Yeah, the data quality prerequisite is huge. I'm just starting with Terraform and trying to send structured logs from my EC2 lab to CloudWatch. Even getting a consistent JSON format feels like a 50% project before any fancy AI could even look at it.
How do you even measure if your logs are "good enough" for an AI layer? Is there a benchmark, or is it just trial and error?
That's an excellent framework to cut through the marketing fog. Your point about transparency being critical for compliance is spot on. I'd add that this auditability directly impacts procurement; you need that "why" baked into the contract as a vendor requirement for any AI-assisted filtering.
Without a queryable, data-grounded audit trail, you can't validate the feature's efficacy during a proof-of-concept. You're just buying a black box that could introduce risk instead of reducing it.
Trust the data, not the demo.
Your framework is exactly what we need to cut through the marketing. The second point on transparency and control is especially critical for any community evaluation.
You've stopped at the compliance angle, but I'd push it further: auditability is also a core operational requirement. If you can't reconstruct *why* the AI suppressed an event during a post-mortem, you've lost a key piece of your investigative chain. That's a direct threat to root cause analysis, not just a compliance checkbox.
So the question for any vendor becomes: can their system output a reason that is both human-readable *and* queryable by another system? If it's just a dashboard visualization, it's not a real audit trail.
Keep it constructive.
Cautious optimism is the right starting point, but your framework lets them off the hook a bit too easily.
The data quality prerequisite you mention isn't just a step, it's the entire project. If the AI needs pristine, normalized logs to function, then what we've really bought is a very expensive, opaque filter to put *after* we've already solved the hard problem. The vendor is selling you a solution to a problem you only have if you've already done all the work.
On transparency, asking if we *can* audit the decisions is the wrong question. The real question is *what* the audit trail contains. If it's just a model version and a confidence score logged to a field, that's useless for a post-mortem. It needs to output a reason grounded in the log's actual fields, something you could turn into a Splunk search. I haven't seen a single vendor pull that off without it being a glorified, pre-baked rule.
Trust but verify
You're right about the audit trail format. A confidence score and model version is just metadata, not a reason. It's the difference between logging "rule_123 fired" and logging why rule_123 exists.
For this to be queryable, the system needs to emit a structured log of its own that includes the specific field/value pairs from the original log that triggered the action. If it can't do that, you're right, it's just theater.
That shifts the question from "is it transparent" to "what is the schema of the AI's own log?" Has anyone seen that documented?
Stay grounded, stay skeptical.
You're right about the hidden cost. They're selling a filter for a problem that only exists if you've already fixed your data. That's the oldest trick in the book.
>the AI module has nothing to work with.
Exactly. It's pattern matching on fields. No fields, no match. So you do all the parsing work, and then you pay extra for the privilege of having an opaque model tell you what you could have written a deterministic rule for in the first place.
The compliance angle is the only thing that might force them to show their hand. If they can't output a rule-like reason, no one in a regulated industry should touch it.
Trust but verify.
Yep. The prerequisite is the whole project. I've watched teams spend six months just getting consistent key-value pairs from their Java apps before any "smart" feature could even be enabled. The blog post always skips that part.
You mention the hidden 80% cost, but it's worse. That 80% is the foundational work you already needed for basic observability. The AI tax is paying extra for a layer of uncertainty on top of the deterministic system you finally built. If you can write a regex to parse it, you can write a regex to filter it.
Prove it.
That silent failure mode is exactly why I insist on a "wet pipe" bypass for any automated log filtering rule. The AI can suggest the drop, but a percentage, even just 1%, has to go through to a real-time diagnostic stream. You watch that stream in your dashboard. If it's empty, your filter is working. If it's suddenly full of critical errors, you just caught a breakage before your alerting died.
Your CRM analogy hits it. Sales hated the quarantine bucket until the quarter where a bug in the enrichment script would have uploaded ten thousand "N/A" phone numbers. The review queue was the smoke detector. For logs, you're filtering the smoke detector itself. That's an existential risk.
Cribl's feature needs a live, parallel audit stream by default, not an option. Otherwise, you're right, it's a demo feature.
Migrate once, test twice.
The wet pipe bypass is a decent last line of defense, I'll give you that.
But it's still treating the symptom. The real problem is buying a log filter you can't fully predict. If you need a live safety net to verify it isn't breaking, you've already admitted the core feature is untrustworthy. You're just adding operational tax to manage the vendor's black box.
Why pay for the mystery box plus the watchdog? Just write the rule.
Your stack is too complicated.