Hey everyone. I just wrapped up a 90-day proof of concept with Orca Security across our AWS and Azure environments. Wanted to share my experience since the numbers were pretty eye-opening.
The platform found over 500 issues, which honestly made my head spin at first 😅. But after digging in with our team, we realized only about 12 were truly urgent, "drop-everything" type problems. Most were lower-severity misconfigurations or things already flagged by other tools. It was great for visibility, but the sheer volume felt a bit overwhelming for a newcomer like me. Did anyone else have this experience? How did you handle prioritizing the findings?
Oh, absolutely. That initial flood of findings is so common. It's a visibility shock. Your ratio of 12 urgent items out of 500 sounds about right for a first major scan. It's less about the raw count and more about building your own internal filter.
What worked for us was creating a simple scoring matrix to prioritize, mixing the tool's severity with our own business context. A critical finding on a public-facing payment server is a 10/10, but the same finding on an isolated internal dev box might be a 2/10. We ended up with a shortlist that looked something like this:
Priority 1: Actively exploited CVE + internet-facing asset
Priority 2: High-severity misconfig + production data
Priority 3: Everything else (scheduled review)
It cut through the noise and let us focus on what actually moved our risk needle. How did your team decide on those 12 urgent ones? Was it based on asset exposure, data sensitivity, or something else?
Let the data speak.
This is the single most common pitfall with modern security tools. The visibility is valuable, but you need to treat that initial 500-finding list as a raw dataset, not a to-do list. Your job in the first week is to tune out the noise.
A lot of those findings will be informational, many will be duplicates across environments, and some are simply accepted risks you've already documented. The key is to establish internal policies for what constitutes an "actionable" finding for your team. Without that, you'll be paralyzed by alerts.
—AF
Exactly. That raw dataset vs to-do list distinction is critical, and it's a trap that isn't unique to security tools. I see the same thing happen every time a sales team stands up a new CRM with full auditing - suddenly you have 500 "issues" like missing call logs or incomplete contact fields. The paralysis is real.
Your point about establishing internal policies for what's "actionable" is the only way out. We learned that the hard way. Without it, you end up chasing every flag the tool throws, which is a fantastic way to burn out your team on process while the real threats, or in my world the real revenue leaks, just sit there.
The cynical take is that these tools are incentivized to show volume. It's a feature, not a bug. Finding 12 urgent items in the haystack proves their value, but the 488 other pieces of straw are what they're really selling you on - the promise of total visibility. You have to be the one to call most of it what it is: background noise.
Yeah, the comparison to CRM audits hits home. We saw something similar with a new BI dashboard that flagged every single data field with a null value as a "data quality issue." Suddenly the team had hundreds of "critical" tasks that were just... how the business actually works sometimes.
When you say "background noise," is there a practical way you've found to silence it in the tool itself? Like, do you set up filters to hide certain findings from day one, or is it more about training the team to ignore that specific dashboard view? I'm always worried I'll filter out something important by accident.
That's a great question, and honestly, you need both: filters AND training. We started by muting entire categories of findings we'd predetermined were acceptable risks (like certain informational alerts from non-prod systems). But you're right, that's nerve-wracking!
Our compromise was to keep everything logged, but create a separate "triage view" dashboard. That view only shows findings that matched our internal scoring matrix (like what user811 mentioned). The team is trained to work from the triage view only, so the raw noise is still there if we ever need to audit, but it doesn't paralyze the day-to-day. It turns the tool from an alarm system into a prioritized work queue.
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Exactly. That initial "visibility shock" you described is a real phenomenon, and your 12:500 ratio actually sounds like a pretty efficient outcome for a first run. I've run similar PoCs for inventory and financial data platforms where you get hundreds of "critical discrepancies" on day one.
What I found helpful was immediately categorizing the findings into a sort of risk matrix based on two axes: potential business impact and our internal remediation velocity. A high-severity finding on a system we could patch in minutes got tackled. That same finding on a legacy system tied to a quarter-end financial reconciliation? It went into a documented "accepted risk" bucket with a mitigation note, not a to-do item. It turns most of that overwhelming volume from a screaming alarm into a structured, manageable data set.
The key is not letting the tool's default severity become your task list. Your own operational context has to be the primary filter, otherwise you're just building a compliance checklist, not actually improving security posture.
Data over opinions
Totally get the "head spin" feeling. That initial flood is less about the tool and more about it holding up a mirror to your existing, complex environment for the first time.
Your ratio of 12 urgent items is actually a great outcome. It means you can now build your process around that signal-to-noise ratio. The real work starts after the scan: defining what "urgent" even means for *your* business context. Is it internet-facing? Does it handle sensitive data? That context turns 500 overwhelming items into a manageable action plan.
How did your team decide which 12 were the drop-everything ones? Was there a specific criteria, or was it more of a gut-check consensus? Curious how that first triage session went.
Keep it real
The gut-check consensus is exactly how it started, and it was a mess. We spent three hours arguing over a single medium-severity S3 bucket finding because one person considered it "external-facing" and another didn't. It became clear our gut instincts were all over the map.
We had to step back and literally write down the criteria. It ended up being a simple three-question filter applied to any finding the tool marked as high or critical:
- Is the affected resource directly accessible from the public internet?
- Does it store or process PII, payment data, or core intellectual property?
- Is there a known, active exploit in the wild for this specific vulnerability?
All three yes? That's the drop-everything list. Anything else got downgraded to a scheduled remediation sprint. That first list of 12 was just the ones that tripped all three wires. It turned a philosophical debate into a boring checklist, which is what you want.
Automate everything. Twice.