When a major outage hits, everyone asks: “What caused this?” But there's an equally critical question: “What else will this impact?”
Root cause analysis is an important part of incident response. But the high-profile outages of October 2025, like the AWS and Microsoft DNS failures, exposed a fundamental gap: by the time companies understand the full impact, customers are already affected, and the damage is done.
Root cause analysis is incomplete. Companies also need affected cause analysis.
| Root cause analysis asks | Affected cause analysis asks |
|---|---|
| This system failed. What caused it? | If this system fails or changes, what else will be affected? |
In many situations, it's important to start with affected cause analysis to quickly put a stop to the impact of the issue, before solving the core problem.
Two recent examples: AWS and Microsoft
On 20 October 2025, AWS experienced a DNS race condition in DynamoDB that cascaded through their US-EAST-1 region for more than 12 hours. The outage affected services like Coinbase, Fortnite, Signal, Venmo and Zoom, platforms that depend on AWS infrastructure. Even AWS's own status page went down during the incident.
Nine days later, on 29 October, a configuration change to Azure Front Door triggered DNS failures that impacted the Azure Portal, Microsoft 365, Outlook and authentication systems. Third-party platforms like Starbucks, Costco and the Dutch railway system were also affected.
Both incidents highlight something every technology leader knows: modern infrastructure is deeply interconnected, and failures cascade in ways that aren't always predictable.
Where affected cause analysis creates value
While teams are investigating root cause, which can take hours, affected cause analysis immediately answers:
- Which services are impacted right now?
- Which customers are experiencing issues?
- Where can we implement temporary mitigations to reduce blast radius?
- What's the priority order for restoration?
You can't fix the root cause instantly, but you can often isolate affected services, reroute traffic, or communicate proactively with impacted customers if you understand the dependency chain in real time.
But how can you answer “what will be affected?”
Traditional dependency maps fall short because they don't capture how systems actually behave. They're static snapshots that become outdated the moment your environment changes. They don't show you whether a dependency is critical every second or runs once a week. And they don't reveal how failures cascade through multiple layers.
What real-time affected cause analysis looks like
Mugato solves this exact problem. We collect data directly from your servers to see how systems really communicate: how often and how critical the connections are. When an incident hits, we instantly show you the complete chain reaction across your infrastructure.
Instead of spending hours correlating data across multiple tools, teams get a single view that answers:
- What's affected?
- How is it spreading?
- Where should we focus our mitigation efforts?
