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ScienceLogic Rolls Out New AI and Automation Updates

ScienceLogicvannounced a series of updates to its solution suite, including new capabilities for the ScienceLogic AI Platform and Skylar™ AI Suite in terms of automation, observability, secure government operations, and low-code development. 

“We’re focused on delivering purposeful AI that empowers IT teams with the insight and control they need to stay ahead of disruptions,” said Michael Nappi, chief product officer at ScienceLogic. “By combining advanced analytics, automation, and secure extensibility, we’re helping customers drive better outcomes across hybrid-cloud and mission-critical environments.”

Key solution updates include:

Skylar Analytics Unleashes AI-Powered Insights for Proactive IT Mastery: Skylar Analytics has cemented itself as a game-changer since its launch in early 2025, winning the AI Breakthrough Award for “Predictive Analytics Platform of the Year” and the Bronze Stevies American Business Award in “Artificial Intelligence and Machine Learning Solution” category. Building on these achievements and recent industry validation, ScienceLogic has continued to refine the Skylar Analytics solution. Most noteworthy are the updates to its ability to transform raw telemetry into a strategic advantage through four cutting-edge enhancements, including:

  • Data Visualization: Incorporation of Apache Superset dashboards offer a wide array of visualization options for ScienceLogic AI Platform data, enabling users to create diverse and interactive charts, graphs, and dashboards from basic bar charts to complex geospatial visualizations, allowing for compelling data storytelling. The updates offer an intuitive, no-code interface for quick chart building, making it accessible to users with varying technical backgrounds.
  • Powerful yet Simple Data Export: New ODBC integrations with Tableau and Microsoft Power BI for deep metric analysis allow your BI team to use their own know-how and tools to leverage ScienceLogic’s comprehensive data lake.
  • Anomaly Detection: Prioritized scoring of outliers in time series data is viewable directly in our updated intelligent Device Investigator.
  • Predictive Alerting: Additional visibility to simplify automatic flagging issues like storage exhaustion or network oversubscription before they escalate.

These enhancements further facilitate IT teams’ transition from reactive operations to proactive, insight-driven management. 

FedRAMP Authorization Accelerates Government Cloud Adoption: In May 2025, ScienceLogic’s Government Cloud achieved FedRAMP Moderate Authorization, satisfying over 300 NIST-aligned controls. Now listed on the FedRAMP Marketplace and DoDIN APL, the FedRAMP-certified platform simplifies procurement for federal agencies. It delivers unified observability across on-prem, cloud, and hybrid environments, with Zero Trust-aligned dashboards and AI-enhanced decision support to boost operational resilience. 

NVIDIA GPU Monitoring Delivers Real-Time Performance at Scale: The updated NVIDIA GPU Monitoring ScienceLogic PowerPack (v100) provides real-time observability into high-performance compute environments, including AI training, simulation, and edge computing. The PowerPack works automatically to simplify intelligent discovery while establishing proactive monitors for GPU metrics like utilization and thermal status. At the same time, built-in event policies trigger alerts for issues such as overheating or performance drops which are critical to capturing maximum value from these expensive systems and workloads.

These capabilities help IT teams optimize resources, sustain uptime, and efficiently manage increasingly complex, rapidly growing GPU infrastructure. 

Dynamic Application Builder Simplifies Custom Monitoring with Low-Code Tools: Many competitive out-of-the-box observability solutions don’t allow IT to control their own destiny to support the next new technology that your organization requires for management and custom instrumentation. These updates to the ScienceLogic AI Platform take no-code simplification to the next level, quickly instrumenting anything you need to monitor, manage, or automate. The Dynamic Application Builder (v1.2.0) accelerates custom monitoring development via a low-code wizard. New features enable developers to configure HTTP/SSH credentials, pull API or CLI payloads, and export directly to SL1. It supports rapid creation of PowerPack-ready components via snippet arguments, JC parsers, and custom headers.  

Enhancements include API testing, basic authentication (OAuth2 support planned), integration with the “Low-Code Tools” PowerPack for bulk buildout, and stronger SSL verification and authentication handling.

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 ...

