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Ivanti Neurons Platform Introduced

Ivanti unveiled Ivanti Neurons™, a new hyper-automation platform that empowers organizations to proactively, predictably and autonomously self-heal and self-secure devices, and self-service end users.

Ivanti Neurons augments IT teams with automation bots that detect and resolve issues and security vulnerabilities while improving the accuracy, speed and costs of services IT delivers.

Early adopters of the solution have reduced unplanned outages up to 63%, reduced time to deploy security updates by 88%, and resolved up to 80% of endpoint issues before users reported them.*

With this release, Ivanti is delivering on its vision to address the rapid growth and complexity of devices, data, multi-generational remote workforce, and increasing cyber-security threats with hyper-automation. By helping organizations mature from basic automation to a confluence of hyper-automation powered by deep learning capabilities, Ivanti is delivering a self-healing autonomous edge with contextual, anticipatory and personalized experiences for remote workers.

“As remote becomes the next normal, Ivanti Neurons enables organizations to heal and secure devices and deliver a seamless ‘work from anywhere’ employee experience,” said Nayaki Nayyar, EVP and CPO, Ivanti. “Always on and always working, Ivanti Neurons enables IT’s desire to ‘shift-left’ with automation bots that autonomously discover, secure and service endpoints at the edge.”

The new Ivanti Neurons hyper-automation platform offers multiple capabilities for enterprises:

- Ivanti Neurons for Edge Intelligence gives IT the ability to query all edge devices using natural language (NLP) and get real-time intelligence across the enterprise in seconds. It provides quick operational awareness, real-time inventory, and security configurations across the edge leveraging sensor-based architecture.

- Ivanti Neurons for Healing offers an army of automation bots to proactively detect, diagnose, and auto-remediate configuration drift issues, performance issues, compliance issues, and security issues for endpoints. Automation of routine tasks paves the way to creating a truly self-healing environment, reducing time, costs, and improving employee experience.

- Ivanti Neurons for Discovery delivers accurate and actionable asset information in minutes. This provides visibility in real-time using active and passive scanning and third-party connectors. These provide normalized hardware and software inventory data, software usage information and actionable insights to efficiently feed configuration management and asset management databases.

- Ivanti Neurons Workspace provides a 360-degree view of devices, users, applications, and services, with real-time data. This allows first-line analysts to resolve issues previously escalated to specialists. User and device views cut complexity, long wait times and high escalation costs, resulting in faster end user resolution and greater productivity.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Ivanti Neurons Platform Introduced

Ivanti unveiled Ivanti Neurons™, a new hyper-automation platform that empowers organizations to proactively, predictably and autonomously self-heal and self-secure devices, and self-service end users.

Ivanti Neurons augments IT teams with automation bots that detect and resolve issues and security vulnerabilities while improving the accuracy, speed and costs of services IT delivers.

Early adopters of the solution have reduced unplanned outages up to 63%, reduced time to deploy security updates by 88%, and resolved up to 80% of endpoint issues before users reported them.*

With this release, Ivanti is delivering on its vision to address the rapid growth and complexity of devices, data, multi-generational remote workforce, and increasing cyber-security threats with hyper-automation. By helping organizations mature from basic automation to a confluence of hyper-automation powered by deep learning capabilities, Ivanti is delivering a self-healing autonomous edge with contextual, anticipatory and personalized experiences for remote workers.

“As remote becomes the next normal, Ivanti Neurons enables organizations to heal and secure devices and deliver a seamless ‘work from anywhere’ employee experience,” said Nayaki Nayyar, EVP and CPO, Ivanti. “Always on and always working, Ivanti Neurons enables IT’s desire to ‘shift-left’ with automation bots that autonomously discover, secure and service endpoints at the edge.”

The new Ivanti Neurons hyper-automation platform offers multiple capabilities for enterprises:

- Ivanti Neurons for Edge Intelligence gives IT the ability to query all edge devices using natural language (NLP) and get real-time intelligence across the enterprise in seconds. It provides quick operational awareness, real-time inventory, and security configurations across the edge leveraging sensor-based architecture.

- Ivanti Neurons for Healing offers an army of automation bots to proactively detect, diagnose, and auto-remediate configuration drift issues, performance issues, compliance issues, and security issues for endpoints. Automation of routine tasks paves the way to creating a truly self-healing environment, reducing time, costs, and improving employee experience.

- Ivanti Neurons for Discovery delivers accurate and actionable asset information in minutes. This provides visibility in real-time using active and passive scanning and third-party connectors. These provide normalized hardware and software inventory data, software usage information and actionable insights to efficiently feed configuration management and asset management databases.

- Ivanti Neurons Workspace provides a 360-degree view of devices, users, applications, and services, with real-time data. This allows first-line analysts to resolve issues previously escalated to specialists. User and device views cut complexity, long wait times and high escalation costs, resulting in faster end user resolution and greater productivity.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...