Operations | Monitoring | ITSM | DevOps | Cloud

Modern security operations are drowning in data but starving for intelligence. Organizations process terabytes of raw telemetry daily from a wide variety of sources, including firewalls, endpoints, and cloud services, yet 80-90% lacks the contextual intelligence needed for effective threat detection and response. This torrent of data creates a major challenge for Security Operations Centers (SOCs).

Security teams are no longer just reacting to alerts, they're conducting deep, forensic-style investigations to uncover sophisticated threats that unfold over months or even years.

Today's announcement that Observo AI is joining SentinelOne isn't just about combining two companies. It's about accelerating a fundamental transformation in how security operations work—moving from reactive, manual processes to truly autonomous defense. The vision is bold: security operations that think, adapt, and respond faster than any human team could alone. But that future requires rethinking security data from the ground up.

‍Situation: The Overwhelming Reality of Modern IT (Security) Data In today’s enterprise environments, IT and security teams face a relentless flood of telemetry data from diverse sources—syslog servers, cloud platforms like AWS CloudTrail, network devices, applications, and security tools such as firewalls and Windows Events.

Microsoft Sentinel is a popular cloud-native SIEM, offering tight integration across Azure services, native machine learning models, and the scalability of Microsoft’s cloud infrastructure. It enables real-time threat detection and response across hybrid environments, making it a go-to solution for many enterprises embracing the Microsoft ecosystem. But like all modern SIEMs, Sentinel is only as effective as the data it ingests.

Security operations today are drowning in data. With cloud workloads, SaaS tools, endpoint agents, and firewall appliances all generating telemetry 24/7, many organizations are reaching a tipping point—unable to afford full visibility, yet afraid of what might slip through the cracks. That’s why AI-native security data pipelines have emerged as a critical solution.

Elastic is a go-to choice for organizations that want a powerful, flexible search and analytics engine without the cost overhead of traditional SIEM platforms. With its open-source foundation and customizable architecture, the Elastic (ELK) Stack—Elasticsearch, Logstash, and Kibana—has become a cornerstone for many modern observability and security workflows.

When we decided to join Google Cloud Partner Advantage, it wasn't just about expanding our reach or checking a partnership box. It represents our belief that the future of enterprise security operations is fundamentally cloud-native—and that belief is driving how we think about solving the data crisis facing security teams today. The announcement comes at a critical inflection point.

Security teams love the power of Google SecOps (formerly Chronicle)—fast detection, scalable infrastructure, and native integration across Google Cloud. But there’s a challenge most organizations still struggle with: the cost, complexity, and chaos of raw telemetry data. Security data doubles every 2-3 years. Without control and context, even the best SIEM platforms can be overwhelmed with noise. That’s where Observo AI comes in.

When it comes to security operations, speed is everything. The faster a team can detect, investigate, and respond to an incident, the more likely they are to prevent impact and contain risk. But accelerating Mean Time to Resolution (MTTR) requires more than faster alerts or streamlined dashboards—it demands a shift in how organizations think about their data. Smart security teams are rethinking the entire telemetry lifecycle.