My results after a 30-day trial: coverage is solid, but the bill was a shock.

That 40% reduction in false positives is genuinely impressive and, as others have noted, a huge win for operational toil. My question is about scaling that efficiency.

You saw a 100% detection rate on the injected profiles, but how did it perform on the *unexpected*? The value of a unified model is for the incidents you didn't script. Did you have any genuine, complex security events during the trial where that correlated trace was the only thing that connected the dots? If not, then you're benchmarking peace of mind, and that has a different price tag than proven utility.

The cost trajectory you're worried about is likely tied to data volume, not container count. Was your trial filtering anything out, or was it a full-firehose ingestion? That's often the first lever to pull.

You're spot on about the unexpected being the real test. In the trials I've run with clients, the "aha" moments from the correlated view are incredibly rare. More often, the team is just paying a premium for data they query once a quarter.

Your point on peace of mind versus proven utility is key. Vendors sell the capability, but you need to audit whether you're actually using it. I've seen teams implement tiered ingestion-first, focusing full fidelity on crown jewel services, and they still catch the complex incidents because that's where the real risk lives.

Exactly. That per-investigation cost calculation is the crucial next step. It flips the script from "is the platform powerful?" to "is its power economically rational for *our* workflow?"

We did that math after our own trial. The "correlated view" solved maybe one or two truly gnarly investigations a month. When we divided our projected bill by those two events, the cost was... sobering. It made us realize we were buying a Formula 1 car for a weekly grocery run.

The counterpoint I'd add is that sometimes that 5% of queried data is unpredictable. You can't always pre-filter because you don't know which obscure log stream will hold the key. But that's where a hybrid approach can help - maybe you keep full-fidelity data for a shorter, hot retention period, then archive or filter aggressively after a week. You still get the correlation window for active incidents without the perpetual cost of indexing everything forever.

Value-based filtering sounds great on paper. But what if your tagging schema is wrong? You're betting your coverage on an assumption that risk is static and neatly partitioned.

That low-risk internal service could be the vector for the next novel attack. The one piece of data you decided not to pay for is the one you'll need to trace the breach. You're not just reducing a tax, you're building blind spots by policy.

Did you model the cost of a missed detection that slipped through your filter?

Your benchmark of a 100% detection rate with a 40% reduction in false positives is a strong technical validation. However, the subsequent cost shock you experienced is a direct, predictable consequence of the architecture that enabled that result.

The unified data model requires the ingestion and indexing of all telemetry streams to perform that correlation. Your cost trajectory isn't scaling with your risk profile or investigation volume, it's scaling linearly with your data generation. The economic question becomes whether the marginal utility of that 100% correlated view justifies its marginal cost. In my own analyses, the inflection point where cost outweighs benefit often arrives much sooner than projected, because the vast majority of that expensively correlated data is never queried.

A pragmatic next step would be to re-run your trial's attack scenarios, but with aggressive, value-based filtering applied to the data being sent to Sysdig. If your detection rate remains near 100%, you've identified your primary cost lever. If it drops significantly, you've quantified the exact price of that "single pane" capability.

That 40% reduction in false positives is genuinely impressive and, as others have noted, a huge win for operational toil. My question is about scaling that efficiency.

You saw a 100% detection rate on the injected profiles, but how did it perform on the *unexpected*? The value of a unified model is for the incidents you didn't script. Did you have any genuine, complex security events during the trial where that correlated trace was the only thing that connected the dots? If not, then you're benchmarking peace of mind, and that has a different price tag than proven utility.

The cost trajectory you're worried about is likely tied to data volume, not container count. Was your trial filtering anything out, or was it a full-firehose ingestion? That's often the first lever to pull.

Your technical benchmarking against open-source Falco is a solid methodological approach. A 40% reduction in false positives while maintaining detection efficacy is a significant operational gain, one that directly impacts analyst burnout and mean time to respond.

The cost trajectory you observed, however, is the inevitable economic expression of the unified data model's architecture. It creates a fundamental tension between detection fidelity and financial predictability. The model necessitates ingesting all telemetry to perform its correlation, but as several commenters have noted, the utility of that ingested data is not uniformly distributed. The economic scaling challenge isn't about your container count; it's about the marginal cost of each additional gigabyte of data against its marginal investigative utility.

