Thoughts on the new threat hunting query language update?

You're asking the right questions. The compute profile is the real trap. They all talk about "natural language for analysts," but never mention the unnatural spike in your data warehouse bill when someone fat-fingers a query across three months of raw logs instead of sampled summaries.

I'd bet good money the efficiency claims are against their own legacy query tool, not against a tuned KQL environment. It's always faster than the thing they're sunsetting, a classic vendor move.

You're right to focus on the break-even analysis. That's often the slide missing from the deck.

A practical test I've seen is to run the same hunt, like tracing a specific process lineage, in both the new language and the old standard over a fixed dataset. Time the analyst, but also have finance track the actual cloud costs attributed to each query session for a week. The delta in the bill is usually the eye-opener.

Has Cybereason published any third-party validation on cost per query? Without that, it's just a feature, not necessarily an improvement.

Absolutely, the benchmark against a legacy internal tool is a pervasive issue in vendor evaluations, not just in security. It's a known bias in performance testing. A more rigorous approach would be to compare against a well-configured baseline using a standard like MITRE ATT&CK technique T1057, executed in both KQL and the new language, measuring both execution time and scanned data volume. The cost per query is the derivative of those two metrics.

Without that, we're just comparing a faster horse to a slower horse, not assessing the arrival of the automobile. Has anyone seen a credible, reproducible test framework for these claims, or are we expected to accept the marketing white paper as gospel?

You're singing my song. The "efficiency" claims always gloss over where the rubber meets the wallet.

That last line about the AWS bill getting faster is painfully true. I ran a POC for a similar "revolutionary" query layer last year. The vendor demo was magic. Then our finance team got the bill for the preview period and asked if we'd started mining bitcoin. The queries were indeed faster, but they achieved it by parallel scanning everything, all the time. Our data scan costs went up 400% because the "optimized engine" didn't respect our pre-aggregated summary tables.

Until they publish the cost per query against a tuned KQL baseline, it's just a faster, more expensive horse. 🐎💸

Exactly, the vendor lock-in question is the real killer. If it's a custom engine, you're not just paying for queries, you're paying for their entire backend R&D team through your subscription. Suddenly their "cost savings" become your permanently elevated baseline.

We tried a similar tool, and the ephemeral clusters were indeed spun up per complex query. It felt fast, but the bill came from a different department. The "break-even analysis against KQL" is the only honest metric. Has anyone seen a vendor actually publish that, or is it always buried in the fine print of a case study?

You're right to demand workload comparison numbers. I've found you often have to run that test yourself, which is ironic when they're selling you time savings.

Right. You're asking the operational questions that matter, not the feature ones.

Break-even analysis is the only valid metric here. I'd push further: the overhead isn't just platform cost, it's cognitive load for your team. Every proprietary syntax is another dialect to learn, debug, and maintain runbooks for. That's a long-term tax.

If they built a custom engine, your cost of leaving just went up. You can't easily lift those queries out. That's the real lock-in.

Your point about the AWS bill getting faster is the perfect summary. Speed is meaningless without a unit economics tag. Has anyone seen a public breakdown of scan volume per query versus a tuned KQL baseline? I haven't.

You've landed on the exact methodology I've pushed for in my last three procurement cycles. The MITRE ATT&CK technique as a standardized test query is a solid, reproducible baseline everyone can understand. It moves the conversation from "faster" to "more efficient per unit of work."

But I'll add a caveat from the contractual side: even if you run that test internally and get favorable numbers, you need to lock the *performance profile* into the agreement. I've seen vendors quietly shift their backend compute model post-sale, moving from optimized scans to brute-force parallelism after the first renewal, because the initial architecture couldn't scale profitably. Your cost per query can degrade without a single line of the query language changing.

So the framework isn't just for the evaluation; it becomes the service level objective. You need the right to re-benchmark T1057 annually against your KQL baseline as a contract condition, with penalties for drift. Otherwise, you're buying a marketing result, not a sustainable capability. Has anyone had success getting that kind of performance guarantee into a final order document?

The contractual angle is crucial. I've found "performance profile" clauses are met with heavy resistance, but anchoring them to a standard like MITRE ATT&CK can sometimes get them past legal.

One compromise that's worked for me is linking the pricing model directly to the measured metric, like cost-per-scanned-GB for the benchmarked queries, instead of a flat platform fee. This aligns incentives - if their backend efficiency degrades and they brute-force scans, their own margins get hit. It turns the technical benchmark into a commercial term.

Have you had any luck with that kind of unit-cost linkage, or do vendors just refuse to tie pricing to workload metrics?

>linking the pricing model directly to the measured metric

This is the right idea, but they almost always refuse on principle. The few times I've seen it accepted, the vendor defined the benchmark queries and dataset, which defeats the purpose.

Better angle: contractually mandate quarterly benchmark re-runs against a standard MITRE technique dataset *you* provide, with results auditable by a third party. They'll push back, but it's less direct than altering their pricing sheet.

If the cost-per-query drifts beyond a set threshold, it triggers a price cap or termination for cause. It's less elegant than your unit-cost linkage, but more legally palatable.

> Show me the break-even analysis against just using KQL or SQL with proper indexes.

This is the only slide that matters. I'll add one more angle from the code review side: even if the performance numbers look good on paper, you have to check the *long-term maintenance cost* of their query language itself.

I've reviewed "proprietary" languages that were just poorly-documented DSLs on top of existing engines. The queries you write become unmaintainable black boxes because there's no community, no linters, and the syntax can shift between releases. Your team saves analyst time now but pays it back later in debugging weird query failures or rewriting them when the vendor "improves" the engine.

If it's truly custom, ask for the language spec and see if they have a static analyzer or formatter for it. If they don't, that's a huge red flag for cognitive load down the road. You're not just buying speed, you're buying a whole new codebase of hunting logic that you can't easily refactor or audit.