I've been evaluating so-called AI SOC platforms for the last quarter, and I'm reaching a state of genuine frustration. The marketing materials are full of promises about autonomous triage and agentic response, but when you actually engage with a sales team to understand what it will cost to deploy their magic box, you're presented with a spreadsheet of hieroglyphics. It's not just complex; it feels deliberately opaque.
Let's take a classic example: pricing based on "analyzed events per second." This seems straightforward until you realize their definition of an "event" is not the same as your SIEM's. Is a single enriched alert with ten contextual logs from your EDR, firewall, and identity provider one event or eleven? The vendor demos always show a simple, isolated alert. They never show the sprawling, nested data their platform ingests to perform its "analysis," and you can bet that sprawl is what they're counting. Then you have the "AI agent credit" models, where a single automated investigation step consumes a credit. Fine, but what constitutes a step? A simple enrichment API call? Or only when the LLM decides to branch its logic? The lack of standardization means you're signing up for a financial black box.
And don't get me started on the licensing of the underlying models themselves. Some vendors bake it in, others charge extra for "premium" models (GPT-4 vs. Claude 3, etc.), and they all swear their proprietary fine-tuning is essential. But this creates a triple-layered lock-in: you're locked into their platform's workflow, their specific model tuning, and their ever-changing pricing calculus. Try to forecast your costs when a major incident hits and your "analyzed EPS" and "agent credits" skyrocket. You're essentially writing a blank check.
The most galling part is that these tools are sold as reducing analyst workload, and therefore cost. Yet, to even understand the bill, you now need a dedicated finance person and a solutions architect to model out scenarios based on hypothetical attack volumes. The total cost of ownership suddenly includes a forecasting team. I'm looking at the open source ecosystem—the security-focused LLMs, the orchestration frameworks—and wondering if the real "AI" here is in the vendors' ability to artificially complicate value extraction.
Just my two cents
Skeptic by default
You're absolutely right about the unit definition problem. It's the same issue we had a few years back with cloud data pipelines charging per "processed GB" - everyone's processing multiplier was different.
The "AI agent credit" model is particularly difficult to forecast. Without a standardized unit, your cost becomes tied to your alert complexity in ways you can't predict during procurement. I've seen contracts where a multi-step enrichment against six external threat intel sources counts as one "investigation," and others where each API call is a separate credit.
Have you asked any vendors for a detailed log of credit consumption during a proof of concept? Some will provide it, and the variance between what they demo and your real workflow is usually telling.
Your bill is too high.
Oh, the "processed GB" comparison is perfect. We just got burned by that with a cloud ETL tool last year. Our bill tripled because their "processing" included compression and decompression cycles we didn't account for.
Asking for the detailed credit log during a POC is such a good idea. I'm going to try that next time. But doesn't that put you in a weird spot where you're basically auditing their pricing model before you even buy? Feels like they should just be transparent from the start.
So, do you think the lack of a standard unit is just vendor lock-in by another name?
rookie