ScienceLogic Rolls Out New AI and Automation Updates

ScienceLogicvannounced a series of updates to its solution suite, including new capabilities for the ScienceLogic AI Platform and Skylar™ AI Suite in terms of automation, observability, secure government operations, and low-code development. 

“We’re focused on delivering purposeful AI that empowers IT teams with the insight and control they need to stay ahead of disruptions,” said Michael Nappi, chief product officer at ScienceLogic. “By combining advanced analytics, automation, and secure extensibility, we’re helping customers drive better outcomes across hybrid-cloud and mission-critical environments.”

Key solution updates include:

Skylar Analytics Unleashes AI-Powered Insights for Proactive IT Mastery: Skylar Analytics has cemented itself as a game-changer since its launch in early 2025, winning the AI Breakthrough Award for “Predictive Analytics Platform of the Year” and the Bronze Stevies American Business Award in “Artificial Intelligence and Machine Learning Solution” category. Building on these achievements and recent industry validation, ScienceLogic has continued to refine the Skylar Analytics solution. Most noteworthy are the updates to its ability to transform raw telemetry into a strategic advantage through four cutting-edge enhancements, including:

  • Data Visualization: Incorporation of Apache Superset dashboards offer a wide array of visualization options for ScienceLogic AI Platform data, enabling users to create diverse and interactive charts, graphs, and dashboards from basic bar charts to complex geospatial visualizations, allowing for compelling data storytelling. The updates offer an intuitive, no-code interface for quick chart building, making it accessible to users with varying technical backgrounds.
  • Powerful yet Simple Data Export: New ODBC integrations with Tableau and Microsoft Power BI for deep metric analysis allow your BI team to use their own know-how and tools to leverage ScienceLogic’s comprehensive data lake.
  • Anomaly Detection: Prioritized scoring of outliers in time series data is viewable directly in our updated intelligent Device Investigator.
  • Predictive Alerting: Additional visibility to simplify automatic flagging issues like storage exhaustion or network oversubscription before they escalate.

These enhancements further facilitate IT teams’ transition from reactive operations to proactive, insight-driven management. 

FedRAMP Authorization Accelerates Government Cloud Adoption: In May 2025, ScienceLogic’s Government Cloud achieved FedRAMP Moderate Authorization, satisfying over 300 NIST-aligned controls. Now listed on the FedRAMP Marketplace and DoDIN APL, the FedRAMP-certified platform simplifies procurement for federal agencies. It delivers unified observability across on-prem, cloud, and hybrid environments, with Zero Trust-aligned dashboards and AI-enhanced decision support to boost operational resilience. 

NVIDIA GPU Monitoring Delivers Real-Time Performance at Scale: The updated NVIDIA GPU Monitoring ScienceLogic PowerPack (v100) provides real-time observability into high-performance compute environments, including AI training, simulation, and edge computing. The PowerPack works automatically to simplify intelligent discovery while establishing proactive monitors for GPU metrics like utilization and thermal status. At the same time, built-in event policies trigger alerts for issues such as overheating or performance drops which are critical to capturing maximum value from these expensive systems and workloads.

These capabilities help IT teams optimize resources, sustain uptime, and efficiently manage increasingly complex, rapidly growing GPU infrastructure. 

Dynamic Application Builder Simplifies Custom Monitoring with Low-Code Tools: Many competitive out-of-the-box observability solutions don’t allow IT to control their own destiny to support the next new technology that your organization requires for management and custom instrumentation. These updates to the ScienceLogic AI Platform take no-code simplification to the next level, quickly instrumenting anything you need to monitor, manage, or automate. The Dynamic Application Builder (v1.2.0) accelerates custom monitoring development via a low-code wizard. New features enable developers to configure HTTP/SSH credentials, pull API or CLI payloads, and export directly to SL1. It supports rapid creation of PowerPack-ready components via snippet arguments, JC parsers, and custom headers.  

Enhancements include API testing, basic authentication (OAuth2 support planned), integration with the “Low-Code Tools” PowerPack for bulk buildout, and stronger SSL verification and authentication handling.

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 ...