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Nyansa Releases Voyance IoT

Nyansa unveiled Voyance IoT, a comprehensive AI-based solution to integrate IoT security and device performance analytics within a single platform.

Voyance IoT automatically classifies, secures, and analyzes the behavior of IoT devices from end-to-end across enterprise wired and wireless access networks.

Voyance IoT represents a new approach to IoT operational assurance that leverages sophisticated AIOps technology to help IT, cybersecurity, and line of business proactively address key issues triggered by IoT, including:

- Continuous discovery, inventory & classification of every critical IoT device

- Baselining IoT device behavior for risk assessment and threat-detection in real-time

- Automating security enforcement to restrict access to malicious or compromised devices

- Ensuring policy adherence of critical IoT devices via micro segmentation

- Detecting and providing root cause for any IoT devices having connectivity problems

- Enabling global industry views into IoT threats, behaviors and performance benchmarks

- Tracking utilization, risk and performance of IoT devices to provide key operational insights

“Companies are spending billions on new non-traditional connected devices to drive specific business outcomes and need assurance that they are achieving the highest possible return on these investments as well as the peace of mind of knowing unmistakably, that these systems are secure,” said Abe Ankumah Co-Founder and CEO of Nyansa. “With Voyance IoT, Nyansa is addressing these pressing industry performance and security concerns on a proven AIOPs platform that has become the de facto standard for big data IT analytics.”

Voyance analyzes the end-to-end behavior of more than 20 million end devices. The benefits of this vast data combined with threat intelligence feeds yield unique value to all Voyance customers.

As a vendor-agnostic analytics solution, Voyance goes beyond simple security to give IT, cybersecurity, and line of business owners rare insight into IoT operational assurance. This includes asset inventory, connectivity, performance and root cause analysis, vulnerability management, risk assessment, and policy compliance. It also helps organizations extend their cybersecurity programs by aligning its core features to the NIST 800-53 and ISO 27K Cyber Security frameworks.

Voyance IoT now allows enterprises to automatically inventory and classify IoT devices, employing a machine learning based, hierarchical device classification system that uses the detailed behavioral signature of each detected device. Beyond automatic classification, customers are also afforded the flexibility of tagging critical devices and assets for continuous analysis within the Voyance IoT security lifecycle management framework.

Once identified, customers immediately have detailed knowledge of every single IoT device in their environment, where they are located and their level of use. They also gain insight into problematic devices that are having any kind of issues connecting to their application with insight into the root cause of the issue.

All IoT devices are fully characterized with an historic baseline of their ‘normal’ behavior. If an abnormality is detected, Voyance IoT seamlessly integrates into a customer’s cybersecurity workflow via their SIEM or other security operations system. This allows customers to enact corrective action directly within Voyance as such quarantining, revoking access, or other customer defined actions through direct integrations to their existing infrastructure.

With patented cloud-native technology that provides anonymized insights for all customers into their IoT devices’ global behavior and threat data, Voyance IoT allows our customers to compare device behavior to other anonymous Voyance customers to gain objective answers to questions surrounding IoT performance and security.

By uniquely analyzing IoT data in full context with all other infrastructure data, IT staff can now proactively find and fix performance issues, automatically identify potential threats and actively enforce policies to ensure the highest levels of security without having to purchase and deploy disparate IoT point products that represent yet another vendor IT tool to master.

The Voyance AIOps platform includes an extensive set of vendor and technology integrations, allowing customers to get the most out of their existing infrastructure and software investments.

- Network Access Control (NAC) and identity systems: Cisco ISE, Aruba/HPE ClearPass, FreeRADIUS, Microsoft RADIUS

- Security threat control platforms: Cisco’s Platform Exchange Grid (pxGrid). Voyance is a certified solution on the Cisco pxGrid ecosystem

- Wireless LAN: Cisco, Aruba/HPE, and Extreme Networks

- CMDB: ServiceNow native integration

- SIEM: Splunk and others via extensible Voyance platform APIs

- Netflow: for wired infrastructure

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Nyansa Releases Voyance IoT

Nyansa unveiled Voyance IoT, a comprehensive AI-based solution to integrate IoT security and device performance analytics within a single platform.

