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SIOS High Availability Software Now Validated for Cimcor CimTrak Integrity Suite

SIOS Technology Corp. announced that SIOS LifeKeeper and SIOS DataKeeper clustering software have been validated for use with Cimcor’s cybersecurity solution, the CimTrak Integrity Suite. 

This collaboration allows Cimcor customers to seamlessly integrate high availability and disaster recovery into their CimTrak environments, ensuring continuous protection against cyber threats and minimizing downtime in critical cybersecurity operations.

SIOS LifeKeeper is a high availability solution that ensures critical applications are always available, automatically detecting failures and initiating failover to standby systems to maintain uninterrupted service. SIOS DataKeeper offers synchronous data replication, ensuring that data is consistently mirrored across multiple servers for fast recovery in case of server failure. Together, these solutions provide comprehensive protection for business-critical applications like CimTrak, helping organizations minimize the risk of downtime and data loss while maintaining secure, highly available systems.

CimTrak is a System Integrity Assurance platform that provides real-time monitoring and protection of critical IT assets - from servers and networks to cloud configurations, containers, databases, and industrial control systems. Its patented real-time detection technology lets CimTrak instantly detect and remediate unwanted and unexpected changes across your infrastructure while maintaining system integrity. The platform helps organizations strengthen their security posture, maintain continuous compliance, and protect against external and internal threats through automated workflows and comprehensive change management.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

SIOS High Availability Software Now Validated for Cimcor CimTrak Integrity Suite

SIOS Technology Corp. announced that SIOS LifeKeeper and SIOS DataKeeper clustering software have been validated for use with Cimcor’s cybersecurity solution, the CimTrak Integrity Suite. 

This collaboration allows Cimcor customers to seamlessly integrate high availability and disaster recovery into their CimTrak environments, ensuring continuous protection against cyber threats and minimizing downtime in critical cybersecurity operations.

SIOS LifeKeeper is a high availability solution that ensures critical applications are always available, automatically detecting failures and initiating failover to standby systems to maintain uninterrupted service. SIOS DataKeeper offers synchronous data replication, ensuring that data is consistently mirrored across multiple servers for fast recovery in case of server failure. Together, these solutions provide comprehensive protection for business-critical applications like CimTrak, helping organizations minimize the risk of downtime and data loss while maintaining secure, highly available systems.

CimTrak is a System Integrity Assurance platform that provides real-time monitoring and protection of critical IT assets - from servers and networks to cloud configurations, containers, databases, and industrial control systems. Its patented real-time detection technology lets CimTrak instantly detect and remediate unwanted and unexpected changes across your infrastructure while maintaining system integrity. The platform helps organizations strengthen their security posture, maintain continuous compliance, and protect against external and internal threats through automated workflows and comprehensive change management.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...