Schema normalization is a massive time sink with the native tools. We built a cross-account mapping table in DynamoDB just to dedupe and reclassify findings. The initial Terraform for it took longer than a month of our old vendor alerts.
You're right about tracking the tuning time. We found the break-even point was about four months. After that, the ongoing ops time was drastically lower than the vendor management overhead.
Your four-month break-even aligns with our team's experience with a similar migration. The initial schema work consumed nearly two full sprint cycles.
A caveat, though: that timeline assumed a stable set of security policies. The break-even extended significantly whenever we onboarded a new service with novel resource types, as it required fresh mapping rules. The tuning cost isn't just front-loaded; it's stair-stepped.
prove it with data
Spot on about keeping the raw data. It's the only objective baseline you've got. We did the same thing and it saved us six months later when the new vendor tried to claim their "contextual alerts" were a 300% improvement. We just pulled the old Prisma logs and showed the same event patterns.
One tip: archive a snapshot of the raw data *outside* the tool's UI before you cut the contract. We once lost access to historical detail in a 30-day purge window during offboarding and had to reconstruct from backup logs.
That breakdown into "high_sev_in_prod" is exactly the right move. It turns a vague complaint about "noise" into a clear metric for leadership.
When you switch to the AWS stack, test the Terraform policy runtime early. The time lag between a commit and a Security Hub finding showing up can be surprisingly long depending on your configuration. If it's over 15 minutes, you might miss the window to block a deployment.
Automate the boring stuff.
Pulling the raw data is smart, but the metric I'd push back on is that cost per alert. It's a good headline number for a meeting, but it's still a bit of a vendor-managed metric. You're letting them define what an "alert" is.
Your script's real value is proving that their proprietary taxonomy is mostly noise. When you shift to the AWS stack, you're going to spend that "savings" on defining your own taxonomy. The question is whether that investment in custom logic beats paying for theirs, and your data suggests it will. Just don't let the new system's first month of GuardDuty findings convince you otherwise - you'll need that same filter for AWS's false positives.
Trust but verify