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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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The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...

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