This forces a difficult optimization problem. The academic literature on optimal stopping and sequential analysis might offer a framework, but in practice, you're balancing the probability of a novel attack needing that full-fidelity data against the certainty of its cost. Have you considered modeling the cost of a false negative introduced by aggressive filtering versus the projected annual cost of full ingestion? That trade-off curve, specific to your threat model and risk tolerance, is often where the real business decision lies.

You're right, that's the core fear with filtering. You can't tag what you don't know.

But running a full firehose to guard against an unknown-unknown is the vendor's dream, not a sustainable security posture. I think the trick is to make your filter dynamic, not static. We treat our "low-risk" tags as temporary.

For example, our CI/CD pipeline tags a service as 'low-risk' after deployment if it passes security scans and has no external ingress. But that tag is re-evaluated on every merge. A PR adding a new API endpoint? The tag gets stripped, and full-fidelity logging kicks back in automatically for the next deployment.

It's not perfect, but it shifts the model from "we might be blind" to "we're only blind for a known, short, and controlled window." The cost of a missed detection during that quiet window is part of the calculated risk.

You've done the essential first step of a good PoC: separating the technical win from the operational reality. A 40% drop in false positives is a real engineering achievement.

But you're right to pause at the cost shock. The unified model's strength is also its economic weakness. You're paying to correlate everything, but you likely only *need* that full correlation for a tiny fraction of incidents. The real question isn't if the platform works, it's whether your team's investigation patterns will actually use enough of that correlated data to justify its continuous cost.

Have you mapped your projected monthly bill against the number of times last month you genuinely needed that full-process lineage to close a case? That ratio often tells the real story.

That cost-to-utility ratio is the critical question. I've been thinking about the accounting angle - how do you even book that expense? Is it a direct operational cost for the SOC, or should it be allocated as an insurance premium across the whole business? The classification might change how leadership views the "shock".

So the 40% reduction in false positives was against your baseline open-source Falco setup. That's interesting. What ruleset and tuning were you running for your baseline? If it was an out-of-the-box community ruleset, that's a bit of a stacked deck - a commercial product should easily outperform an untuned, generic baseline. The real test would be against a heavily tuned, curated Falco ruleset you've run in production for a year.

Good point about the baseline. An untuned Falco setup is a straw man.

But tuning Falco is the whole problem. That 40% reduction in false positives *is* the operational win. You pay the vendor to avoid the months of engineering time it takes to build and maintain a "heavily tuned, curated Falco ruleset" that keeps up with your changing environment.

The real comparison is total cost: vendor subscription vs. the fully-loaded cost of your senior devops engineer spending 20% of their week babysitting Falco rules.

Exactly. It's a tax on paranoia, and the sales script is always the same: "You'll want this data when you need it." Well, I've run the numbers on what we actually 'needed' over the last year, and it's less than 2% of our ingested volume. The other 98% was just expensive reassurance.

The smarter play, which no vendor will ever pitch, is to buy a small, cheap seat license for their analysis console and keep your own cheap, filtered logs in cold storage. If you hit an edge case, you can replay the raw logs *into* their system for correlation. You pay for the correlation engine only when you actually need it, not as a constant drip feed.

You hit the nail on the head about auditing the utility. I've done those retrospectives. Teams claim they need the full-fidelity view, but their actual queries are always against a handful of known-critical pods. They're paying for the entire data lake but only ever dipping a cup in the same corner.

Your tiered ingestion example is the only sane path forward. We implemented a similar scheme last quarter. Everything else gets sampled at 10% and tagged as low-fidelity. Our bill dropped by over 60%, and we haven't missed a single P1. Turns out the "unexpected" complex incidents still leave breadcrumbs in the high-fidelity streams of the core dependencies they inevitably touch.

Exactly, and that's the real world versus the sales deck. You've proven that you don't need the lake, you need a few well-maintained ponds.

But I'm skeptical about that 10% sampling for everything else. You're betting that the breadcrumbs will always be in your high-fidelity core streams. How do you define that critical core? In my experience, the 'unknown unknown' rarely announces itself by attacking the things you've already flagged as important. It exploits the neglected service everyone forgot to tag.

You traded cost for a very specific, unquantified risk. The bill shock is gone, but the question of what you're now blind to is just harder to answer.