi-Fi Assurance and Marvis™, the industry’s only virtual network assistant driven by Mist AI, as well as Juniper Connected Security services. Marvis combines 7th-generation data science with extensive network (wired/wireless/WAN), location and security domain expertise and a conversational interface to provide exceptional visibility into user experiences and proactive automation to detect and solve problems before users know they exist. Today, Juniper announced new enhancements to Marvis that include LLM (Large Language Models) and support for Zoom, making it even easier to deliver predictable, reliable and measurable user experiences from client to cloud.

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Juniper Networks Introduces Cloud-Native Access Assurance Service Driven by Mist AI

Juniper Networks announced the latest innovation to its AI-driven enterprise portfolio, the Juniper Mist® Access Assurance service.

This new service leverages Mist AI and a modern microservices cloud to provide a full suite of network access control (NAC) and policy management functions via the same flexible and simple framework already included in Juniper’s wired access, wireless access, indoor location, SD-WAN and secure client-to-cloud portfolio. The result is unsurpassed automation, insight and assurance for superior access control and policy enforcement, coupled with exceptional operational savings.

“Network administrators often fear their own legacy NAC solutions because they are built using on-premises overlay hardware, which makes them brittle, complex to deploy and operate, and inherently lacking in both scale and resiliency,” said Sudheer Matta, Group VP of Products at Juniper Networks. “The new Juniper Mist Access Assurance service solves this by natively integrating NAC into the network infrastructure, and by leveraging AIOps and a cloud-native architecture to seamlessly set up and operate access management, policy creation and enforcement from anywhere via a familiar interface. With our groundbreaking Mist AI and microservices cloud, Juniper customers and partners get exceptional client level insights and automation, deployment times that are reduced from months to minutes and cost-effective resiliency and scale. By integrating Mist AI with NAC, Juniper is further expanding the value of the AI-driven enterprise portfolio, while bringing much needed modernization to a key part of the network.”

Features of the new Juniper Mist Access Assurance service include:

- Granular security: Juniper Mist Access Assurance is natively integrated into the network, as opposed to traditional overlay NAC systems. It provides secure user-, role- and device-based network access aligned with Zero Trust principles enforced for guest, IoT, BYOD and corporate-managed devices. Juniper Mist Access Assurance provides granular network admission controls leveraging the secure 802.1X standard, looking at user/device identity, role and location context to allow for identity-based micro-segmentation and policy assignment.

- AI-driven automation: The Juniper solution provides automated zero-touch network access with fast, one-click device provisioning. In addition, it is 100% programmable, using open APIs for full automation and seamless integration with Juniper Secure Analytics (JSA) and other SIEM or ITSM systems and identity providers for configuration, authentication and real-time data extraction. The Juniper Mist Access Assurance service, like the rest of the Juniper AI-driven enterprise portfolio, is powered by Mist AI and the Marvis Virtual Network Assistant for proactive troubleshooting and self-driving operations to save time and money.

- Cloud-native simplicity, resiliency and scale: Juniper Mist Access Assurance greatly simplifies the administrator experience by combining cloud-based deployment, easy-to-use workflows, simplified policies, API-based automation and troubleshooting for connected devices. A unique microservices-based cloud architecture ensures support for one to tens of thousands of sites, users and devices with exceptional ease and no planned downtime. The service also provides geo-affinity to users for reliable and low-latency access control, which eradicates the need for complex network designs that require multiple on-premises NAC devices and/or load balancers across many data centers. Further, the Juniper® Cloud provides elastic scale, ensuring cost effective growth in conjunction with customer demand.

- Client-to-cloud assurance: Juniper Mist provides a unified cloud-hosted management experience for IT operations across the full network stack, including SD-WAN, SSE, wired and wireless access. By leveraging a common cloud and Mist AI engine, Juniper delivers automation and insight for superior end-to-end user experiences. This includes, for example, unified event correlation, anomaly detection, prescriptive actions and even self-driving network operations.

The Juniper Mist Access Assurance service works in conjunction with other Juniper Mist Cloud and Connected Security services, including Wired Assurance, Wi-Fi Assurance and Marvis™, the industry’s only virtual network assistant driven by Mist AI, as well as Juniper Connected Security services. Marvis combines 7th-generation data science with extensive network (wired/wireless/WAN), location and security domain expertise and a conversational interface to provide exceptional visibility into user experiences and proactive automation to detect and solve problems before users know they exist. Today, Juniper announced new enhancements to Marvis that include LLM (Large Language Models) and support for Zoom, making it even easier to deliver predictable, reliable and measurable user experiences from client to cloud.

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...