Hello everyone! I've been tasked with shepherding our company's new Akamai Prolexic implementation, and I’ll be honest—while I'm comfortable with APIs and data flows, the world of DDoS protection reporting is a bit new to me. Our leadership is supportive but has (rightly) asked for clear metrics to demonstrate the ROI and effectiveness of this solution.
I understand the basic premise of blocking attacks, but I want to make sure I'm tracking the right things from day one. Beyond just "we blocked X attacks," what are the specific, tangible data points you've found most compelling for stakeholders? I'm thinking about this in a few buckets:
**Operational & Security Metrics:**
* Attack frequency and volume before/after onboarding: What's the best way to baseline our "before" state if we were previously relying on infrastructure providers?
* Top attack vectors mitigated: Is it most valuable to show the diversity of threats (e.g., DNS floods, SYN floods, HTTP/S attacks) Prolexic is handling?
* Time to mitigation: I've heard Prolexic is fast, but what's a realistic benchmark for full mitigation once an attack is detected?
**Business & Financial Metrics:**
* Uptime/SLA adherence for protected services: How do you correlate attack logs to service availability dashboards (like from our status page tool)?
* Resource cost savings: Has anyone quantified the saved bandwidth, compute, or staff time previously spent on manual mitigation?
* Risk reduction: This feels abstract, but is there a way to tie blocked attacks to potential downtime costs or breach risks?
I'm particularly interested in how you might **automate** pulling these metrics from Prolexic reports into a dashboard (like a Grafana or a Google Sheet). Does the portal offer good API access for this, or is it mostly manual report generation?
Any guidance on the key reports or specific terminology to focus on would be incredibly helpful. I want to build a clear, automated narrative that shows this isn't just a cost center but a critical piece of our operational integrity.
Thanks in advance for sharing your wisdom
Stay connected
Good luck baselining the "before" state. Most infra provider DDoS "protection" is just auto-scaling you into bankruptcy while the attack runs. The real metric is the cloud bill you didn't get.
For time-to-mitigation, you need to define "mitigation." Full scrubbing? Under a minute. Your internal dashboards showing clean traffic? That's the lag that matters to your team. Track that.
Your business metric is the one nobody talks about: engineering hours *not* spent fighting fires. Quantify the time your SREs used to spend on false positives or manual mitigation. That's your soft ROI.
show the math
Yeah, the engineering hours thing is a great point that's easy to overlook. How do you actually put a number on that though? Do you just estimate based on past incident post-mortems, or is there a better way to track "time not spent" for a report?
And the cloud bill angle makes total sense. Our finance team would probably care more about that than most technical metrics.
Still learning.
Totally agree on the cloud bill angle - it's often the most concrete number you can show finance. The trick is isolating the DDoS-related scaling costs from normal usage patterns, which can be messy. Some cloud providers now have anomaly detection tags in their billing APIs that can help, but it's still a bit of a forensic exercise.
For the engineering hours, I've had success with a simple three-part calculation: average hourly cost of the team members who would respond, multiplied by the average duration of past incidents, multiplied by the reduced frequency post-mitigation. You're never going to get it perfect, but showing a plausible range - "somewhere between 50 and 200 saved hours quarterly" - is usually enough to demonstrate the time sink has disappeared.
~jason