Logentries released two new Community Packs offering out-of-the-box intelligence for AWS CloudTrail and AWS CloudWatch log monitoring and troubleshooting. Logentries AWS Community Packs give AWS users exactly what they need to extract key information and easily correlate the critical performance, security, and systems events.
With only a few mouse clicks, AWS users can turn raw CloudTrail and CloudWatch logs into meaningful insights using the Community Pack dashboards, pre-defined queries and alerts. This insight provides users with immediate understanding of what is occurring across their AWS environment and the important applications running on it. The Logentries and AWS integrations were designed with AWS to offer users quick insight into security and compliance issues; AWS account user activity and tracking; and system performance monitoring and troubleshooting.
“With many of our critical services running on AWS, it is incredibly valuable to have real-time, log-level insight into what is happening across our environment and users,” said Kirill Bensonoff, Co-Founder and Principle at ComputerSupport. “Logentries’ integration with AWS logs provides us with real-time visibility into important server monitoring metrics, as well as exceptions, anomalies, and custom security events that we need to know about as soon as they occur.”
The two new AWS Community Packs for CloudTrail and CloudWatch log collection and analysis include the following “plug and play: features:
Saved Queries: Saved Queries can be used to quickly get visibility into:
- AWS account information such as EC2 instance activity, AWS account user activity, and common security events.
- Important resource usage trends such as CPU, Network or I/O of EC2 instances, as well as performance insights into other AWS services (EBS, ELB, DynamoDB, etc.)
Tags and Alerts: Saved Tags and Alerts highlight:
- Instances of important AWS CloudTrail events (based on AWS CloudTrail best practices) as well as anomaly detection and inactivity alerts to highlight spikes in important security events, password updates, policy-related events and EC2 instance restarts and terminations.
- Breaches in performance thresholds, spikes in resource usage, and API downtime.
Data Visualizations: Visualizations of log data provide immediate visibility into:
- AWS user actions (root and IAMUsers) as well as into the activity of all EC2 instances.
- CPU, Memory and Network usage over time across the entire AWS environment and at a per server instance level.
“Today, thousands of Logentries users are managing CloudTrail and CloudWatch data, and we wanted to offer a set of tools that would reduce the amount of time they need to spend collecting and analyzing their logs,” said Trevor Parsons, Chief Scientist and Co-Founder, Logentries. “By looking across our global Community, we identified the most popular queries, alerts, and dashboards for the AWS environment, and created two Community Packs that would deliver these, instantly.”
The cloud-based Logentries service collects and pre-processes log events in real-time for on-demand analysis, alerting and visualization. With custom tagging and filtering, users can correlate security and performance issues with broader infrastructure activity including application usage, server metrics and user behavior. The Logentries Community Packs can be downloaded in minutes and provides users with immediate access to pre-configured queries with real-time tags, alerts, and data visualization dashboards.
The Latest
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 ...
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.