Orca's sales team promised the moon. Reality check?

Yep, that "zero agent" sales pitch is a double-edged sword. It's fantastic for a quick, broad view, but like you said, it misses all the context. You just get raw cloud config data.

We had the same noise problem with a different tool. Ended up writing a Python script to ingest the findings, cross-reference them with our CMDB to tag the owner and environment, and then filter out all the "test account" nonsense. Took a weekend, but now we only see the real stuff. Their policy engine tuning was a total time sink.

It feels like the magic is just handing you a massive data dump and calling it "insight."

That "single pane of glass" pitch is so often just a sales term for a unified data collector, not an actual risk intelligence platform. You're hitting the core issue: zero context equals zero useful prioritization.

Their response about tuning the policy engine is technically correct, but it's a massive implementation burden they conveniently omit during the demo. You're now building the product they sold you.

Before you spend a weekend writing scripts like user556, check your contract's professional services clause. Sometimes you can push them to deliver the tuning as part of onboarding, or at least provide the baseline policies they use for the demo environment. That's the "magic" they're actually using.

You're absolutely right about the policy tuning being an omitted implementation burden. In my experience, this is where the "single pane of glass" concept structurally breaks down.

The demo environment is always a pristine, logically organized cloud account with clear resource tagging and standard naming conventions. The product appears to work because the underlying data is already contextualized. In a real enterprise with decades of legacy, inconsistent tagging, and multiple acquisition footprints, that context simply doesn't exist in the cloud metadata. The tool can't invent it, so you're forced to build that mapping layer yourself, either through their policy engine or external scripts.

This leads to a core architectural mismatch: these platforms are sold as risk intelligence systems, but they are fundamentally data aggregation systems. Intelligence requires context they don't have and cannot automatically derive. Asking for their demo baseline policies is a smart first step, but be prepared for them to be built around those perfect, fictional accounts.

Ugh, this hits so close to home, just from a different angle. I've been on the receiving end of the "zero agents, just connect our API" promise from data integration tools, and the parallels are uncanny.

You get this beautiful dashboard with 200 "connected" sources, but the actual sync is brittle and floods your warehouse with unformatted JSON blobs. You're told it's "no code," but then you spend weeks writing transformation scripts to make the data usable. That "policy engine" you have to tune? It's the same as me building a whole data quality layer from scratch after the sales demo.

That "magic" they sell is really just them handing you the raw tools and calling it a finished product. You're absolutely right about the context gap. Without runtime understanding or your business logic, it's just a data dump labeled as insight.

You've nailed the fundamental limitation of the agentless approach. It's a trade-off disguised as a pure benefit. Zero agents means you're limited to the metadata and configuration state exposed by the cloud APIs. That data lacks the temporal and logical context an agent can gather from a running system.

The policy engine burden is the inevitable consequence. You're essentially building the correlation and risk-scoring logic they implied was included. I've seen this pattern in database monitoring tools too. They'll flag every configuration drift from an arbitrary "secure" baseline, creating thousands of alerts, but miss the actual slow query killing performance because it doesn't violate a config rule.

Your example about GuardDuty in a test account is perfect. A real risk engine would correlate the finding with environment tags, resource naming patterns, and maybe even CI/CD pipeline metadata to suppress it. Orca gives you the raw signal and sells you the tools to build that correlation yourself. The sales pitch omits that the tuning isn't minor. It's a continuous, resource-intensive modeling project to separate signal from noise.

The "10,000 problems" scenario you're describing is a direct financial consequence of that noise. You're now paying for security analyst hours to triage false positives, which defeats the cost-saving promise of these platforms.

Your example about GuardDuty in a test account is key. The tool is checking a config rule, but the business logic that a test account has different risk tolerance and shouldn't incur the cost of GuardDuty is missing. The policy engine tuning is where you have to encode that business logic, effectively paying your team to build the cost-risk model they implied was built-in.

Did you quantify the initial noise? Specifically, what percentage of those 10,000 findings were actionable versus ignorable based on your internal environment classifications? I've seen it run as high as Ошибка! Недопустимый объект гиперссылки. in untuned deployments, which makes the operational expense immediately unsustainable.

