The “Oh No” Moment – Something Went Wrong
It started with that distinctive chime of a new email – an AWS Cost Anomaly alert. While these alerts are part of my daily life as a FinOps lead, this particular notification about CloudTrail costs caught my attention. AWS’s anomaly detection service had spotted an unusual pattern in our spending, suggesting something more systematic than a typical usage spike. As I dug deeper, I realized we’d stumbled upon a common yet often overlooked cloud cost optimization opportunity.
Understanding the CloudTrail Conundrum
To appreciate why this discovery mattered, let me explain CloudTrail’s complexity. AWS CloudTrail is like an always-on flight recorder for your cloud infrastructure, documenting every API call made in your AWS accounts. While some events are logged for free, others – particularly detailed data events – come with a price tag.
Picture this: the security team sets up organization-wide trails for compliance, while individual application teams create their own trails for debugging. Meanwhile, the DevOps team configures separate trails for operational monitoring, and regional teams in EMEA and APAC establish their own trails, believing they need local logging. Each team has valid requirements – security needs comprehensive audit logs, developers want quick access to troubleshooting data, and operations teams require real-time monitoring. However, this decentralized approach, often arising from team autonomy and legitimate business needs, leads to multiple overlapping trails capturing the same events. The result? Unnecessary costs and complexity that could have been avoided through better coordination and understanding of CloudTrail’s capabilities. The challenge lies in CloudTrail’s flexible but potentially redundant configuration options.
In our case, we discovered several overlapping scenarios: multiple multi-region trails recording identical events, regional trails duplicating what global trails already captured, and separate teams creating independent trails for the same monitoring requirements. Each trail functioned perfectly, but together they created an intricate web of unnecessary costs – like paying multiple photographers to take the same picture from slightly different angles.
Finding the Hidden Costs
Rather than attempting a manual audit of our CloudTrail configurations, I turned to Wiv.ai to build an automated solution. What began as a simple automation evolved into a sophisticated detection system. Using Wiv.ai’s platform, I created a workflow that not only scans for obvious duplicates but understands the nuanced relationships between trails across our AWS organization.
The workflow examines trail configurations with remarkable detail – analyzing event selectors, comparing coverage patterns, and identifying overlaps across regions and accounts. But Wiv.ai’s real strength showed in how it transformed these technical findings into actionable insights. Each discovery automatically generates a detailed case, providing our team with clear visibility into the redundancy and its cost impact.
Streamlining the Solution
The heart of our solution lies in Wiv.ai’s intelligent workflow system. Instead of implementing immediate automated fixes – which could be risky – we built a process that combines automation efficiency with human oversight. When the system identifies redundant trails, it creates detailed cases for review, empowering our DevOps team to make informed decisions.
This approval-based approach proved crucial in building trust. Through Wiv.ai’s interface, our engineers can examine each case in detail, understanding exactly what events are being duplicated and where. They can confidently mark which trails serve as primary logging sources and which can be safely removed. Once approved, Wiv.ai’s automation handles the rest – terminating redundant trails, updating case statuses, and maintaining a comprehensive audit trail.
The system’s intelligence shines in how it handles different scenarios. Cases for removed trails are marked as “Completed”, primary trails as “Expired” once their duplicates are eliminated, and trails kept for specific purposes are appropriately tagged. Once the discovery workflow will run again, when we won’t be able to find the redundant trails, the cases for removed trails will be marked as “Verified”. This automated status management ensures clear tracking of all actions while maintaining compliance and operational requirements.
Conclusion
What began as a response to a cost alert evolved into a showcase of modern FinOps automation. Through Wiv.ai’s platform, we transformed a complex cloud optimization challenge into a continuous improvement process. The system now runs regularly, providing ongoing protection against trail redundancy while maintaining operational safety through human approval processes.
The real success story isn’t just in the immediate cost savings – it’s in how we’ve prevented future redundancies while keeping our DevOps team firmly in control. When new trails are created today, our team receives immediate notifications in Slack about potential overlaps and can confidently remediate them through our automated process.
This journey exemplifies the power of intelligent automation in cloud cost optimization. Through Wiv.ai, we didn’t just solve a problem – we established a new standard for how automation can enhance rather than replace human expertise in cloud operations. It’s a testament to how the right tools and approach can transform complex cloud challenges into streamlined, efficient processes that deliver ongoing value, especially if the platform can be tailored to the organization’s maturity in their FinOps journey.
