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
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...