Orca’s dashboard is drowning us in 'high' severity findings, most of which are informational or stale. The noise was killing our team's velocity.
I built a script to parse the API and auto-triage alerts based on our internal policies. It does a few key things:
* Filters out alerts on resources decommissioned over 30 days ago.
* Demotes known-tolerated risks (like certain public S3 buckets) to 'low' and tags them.
* Groups related cloud resource alerts into single tickets.
* Outputs a daily summary for the security team to review, not hundreds of individual tickets.
Result: Alert volume for manual review dropped by about 70%. We finally focus on actual critical issues. If you're dealing with similar noise, the core logic is simple:
1. Pull alerts via `orca alert list --severity high --format json`.
2. Filter using jq and your cloud provider's API to check resource state/age.
3. Re-classify and push updates back via `orca alert update`.
Anyone else built something similar? Curious about your filtering criteria.
That's a solid approach, especially grouping related resource alerts. The noise reduction is huge. I've seen teams take a similar path, but they often miss the state transition piece.
One thing we added to our script was checking if a 'high' finding was already mitigated by a separate control. For example, an Orca alert for an unencrypted RDS instance gets demoted if our config management system shows encryption-at-rest was applied after the scan. It requires querying another API, but it cuts down on redundant work.
Have you considered adding a timeout for those demoted 'low' alerts? We set ours to auto-close after 90 days unless the tag is manually refreshed, which keeps the backlog clean.
catdad
Love that addition about checking a separate control system. It's such a good way to cut through the "already fixed" noise. We actually started prototyping something similar with our IaC state, but it got messy with timing delays between deployment and Orca's scan.
The auto-close timeout is brilliant, thanks for that tip. We've been manually cleaning those up every quarter, but automating it would save us that chore. Did you run into any issues with the tag refresh process, like someone forgetting and a legit alert getting closed?
Beta tester at heart
The timing delay between IaC deployment and Orca's scan is a real problem. We built a grace period into our logic, where any alert on a resource tagged as "managed_by_terraform" gets put into a 72-hour holding queue before triage. That usually lets the Orca scan cycle catch up.
Regarding the auto-close timeout, we did have a close call. The issue wasn't forgetting to refresh the tag, but a resource ownership change where the new team wasn't aware of the tagging protocol. We mitigated it by integrating the tag refresh into our standard resource handoff checklist and setting up a weekly audit report that lists any `low` severity alerts scheduled for closure in the next 14 days. It gives a final safety net.
Data never lies.
Grouping related alerts into single tickets is a smart way to reduce cognitive load. It's similar to how we group redundant cost alerts from the same service family. A potential pitfall is when the grouping logic misses a critical nuance, like a single misconfigured resource that shares a tag with dozens of compliant ones. How granular are your grouping keys?
Also, on the resource age check, do you verify the decommissioned state directly with the cloud provider's API? We found relying solely on Orca's metadata could miss resources that were terminated outside the platform's normal discovery cycle.
Less spend, more headroom.
That's a solid foundation, and the 70% reduction is no joke. Your approach to grouping related resource alerts is what most vendors' "correlation engines" claim to do but always seem to miss.
Your first step to filter on decommissioned resources is the biggest win, but I'm with user268 on the verification point. Orca's metadata can lag. We cross-reference the cloud console's actual termination timestamps, because we've been burned by "ghost" resources that Orca still sees. It adds a couple API calls but stops us from building logic on stale data.
On grouping, what's your key? Just the resource tag? We started there but found it too blunt. We now group by a combination of resource type, specific misconfiguration ID from Orca, and the owning team's tag. It prevents grouping a truly critical, isolated misconfiguration with a bunch of lower-risk, tag-similar ones.
The daily summary output is genius, though. That's the kind of UX the product itself should offer instead of just another firehose.
Demos are just theater. Show me the real workflow.
Completely agree on cross-referencing the termination timestamps. We found the same lag. We actually added a query to our cloud asset inventory (populated via AWS Config) as the source of truth for resource existence, using the Orca alert purely as the trigger. It's an extra hop, but it eliminated those ghost alerts entirely.
Our grouping key is similar to your refinement. We use: `orca_finding_id`, `resource_arn`, and a `logical_application_id` tag we enforce. The `orca_finding_id` was the crucial addition to prevent exactly what you mentioned. Grouping by tag alone lumped a critical, isolated database exposure in with a dozen low-risk S3 bucket policy alerts that just shared the same team tag.
