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Observo AI Joins Google Cloud Partner Advantage

Observo AI has joined Google Cloud Partner Advantage as a Partner level partner in the Build engagement model, giving Google Cloud customers the ability to leverage Observo AI to dramatically reduce security data volumes and costs while improving threat detection speed through AI-powered telemetry pipelines.

As a Google Cloud partner, Observo AI offers customers intelligent data optimization that enriches security telemetry by over 80% before it reaches Google Security Operations (SecOps), enabling faster threat detection and more comprehensive security coverage. 

Key features include:

  • 40% faster mean-time-to-resolution through AI-powered anomaly detection and sentiment analysis that prioritizes critical threats.
  • Smart data optimization and routing for Google SecOps with real-time threat intelligence integration and enrichment.
  • Orion agentic AI engineer that autonomously builds, optimizes, and maintains data pipelines while providing intelligent recommendations.
  • Enhanced threat hunting and forensics with searchable data lakes, natural language querying, and seamless BigQuery integration for advanced analytics.
  • Automated compliance protection with AI-powered PII detection and masking across all data streams.
  • Petabyte-scale processing handling enterprise data volumes while maintaining real-time performance.
  • 400+ tool integrations enabling seamless connectivity across existing security and DevOps ecosystems

"Security teams are drowning in data growth while budgets remain flat. By joining Google Cloud Partner Advantage, Observo AI gives SecOps customers an AI data engineer that automatically optimizes their telemetry—cutting costs dramatically while actually improving their security posture," said Gurjeet Arora, Co-Founder and CEO of Observo AI. "Instead of choosing between comprehensive security coverage and budget constraints, teams can now have both." 

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Observo AI Joins Google Cloud Partner Advantage

Observo AI has joined Google Cloud Partner Advantage as a Partner level partner in the Build engagement model, giving Google Cloud customers the ability to leverage Observo AI to dramatically reduce security data volumes and costs while improving threat detection speed through AI-powered telemetry pipelines.

As a Google Cloud partner, Observo AI offers customers intelligent data optimization that enriches security telemetry by over 80% before it reaches Google Security Operations (SecOps), enabling faster threat detection and more comprehensive security coverage. 

Key features include:

  • 40% faster mean-time-to-resolution through AI-powered anomaly detection and sentiment analysis that prioritizes critical threats.
  • Smart data optimization and routing for Google SecOps with real-time threat intelligence integration and enrichment.
  • Orion agentic AI engineer that autonomously builds, optimizes, and maintains data pipelines while providing intelligent recommendations.
  • Enhanced threat hunting and forensics with searchable data lakes, natural language querying, and seamless BigQuery integration for advanced analytics.
  • Automated compliance protection with AI-powered PII detection and masking across all data streams.
  • Petabyte-scale processing handling enterprise data volumes while maintaining real-time performance.
  • 400+ tool integrations enabling seamless connectivity across existing security and DevOps ecosystems

"Security teams are drowning in data growth while budgets remain flat. By joining Google Cloud Partner Advantage, Observo AI gives SecOps customers an AI data engineer that automatically optimizes their telemetry—cutting costs dramatically while actually improving their security posture," said Gurjeet Arora, Co-Founder and CEO of Observo AI. "Instead of choosing between comprehensive security coverage and budget constraints, teams can now have both." 

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

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 ...