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Elastic Announces New Alerting Framework

Elastic announced the launch of a new alerting framework delivered across the Elastic Stack to provide first-class experiences with tailored interfaces that allow users to create powerful alerts in the normal flow of their daily tasks.

The new alerting framework is delivered via Kibana across the Elastic Stack and available within the SIEM, Uptime, APM, and Metrics applications. From monitoring application transactions to tracking brute force login attempts, users are enabled with embedded alerting functionality and easily configured integrations with email platforms, and providers including PagerDuty, ServiceNow, and Slack.

Embedding native alerting within the Elastic Stack delivers on the company’s vision for creating a single, intuitive user experience with integrated workflows that are tailored to a user’s context and use case, and includes predefined detection and action mechanisms.

"Alerting is a critical capability for anyone with time series data, but it’s especially critical for Observability and Security," said Steve Kearns, VP, Product Management, Elastic. "That's why we designed our new alerting framework from the ground up to make it easy to build alerting UIs anywhere in Kibana, allowing us to bring intuitive workflows to where the operations and security practitioners need them. With integrations into key third-party systems, from PagerDuty to Slack, it's never been easier to keep an eye on data from a distance."

The new alerting framework is being introduced as a beta in the 7.7 release of Kibana and is available immediately.

Elastic also announced major updates across the Elastic solution portfolio with dozens of advances to bring efficiency, flexibility, and integrated workflows to teams of every size and across every use case.

In addition to the alerting, Elastic Observability updates include:

Service Maps

- Provides a graphical view of the dependencies between the services powering an application.

- Presents real-time view of live data and system dependencies to speed the troubleshooting of issues in today’s distributed and cloud-native environments.

- Offers an aggregate view of how services interact, along with key summary information about each component, allowing teams to toggle between a 50,000-foot view and a granular view with ease.

Expanded Integrations

- Adds new, out-of-the-box integrations to collect logs and metrics from many common data sources across the infrastructure ecosystem and simplifies instrumentation across all layers of the technology stack.

- Ensures teams can quickly gather the context they need from a system to investigate and debug new and complex problems within their infrastructure.

- Key integrations include:
AWS Lambda, Virtual Private Cloud, Amazon Aurora, DynamoDB
Azure Database accounts, Kubernetes, and container metrics
Google Cloud Platform Pub/Sub and Load Balancing
IBM MQ
Istio
MQTT
Pivotal Cloud Foundry
Prometheus
Redis Enterprise

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Elastic Announces New Alerting Framework

Elastic announced the launch of a new alerting framework delivered across the Elastic Stack to provide first-class experiences with tailored interfaces that allow users to create powerful alerts in the normal flow of their daily tasks.

The new alerting framework is delivered via Kibana across the Elastic Stack and available within the SIEM, Uptime, APM, and Metrics applications. From monitoring application transactions to tracking brute force login attempts, users are enabled with embedded alerting functionality and easily configured integrations with email platforms, and providers including PagerDuty, ServiceNow, and Slack.

Embedding native alerting within the Elastic Stack delivers on the company’s vision for creating a single, intuitive user experience with integrated workflows that are tailored to a user’s context and use case, and includes predefined detection and action mechanisms.

"Alerting is a critical capability for anyone with time series data, but it’s especially critical for Observability and Security," said Steve Kearns, VP, Product Management, Elastic. "That's why we designed our new alerting framework from the ground up to make it easy to build alerting UIs anywhere in Kibana, allowing us to bring intuitive workflows to where the operations and security practitioners need them. With integrations into key third-party systems, from PagerDuty to Slack, it's never been easier to keep an eye on data from a distance."

The new alerting framework is being introduced as a beta in the 7.7 release of Kibana and is available immediately.

Elastic also announced major updates across the Elastic solution portfolio with dozens of advances to bring efficiency, flexibility, and integrated workflows to teams of every size and across every use case.

In addition to the alerting, Elastic Observability updates include:

Service Maps

- Provides a graphical view of the dependencies between the services powering an application.

- Presents real-time view of live data and system dependencies to speed the troubleshooting of issues in today’s distributed and cloud-native environments.

- Offers an aggregate view of how services interact, along with key summary information about each component, allowing teams to toggle between a 50,000-foot view and a granular view with ease.

Expanded Integrations

- Adds new, out-of-the-box integrations to collect logs and metrics from many common data sources across the infrastructure ecosystem and simplifies instrumentation across all layers of the technology stack.

- Ensures teams can quickly gather the context they need from a system to investigate and debug new and complex problems within their infrastructure.

- Key integrations include:
AWS Lambda, Virtual Private Cloud, Amazon Aurora, DynamoDB
Azure Database accounts, Kubernetes, and container metrics
Google Cloud Platform Pub/Sub and Load Balancing
IBM MQ
Istio
MQTT
Pivotal Cloud Foundry
Prometheus
Redis Enterprise

The Latest

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 ...

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 ...