Voyance IoT automatically classifies, secures, and analyzes the behavior of IoT devices from end-to-end across enterprise wired and wireless access networks.

Voyance IoT represents a new approach to IoT operational assurance that leverages sophisticated AIOps technology to help IT, cybersecurity, and line of business proactively address key issues triggered by IoT, including:

- Continuous discovery, inventory & classification of every critical IoT device

- Baselining IoT device behavior for risk assessment and threat-detection in real-time

- Automating security enforcement to restrict access to malicious or compromised devices

- Ensuring policy adherence of critical IoT devices via micro segmentation

- Detecting and providing root cause for any IoT devices having connectivity problems

- Enabling global industry views into IoT threats, behaviors and performance benchmarks

- Tracking utilization, risk and performance of IoT devices to provide key operational insights

“Companies are spending billions on new non-traditional connected devices to drive specific business outcomes and need assurance that they are achieving the highest possible return on these investments as well as the peace of mind of knowing unmistakably, that these systems are secure,” said Abe Ankumah Co-Founder and CEO of Nyansa. “With Voyance IoT, Nyansa is addressing these pressing industry performance and security concerns on a proven AIOPs platform that has become the de facto standard for big data IT analytics.”

Voyance analyzes the end-to-end behavior of more than 20 million end devices. The benefits of this vast data combined with threat intelligence feeds yield unique value to all Voyance customers.

As a vendor-agnostic analytics solution, Voyance goes beyond simple security to give IT, cybersecurity, and line of business owners rare insight into IoT operational assurance. This includes asset inventory, connectivity, performance and root cause analysis, vulnerability management, risk assessment, and policy compliance. It also helps organizations extend their cybersecurity programs by aligning its core features to the NIST 800-53 and ISO 27K Cyber Security frameworks.

Voyance IoT now allows enterprises to automatically inventory and classify IoT devices, employing a machine learning based, hierarchical device classification system that uses the detailed behavioral signature of each detected device. Beyond automatic classification, customers are also afforded the flexibility of tagging critical devices and assets for continuous analysis within the Voyance IoT security lifecycle management framework.

Once identified, customers immediately have detailed knowledge of every single IoT device in their environment, where they are located and their level of use. They also gain insight into problematic devices that are having any kind of issues connecting to their application with insight into the root cause of the issue.

All IoT devices are fully characterized with an historic baseline of their ‘normal’ behavior. If an abnormality is detected, Voyance IoT seamlessly integrates into a customer’s cybersecurity workflow via their SIEM or other security operations system. This allows customers to enact corrective action directly within Voyance as such quarantining, revoking access, or other customer defined actions through direct integrations to their existing infrastructure.

With patented cloud-native technology that provides anonymized insights for all customers into their IoT devices’ global behavior and threat data, Voyance IoT allows our customers to compare device behavior to other anonymous Voyance customers to gain objective answers to questions surrounding IoT performance and security.

By uniquely analyzing IoT data in full context with all other infrastructure data, IT staff can now proactively find and fix performance issues, automatically identify potential threats and actively enforce policies to ensure the highest levels of security without having to purchase and deploy disparate IoT point products that represent yet another vendor IT tool to master.

The Voyance AIOps platform includes an extensive set of vendor and technology integrations, allowing customers to get the most out of their existing infrastructure and software investments.

- Network Access Control (NAC) and identity systems: Cisco ISE, Aruba/HPE ClearPass, FreeRADIUS, Microsoft RADIUS

- Security threat control platforms: Cisco’s Platform Exchange Grid (pxGrid). Voyance is a certified solution on the Cisco pxGrid ecosystem

- Wireless LAN: Cisco, Aruba/HPE, and Extreme Networks

- CMDB: ServiceNow native integration

- SIEM: Splunk and others via extensible Voyance platform APIs

- Netflow: for wired infrastructure

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...