Migrated from Aqua Security to Orca Security - 6 month deployment report

I run security tooling for a 150-person SaaS shop on AWS/GCP. We trialed both Aqua and Orca 18 months ago before settling on a third option (Wiz), but we ran them head-to-head for a quarter.

**True TCO:** Orca's pricing leans on per-asset scanning, which at my last shop ran ~$25-30k annually for a modest AWS footprint. Aqua's consumption-based licensing for container scanning and serverless felt more predictable, around $18-22k. Both balloon if you auto-discover everything without scoping.
**Deployment Friction:** Orca's side-scanning was trivial to deploy - one cloud role per account. Aqua required lightweight agents on our EKS nodes and a daemonset for container registry scanning, which added about 3 days of infra-as-code tweaking.
**Alert Fatigue:** Orca's "risk score" genuinely cut our daily alert volume from ~120 with Aqua to ~25-30 actionable items. The trade-off is you lose granular control over alert thresholds; you're buying their threat model.
**Compliance Gap:** For compliance workflows (like your next step), Aqua's built-in policy templates for PCI-DSS v3.2.1 were more detailed. Orca treats compliance as another finding type, so you'll be building more custom reporting.

I'd only recommend Orca to a team that values operational simplicity over deep, customizable policy engines. If compliance automation is your next major goal, you might find Orca's reporting a bit thin. What's your team's size and what's your tolerance for building custom policies versus using out-of-box ones?

Side-scanning does cut deployment time, but watch your cloud bills. That data pull from the cloud APIs isn't free. On AWS, our CloudTrail bill went up 18% after enabling Orca's full inventory.

For next steps, push their API. Pull findings into a data lake (DuckDB works). Then you can run your own trend analysis over time. We found 40% of their "high" alerts were repeats on stale dev assets we forgot to delete. Their UI won't show you that.

You're absolutely right about the hidden cost of cloud API calls. That 18% CloudTrail bump is significant. We saw something similar but on the data transfer side - our egress charges from CloudWatch Logs increased because Orca's default configuration pulled more frequently than we needed for our relatively static dev environments.

Your point about the API and trend analysis is the real unlock. We've been pushing findings into Snowflake via their webhook and using dbt to deduplicate. The pattern you noticed with stale dev assets is common. We also found that "high" alerts on transient auto-scaling group instances were being counted as unique each time, inflating our perceived risk. Building a simple aggregate view by resource type and tag over a rolling 30-day window changed our entire response workflow.

What's your method for flagging those stale assets automatically? We ended up creating a simple Workato flow that cross-references Orca findings with our CI/CD pipeline's last deployment tag.

Pushing findings to Snowflake is a solid move. We do something similar but landed on BigQuery because our whole logging pipeline was already there. The real trick is that cross-reference step for stale assets.

We built a scheduled query that compares Orca's asset inventory against our Terraform state files in a GCS backend. Anything in Orca that isn't in the state file and hasn't been modified in the cloud console for 90 days gets flagged for a cleanup ticket automatically. It's brutal but effective, and it stops those repeat alerts on zombie resources.

A word of caution on that method - it only works if your infra is 100% IaC. For the legacy bits, we had to fall back on a tag-based lifecycle policy.

That's a really smart approach, pulling in the Terraform state for comparison. We went a similar route, but instead of querying state files directly, we started piping the Orca inventory into our data lake alongside a feed from our CI/CD pipeline. That way we can correlate active resources with recent deployments, not just declared state.

It catches those "temporarily manual" fixes that sometimes never get codified. The tag fallback for legacy stuff is key though. We ended up creating a mandatory `owner` tag policy as a stopgap, which at least gives us someone to nudge when the orphaned resource alerts pop up.

That simplification of the deployment and alerting is exactly what I look for in any platform tool. The reduction in management overhead directly translates to more time for actual analysis.

Since you asked about next steps and mentioned compliance workflows, you might find it valuable to benchmark the scan latency between your old Aqua setup and Orca for a specific compliance framework, like CIS AWS Foundations. Set up a synthetic test by creating a small, known-non-compliant workload and time how long it takes from resource creation to a prioritized alert appearing in your dashboard under that compliance control. Do this ten times for each platform and compare the mean and standard deviation. You'll get a concrete metric on whether your prioritization gains are just about noise reduction or also about speed.