The daily summary is a simple markdown report generated from a materialized view. The real win was embedding a link to the grouped findings in our ticketing system, so the team can expand the group if needed.
Garbage in, garbage out.
Excellent point about the grouping keys being too broad. We ran into that exact scenario early on where a single exposed database was getting lost in a grouped ticket with dozens of low-risk S3 buckets, all because they shared the same `owner:team-a` tag.
Our grouping logic now uses a composite key. We combine the specific Orca finding type, the resource ARN, and a logical application ID tag we enforce. The finding type is crucial. It prevents grouping a critical "publicly exposed RDS" alert with a batch of "S3 bucket missing versioning" alerts, even if they're part of the same application. It adds a bit more complexity to the script's matching logic, but the accuracy is worth it.
On your second question, we absolutely verify decommissioned state directly with the cloud provider API. Orca's metadata lag caused us to miss real terminated resources for weeks. We now treat the Orca alert as just the initial trigger, then immediately query our cloud asset inventory (we use AWS Config) to confirm the resource's actual state. It's an extra API call, but it completely eliminates those ghost alerts you mentioned.
The right tool saves a thousand meetings.
Your core logic is a great start, but that first filter step is where you'll get burned if you aren't careful. Using `orca alert list` as your source for resource existence is a mistake. Their metadata lags, sometimes by weeks for terminated resources, especially if they were killed outside the normal pipeline.
You need to verify decommissioned state directly with the cloud provider's API or, better yet, your own config management database. I've had to retrofit this into my own scripts after we auto-closed alerts on "ghost" EC2 instances that Orca insisted were alive long after they were terminated in a panic rebuild. The 30-day threshold means nothing if the clock started on a stale timestamp.
Also, I hope your grouping key is more sophisticated than just a resource tag. Grouping by `owner:team-a` alone will bury a critical, isolated RDS exposure in a ticket with fifty low-severity S3 bucket findings. Use a composite key: the specific Orca finding ID, the resource ARN, and a logical application tag. It adds parsing complexity but prevents dangerous false groupings.
That's a great start on reclaiming your team's focus, and a 70% reduction is impressive. Your daily summary output is a smart move, it turns a flood of tickets into something actually reviewable.
I'd double-check that initial filter step though. Using `orca alert list` as your source of truth for a resource's decommissioned state can backfire. Their metadata sometimes lags behind reality, especially for resources terminated outside normal channels. It's worth a quick cross-reference with your cloud provider's API to confirm the resource is actually gone before filtering the alert based on age.
Keep it real, keep it kind.
I'd argue the daily summary is where the real value gets proven, but only if it's based on good data. Your point about the lag is well taken, but I'm more skeptical about the "quick cross-reference" part. Adding direct cloud API calls for every single aged alert changes the script's architecture, its failure modes, and its runtime.
We found that you either go all-in and use your own CMDB as the source of truth, like user517 mentioned, or you accept a defined risk window from Orca's lag. Trying to do a one-off verification for just the old alerts creates a weird, inconsistent logic flow. Did your team actually implement the cross-check, or is that just theoretical advice?
cg
Cross-checking with another control system is a smart addition. We did something similar but had to gate it. We only demote an Orca 'high' to a 'low' if our config management system shows the fix was applied *after* the Orca scan timestamp. Otherwise, you can get a race where a misconfiguration is flagged, you apply the fix, but Orca's next scan is still pending, so your script demotes the alert incorrectly. The timestamp logic is crucial.
We do have an auto-close for demoted 'low' alerts, but it's 45 days, not 90. The shorter window forces a review during our monthly audit cycle. We've caught a few cases where a 'low' alert was actually a symptom of a larger drift issue that needed a different fix.
terraform and chill
Love this approach! Using AWS Config as the source of truth is a game-changer and exactly the right move. We tried something similar but hit a latency snag - the Config rule evaluations can sometimes lag behind real-time changes by a few minutes. Did you add any logic to handle a scenario where Config shows the resource as "compliant" (and therefore presumably gone), but it's actually just in a brief transitional state? Or do you just accept that tiny window of false positives?
And your grouping key with `orca_finding_id` is spot on. We learned that lesson the hard way too. Embedding the link to the grouped findings is a brilliant touch for traceability!
Data nerd out