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ScienceLogic Introduces Behavioral Correlation

ScienceLogic unveiled its latest innovation in context-infused AIOps—Behavioral Correlation—which enables IT teams to identify, troubleshoot and remediate service-disrupting events before end-user impact can even be detected.

Modern IT environments breed complexity through the proliferation of applications and data, outstripping human capacity for analysis and response. Operations teams pressured to hunt down and fix customer-impacting IT events are often constrained by antiquated methods founded on human-centered analyses. With Behavioral Correlation, ScienceLogic introduces a radically more efficient approach, leveraging machine-speed detection and remediation. Real-time service health and risk can now be understood at a glance, allowing IT teams to rapidly address issues when they occur or even intercept potential service-impacting issues before they happen.

Behavioral Correlation for ScienceLogic SL1 is delivered through tightly integrated core capabilities:

- A comprehensive, real-time data lake that captures multimodal data types and their relationships.

- The ability to map and visualize IT services, their underlying dependencies, and associated health, availability, and risk.

- Machine-learning techniques that reason over these service topologies to detect the root cause of issues or anomalous behavior, and can recommend actions to address.

The end result is a system that provides holistic visibility into complex IT estates through an intuitive, service-centric lens. Decision-makers can quickly understand the impact, root causes and relative priority – ensuring IT is spending time on what matters most to the business.

“This is a game-changing innovation that buries the old-school, reactionary approach to IT event management. We can now instantly provide a real-time picture across the entire ephemeral state of IT – pinpointing where service degradation is happening, which issues should be prioritized, and the potential business impact,” said Dave Link, ScienceLogic CEO. “The cohesive, service-level view alleviates IT teams scrambling from one incident to the next and empowers providers worldwide to deliver a resilient customer experience.”

With IT teams free from the 1990s war rooms and defensive positions battling “event storms,” they can embrace a new standard for situational awareness to help drive faster root-cause analysis and time to resolution.

ScienceLogic customers will be able to access this new capability through the latest Colosseum Release due out in late Q2, 2020.

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ScienceLogic Introduces Behavioral Correlation

ScienceLogic unveiled its latest innovation in context-infused AIOps—Behavioral Correlation—which enables IT teams to identify, troubleshoot and remediate service-disrupting events before end-user impact can even be detected.

Modern IT environments breed complexity through the proliferation of applications and data, outstripping human capacity for analysis and response. Operations teams pressured to hunt down and fix customer-impacting IT events are often constrained by antiquated methods founded on human-centered analyses. With Behavioral Correlation, ScienceLogic introduces a radically more efficient approach, leveraging machine-speed detection and remediation. Real-time service health and risk can now be understood at a glance, allowing IT teams to rapidly address issues when they occur or even intercept potential service-impacting issues before they happen.

Behavioral Correlation for ScienceLogic SL1 is delivered through tightly integrated core capabilities:

- A comprehensive, real-time data lake that captures multimodal data types and their relationships.

- The ability to map and visualize IT services, their underlying dependencies, and associated health, availability, and risk.

- Machine-learning techniques that reason over these service topologies to detect the root cause of issues or anomalous behavior, and can recommend actions to address.

The end result is a system that provides holistic visibility into complex IT estates through an intuitive, service-centric lens. Decision-makers can quickly understand the impact, root causes and relative priority – ensuring IT is spending time on what matters most to the business.

“This is a game-changing innovation that buries the old-school, reactionary approach to IT event management. We can now instantly provide a real-time picture across the entire ephemeral state of IT – pinpointing where service degradation is happening, which issues should be prioritized, and the potential business impact,” said Dave Link, ScienceLogic CEO. “The cohesive, service-level view alleviates IT teams scrambling from one incident to the next and empowers providers worldwide to deliver a resilient customer experience.”

With IT teams free from the 1990s war rooms and defensive positions battling “event storms,” they can embrace a new standard for situational awareness to help drive faster root-cause analysis and time to resolution.

ScienceLogic customers will be able to access this new capability through the latest Colosseum Release due out in late Q2, 2020.

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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 ...