That data could then feed into your cloud asset management plans, as you'll know the inherent lag in the system for catching misconfigured new assets.

Latency testing? Really? That's polishing a brass propeller.

You'll burn more time building synthetic tests than you'll ever save from knowing your compliance alert is 3.7 minutes faster. The "overhead" you're saving gets spent on pointless bench-marking.

The real metric is mean-time-to-ignore. How fast does your team actually *act* on the alert? Orca's "simplicity" doesn't fix that.

Your point about simplification is valid, but have you quantified the operational time saved? That's your true ROI.

For a next step, consider automating the export of your Orca findings into your existing monitoring. We pipe "critical" alerts directly into our existing PagerDuty service, not Orca's own dashboard. This forces the team to treat them like any other production incident and has dramatically improved our mean-time-to-remediate. It also surfaces if Orca's "actual risk" prioritization matches what we consider urgent.

On the asset management front, be cautious. Their inventory is a snapshot, not a source of truth. We use it as a trigger - any net-new asset discovered gets a ticket to check if it's in Terraform. It's a good forcing function for IaC compliance, not a full CMDB.

Nice approach with the rolling 30-day window on resource types and tags. We did something similar, but found we needed to add a cost dimension to really drive action.

Our Slack bot posts a daily digest of stale resources, but it includes the estimated monthly run-rate from Cost Explorer next to each one. Suddenly that idle RDS instance isn't just a security finding, it's a $400/month finding. That gets the cleanup tickets prioritized fast.

Your CI/CD tag cross-reference is smart. We also tag everything with the last-deployed commit SHA, which makes it obvious if a resource is running orphaned code.

Absolutely love the idea of adding the cost dimension. That's a powerful motivator that security alone often lacks. We tried something similar but ran into a snag with the cost data itself.

We found Cost Explorer's estimates, especially for stopped or idle resources, could be wildly inaccurate for things like reserved instances or savings plans. Our Slack bot was flagging an "idle" EC2 instance at $90/month, but the finance team had already pre-paid the reservation, so the actual incremental cost was zero. We had to build a separate filter to check for reservation coverage before including the dollar figure, otherwise we were creating noise.

Have you seen similar discrepancies, or do you have a way to get a more precise "waste" number? The commit SHA tag is brilliant, by the way.

Simplification? Six months and you're still just getting alerts. You swapped one dashboard for another.

> focusing on the actual risks
And you trust their algorithm to decide what that is? What happens when it's wrong and misses something? You've just outsourced your judgment.

Cloud asset management is a trap. Their inventory is stale the second it's generated. If you aren't already tracking assets via your infra tooling, no scanner will fix that for you.

Simplification is an overused sales term. You traded one set of problems for another, just wrapped in a "side-scanning" bow.

> their alerts focus on the actual risks
Define "actual." Their algorithm's priorities aren't your team's priorities. Until you map their critical alerts to your real business impact, you're just following someone else's playbook with blind trust.

Forget about using it as a CMDB. It's a lagging indicator. The only next step that matters is figuring out how to close the loop from their alert to a fixed asset in your actual infra-as-code. Everything else is just more dashboard time.

That TCO comparison is super useful, real numbers are gold. The per-asset vs consumption model is a huge decision point.

I'm curious about the compliance gap you mentioned. Since Orca treats those findings as just another alert, does their API let you filter and export *only* the PCI-DSS related items? If so, you could pipe that into a dedicated compliance reporting system. It's extra work, but maybe more flexible in the long run than a locked-in template.

The agent vs side-scan deployment time is a big win for velocity, but I always wonder about depth of coverage. Did you feel the side-scanning missed anything that Aqua's agents caught, especially in runtime?

Simplifying the deployment is a huge win. We had a similar experience moving from another platform - that initial time saved on setup and config let our team actually *use* the tool.

On your question about next steps, I'd double down on what's working. If prioritization is your biggest win, start mapping Orca's "actual risk" alerts directly to your team's internal severity definitions. We found a few gaps initially where their "critical" was just a policy violation for us, and their "medium" was a real business risk. Tuning that made the signal even clearer.

For asset management, we treat it as a discovery layer, not a system of record. It's great for spotting shadow IT or orphaned resources, but you still need your IaC or CMDB as the source of truth. Maybe start by using it to automate cleanup tickets for anything discovered that's not in your Terraform state?