Leveraging Sumo Logic to Achieve Cloud-First Security

As organizations increasingly shift to cloud-native infrastructures, traditional approaches to security information and event management (SIEM) have struggled to keep pace. Legacy SIEM platforms, originally designed for on-premises environments, often lack the agility, scalability, and cost-effectiveness required to manage the velocity of cloud-scale telemetry. In our recent efforts to modernize security operations, we transitioned to a cloud-native SIEM model, leveraging Sumo Logic, to better align with our cloud-first strategy.

Why Cloud-Native SIEM?

Before diving into the “how,” it’s worth considering the “why.” As our infrastructure expanded to encompass Kubernetes clusters, serverless applications, and multi-cloud deployments, our security operations had to evolve too.

We required a platform that was capable of:

• Natively ingesting cloud logs at scale (e.g., AWS CloudTrail, Okta Audit logs, Tenable)
• Providing real-time visibility and alerting across disparate systems
• Leveraging machine learning to filter out noise and identify credible threats
• Enabling rapid investigation without the need to maintain underlying infrastructure

This set of requirements underscored the need for cloud-native SIEM – one designed for elasticity, speed, and intelligence from the ground up.

Implementing Sumo Logic: A Pragmatic Approach

Sumo Logic stood out due to its flexible ingestion model, support for a wide range of cloud services, and its cloud-native architecture, which also aligned well with our operational goals.

Step 1: Data Onboarding

We began by identifying our most critical log sources, including:

• AWS CloudTrail and VPC Flow Logs
• Kubernetes audit logs and container runtime events
• Identity provider logs
• SaaS platform logs (such as GitHub and Atlassian)

Sumo Logic’s cloud-to-cloud integration made onboarding straightforward, eliminating the need for sidecars or agents for many of the sources. For more complex sources like Kubernetes logs, we utilized a combination of Syslog servers and Sumo Logic’s open-source Kubernetes collection agents.

Step 2: Normalization and Parsing

An early success came from leveraging Sumo Logic’s out-of-the-box log parsing for commonly used cloud services. For our custom applications, we developed field extraction rules to structure our semi-structured logs. This improved downstream queries and enabled correlation across systems.

Step 3: Detection and Alerting

Sumo Logic’s Cloud SIEM product provided a solid foundation of pre-built rules. Utilizing the existing foundation, we incorporated custom detections, tailored to our architecture.

Examples included:

• Unusual Access Patterns: Alerts for logins from unfamiliar geographic locations, especially involving privileged accounts
• Infrastructure Drift: Identification of unauthorized changes to security groups or identity and access management (IAM) policies outside approved windows
• Kubernetes Threats: Detection of containers initiating unexpected processes or accessing sensitive mounts

Alerts were integrated with our incident response tooling via webhooks and automation runbooks, reducing mean time to detect (MTTD) and mean time to respond (MTTR).

Step 4: Investigation and Context

Raw logs offer limited insights without much context. A major advantage of cloud-native SIEM platforms is their ability to correlate activity across services. For instance, if a user logs in from an unknown IP address, makes a code commit, and then launches an EC2 instance with elevated permissions, Sumo Logic correlates these events into a single, consistent security insight.

This holistic view significantly reduced the time analysts spent navigating between systems and enabled earlier detection of potential attack paths.

Key Lessons Learned

1. Start small, iterate fast: Begin with high-priority log sources and expand over time.
2. Use built-in content but customize: Default rules are useful, but must be tailored to your organization’s environment.
3. Design: Dashboards and queries should prioritize usability for Tier 1 analysts.
4. Treat the SIEM like a product: Continuous feedback, tuning, and governance are essential for long-term success.

Looking Ahead

Our journey with cloud-native SIEM is ongoing. We are currently exploring integrations with threat intelligence feeds, expanding our ML-based detections, and working to better align our DevSecOps workflows with insights generated by the SIEM.

Ultimately, cloud-native SIEM is more than just a tool, it is a foundational capability. When implemented thoughtfully, it functions as the central nervous system of cloud security operations, driving agility and insights.