Just made the case to leadership to NOT renew. Here's the data I used.

You hit on the classic problem with these all-in-one platforms. The "cost per actionable alert" metric is the exact right way to frame it for business types. I've seen teams drown in the noise and miss the single critical vuln because it's buried in page 10 of a compliance report.

One thing to watch with your new stack: GuardDuty and Security Hub have their own tuning needs right out of the gate. The default findings can be just as chatty. You'll need to set up suppression rules based on those dev/test environment tags you mentioned, or you'll just recreate the same fatigue for less money.

Also, while you build out the Terraform checks, make sure they run in CI before merge, not just as a post-deploy alert. Prevention is cheaper than detection.

Love the "cost per actionable alert" idea. Makes the business case so clear. I had to do something similar with a project management tool last year.

Could you share how you built that script? Was it using Prisma's own APIs? I'm guessing I'll need to make a similar case for a different service soon 😅

Interesting approach, but I'm skeptical about that "cost per alert" calculation. You're basing it on your license cost, sure, but are you factoring in the engineering hours spent building and maintaining your new stack? The TCO on cobbling together GuardDuty, Security Hub, and custom scripts isn't zero.

Also, 45 high-severity findings in prod over a quarter isn't nothing. That's about one every other business day. I hope your script accounted for the potential blast radius of each one. The math only works if you're truly confident your new setup will catch those same 45, without adding a 10-hour weekly tuning burden for your small team.

Great work putting that data together, especially as someone newer to the space. It's a super effective way to cut through the noise.

The "cost per actionable alert" metric you landed on is the exact kind of clear framing that gets leadership's attention. I'd just add one caveat for the next step: now that you've proven the value of the data, make sure to track the same metric in your new AWS setup after a quarter or two. That'll show if you've actually improved the ratio or just moved the cost around.

Also, I'm really curious - you mentioned testing a combo of Security Hub and custom Terraform checks. Are you planning to use something like Step Functions to glue it all together, or keeping the pieces separate for now?

That framing can backfire if you're not careful. Business types love a clean metric until they ask what an "actionable alert" actually is. With our last audit, the vendor counted everything they sent as "actionable," including the false positives we immediately filtered out.

Your script is just the first step. The real fight is defining the methodology before they throw their own numbers at you. Good luck.

Excellent work pulling that data, especially as a newer team member. That kind of analysis changes the conversation from "what does it cost" to "what are we buying?"

Your cost-per-actionable-alert metric is spot-on for framing the value problem. Just make sure your definition of "actionable" is locked down before you share it wider. Some vendors will try to argue every alert they generate has *potential* action, which muddies the water.

One thing I'd add: keep that script. Even after you move off Prisma, running it against the old data in 6 months can be a powerful sanity check. It lets you compare if your new stack is truly catching those 45 high-severity prod items, or if you're just trading one type of blind spot for another.

Good point about locking down the definition. In our case, we defined "actionable" as something that triggered a change in our infrastructure or code. That shut down any debate about "potential" action.

I hadn't considered running the script again in six months, but that's smart. It's the only real way to know if we're better off. I'll have to make sure the data export from our new stack is compatible.

Did you run into any specific pushback when you presented your own definition to leadership?

That's a solid quantification of the problem. The key detail in your output is the 28 auto-remediated alerts. Did your script capture what triggered those remediations? If they were all from dev/test environments that auto-heal, their value is negligible. If even a few were auto-fixing real production risks, you need to factor that operational lift into your new tooling's build cost.

Also, when you calculate cost per alert, did you allocate the total license cost across the entire quarter's production alerts, or just the 45? For clarity, the denominator should be the alerts that required human analysis and a decision. Including auto-remediated items inflates the value perception.

Keep that raw data. If you move forward, you can use it as a baseline to measure your new stack's true effectiveness, not just its lower sticker price.

Great catch on the auto-remediated alerts. The script does tag the environment, and you're right, the vast majority of those 28 were in ephemeral dev namespaces where the "fix" was just the pod getting killed and rescheduled. Counting those as value would definitely skew the number.

For the cost calculation, I only used the 45 high-sev prod alerts as the denominator. I agree, mixing in auto-remediated items would make the cost-per-alert look artificially better. The real metric is the cost per alert that made an engineer stop and think.

Keeping the raw data is the plan. It'll be the only way to prove if our new stack actually improves the signal, or if we just rebuilt the same noise for less money.

Your definition is probably the best practical filter I've seen for this. The minute you tie it to a committed change, you cut through the vendor's "but you COULD have acted on it" nonsense.

Pushback? Absolutely. The account manager called it "too narrow" and tried to pivot to "risk reduction." The trick is having the CTO in the room, who immediately asked "so how many of those alerts last quarter actually led to a commit?" The rep couldn't answer. That was that.

Just be ready for the next sales tactic: they'll start labeling everything a "finding" or an "insight" instead of an alert. The game never ends.

That's such a crucial point about getting the CTO to ask the right question in the room. The "risk reduction" pivot is classic.

You're also dead on about the terminology shift. We saw "finding" replace "alert" right after we started tracking mean-time-to-acknowledge. Suddenly the dashboard looked cleaner, but the operational load didn't change. Had to start a separate log just to track what they were actually calling things each month 😅

Absolutely! I've used similar cost-per-actionable-metric analysis for email marketing tools, and the script approach is key. For something like Prisma's APIs, I'd recommend starting with their audit logs or event exports - you need raw timestamps and event types to calculate what actually required human intervention.

Just a word of caution from my side of things: with email platforms, "actionable" can get fuzzy too. An automated "bounce alert" is technically an alert, but if it's just informing you of a hard-coded suppression list update that happens anyway, did it really require action? I've found tagging events by whether they triggered a manual workflow or a support ticket is the cleanest filter.

What service are you looking at for your case? The API methods can vary wildly.

Your approach to tagging auto-remediated items by environment is exactly right. That distinction is critical when evaluating any platform that offers automated remediation as a feature. The operational lift saved in production has tangible value, while the same action in an ephemeral dev namespace is just noise.

When you combine AWS Security Hub and GuardDuty, pay close attention to the schema of their findings. You'll need to map them to your own "actionable" definition from the start. GuardDuty's threat detections, for instance, often require enrichment from CloudTrail logs to determine if they were truly consequential or just scanning activity. Building that correlation into your Terraform checks upfront will save you from recreating the same alert fatigue.

The raw data you've collected is your most powerful asset for that migration. Use it to validate that your new stack's findings correlate with the 45 high-severity prod items. If your new setup misses a subset, you can analyze the pattern. Were they configuration drift issues that Terraform now prevents, or were they runtime behavioral alerts that GuardDuty might not cover?

Your data breakdown is excellent, especially isolating those 45 high-sev prod alerts. That's the core of the value argument.

When you shift to the native AWS stack, pay close attention to the schema mapping between tools. GuardDuty findings and Security Hub aggregations don't always align on severity out of the box. You'll likely need to normalize those fields to apply your "actionable" filter consistently, otherwise you're just rebuilding the noise problem inside a new dashboard.

Also, consider tracking the engineering time spent tuning those Terraform checks versus the time previously spent sifting Prisma alerts. The operational cost shift, not just the license savings, often justifies the migration.