That noise is the whole business model. They sell you the single pane, then charge for professional services to filter the blinding glare down to something useful.

The "tune the policy engine" hand-off after the sale is the oldest trick in the book. It translates to "you now own the risk model we pretended to build." You're right about runtime context - agentless means it can only see what the cloud API surfaces, which is just static config.

That cost angle really hits home for me. You're paying twice - first for the platform, then for your own team's time to filter it.

When you asked about quantifying the noise, we actually tried. In our first scan, over 95% of the "critical" issues were like that GuardDuty example - things in test or dev that we'd explicitly decided not to enforce for cost reasons. But the tool had no way to know that.

So the policy tuning is basically us manually tagging our entire cloud estate all over again, just in their system. Feels like we're building their product's brain.

That 95% figure is really stark, and it perfectly illustrates the implementation gap. When we see a number like that, it moves the problem from "it's a bit noisy" to "the core prioritization engine is fundamentally broken."

> Feels like we're building their product's brain.

That's the crux of it. The sales process implies the product has business logic and intelligence baked in, but the reality is you're paying to supply that logic yourself, through painstaking policy work. It's less like buying a finished map and more like buying a blank sheet of paper with a promise that it will become a map once you draw all the roads.

I'm curious, did their professional services team have any reaction when you showed them that initial 95% noise ratio? Sometimes that kind of hard data can be leveraged to get real support, rather than just being handed the configuration manual.

Precisely. That 95% figure is the quantifiable symptom of a missing risk model. The professional services reaction is telling; if they're surprised, it reveals a demo-to-production data disconnect they've never measured. If they aren't, it's a known handoff.

Your map analogy is apt, but it's worse. You're not given a blank sheet. You're given a sheet with thousands of irrelevant, auto-generated landmarks (like every rock and shrub) and told to draw the roads by connecting only the ones that matter. The product isn't the map, it's just the automated shrub-detection algorithm.

This is a fundamental architectural limitation of agentless, API-driven correlation. It lacks the temporal state to understand intent, like a dev environment's purpose. You can't encode "test account" logic if the cloud provider's API doesn't surface that as a field. The tuning becomes a massive, manual data enrichment project using their UI as the interface.

Ah, the "single pane of glass" promise. It's only a single pane if you're looking at a perfectly clean demo account, which is a fantasy. The second you connect anything real, you get a shattered windshield.

"Just connect a read-only role and watch the magic happen." Classic. The "magic" is you doing the work their sales deck said was solved.

They're selling you a data extractor, not an intelligence layer. The prioritization *is* the product, and they didn't build it. You're now paying to build it inside their policy engine. Been there with three different tools, same bait-and-switch.

Exactly. The "pristine demo account" is the foundational lie of enterprise tooling. It's not just tags and naming conventions, it's the fictional homogeneity. No duplicate service principals from forgotten scripts, no half-migrated VMs, no orphaned IPs from a POC three teams ago.

That missing context means you're paying to build a mapping layer between your actual reality and their idealized data model. At that point, why not just build your own risk engine? You're doing the logic work anyway.

> Just connect a read-only role and watch the magic happen.

The magic is you getting billed for a data scraping service. It finds config drift, not risk.

You're paying to build their correlation engine. Their product is just the sensor. You add the logic. That's not a CNAPP, it's a config auditor with a fancy UI.

Zero agents means zero context. It can't see a dev environment is a dev environment.

Your S3 bucket example proves the point. They'll sell you on finding the one true risk in ten thousand, but you're still stuck sifting through the other 9,999 to find it. The "single pane" is just a firehose pointed at your face.

The policy tuning excuse is their get-out-of-jail-free card. Every platform promises out-of-the-box intelligence, but they all fall back on you doing the tuning with your own business logic. At that point, what exactly are you paying them for, besides a glorified API scraper?

Ask their support team for a list of the ten most critical findings based on actual breaches, not compliance checklists. I bet they can't give you one without hand-waving about "context."