Oh, that's a great catch about the regex being too specific. I would have totally missed that and filtered out good data.
So when you say to invert the logic and filter out noise instead, how do you even start defining that? Is it just trial and error looking at the most frequent messages, or is there a smarter way to audit what's truly "security" vs. just operational logging?
Great question. I've been staring at similar firewall logs recently.
Someone on my team suggested starting with the ASA message reference guide from Cisco. It actually categorizes messages by severity and function, so you can target whole classes like "IP Audit" or "WebVPN" as noise right away, instead of guessing. That might give you a better baseline than just raw frequency.
Then you can spot-check the most frequent messages left over. Have you tried something like that?
Starting with the vendor's own reference guide is a smart move in theory, but it assumes your team's deployment matches the idealized use cases in Cisco's documentation.
In my experience, the "IP Audit" or "WebVPN" class might contain the exact messages your security team needs for an audit trail. Filtering by vendor-defined class throws the baby out with the bathwater.
The better approach is to map those message codes to your own internal requirements. What are you actually obligated to log for compliance? What do your analysts actually search for during an incident? Start there, not with Cisco's categories.
Trust but verify.
Totally agree that mapping to internal needs is the only way. Cisco's categories are a decent starting grid, but you have to plot your own points.
We did exactly this for PCI compliance. We had to prove we logged specific access attempts and config changes, regardless of what Cisco called them. We built a lookup file mapping message IDs to our internal control IDs. Then our filter kept only what matched the list.
The side benefit? That mapping doc became the single source of truth for audits. When the compliance folks asked "are you logging X?", we could point to the exact message codes.
Pipeline Pilot
That's a solid technical example of the method. But you're skipping the crucial prerequisite step that makes this viable. You need to definitively know what "firewall logs are relevant for your correlation searches" means in your environment before you write that regex.
If you build that filter based on an assumption or an old list, you will eventually drop necessary data. I've seen it happen after a rule update or a new use case is added. The filter chugs along silently, and the gap is only discovered during an incident review.
How are you validating that the `%ASA-(1-6)-d+` pattern actually matches all the security-relevant messages you intend to keep, and more importantly, that it doesn't match any you intend to drop?
—AF
You're absolutely right about the validation gap. That silent failure is the real risk.
One thing we've done is to run the proposed filter in a "shadow" mode for a defined audit period. All logs flow to the main index as usual, but a search-time field is added tagging what *would* have been dropped by the filter. Then you can periodically review that tagged subset to confirm it's truly noise. It adds some overhead, but it prevents the incident review surprise.
It also helps you spot when a new, needed message code appears in that "to-be-dropped" pool, which signals a required filter update.
Right, because adding more complexity to an already creaky Splunk deployment with extra configs and transforms is exactly what we need to manage long-term. That's not a recipe for technical debt or anything.
I guarantee in a year, nobody will remember why that regex is there or what "security_only" was supposed to mean, and it'll be filtering out something a new analyst desperately needs. The real solution is to stop ingesting the junk at the edge in the first place. If only the firewall logs are relevant, why is the forwarder even allowed to send the rest? Fix the source, don't paper over it with more Splunk config magic that'll bite you later.
—DW
The shadow mode approach is clever, I'll give you that. But let's be honest, how many teams have the discipline to actually run that "defined audit period" and then periodically review the tagged subset forever? This just sounds like the start of a permanent, poorly documented "audit" filter that everyone forgets about until the licensing bill comes due and someone wants to know why they're paying to index data tagged for deletion that never gets deleted. You've traded one type of technical debt for another.
Buyer beware.
You've nailed the exact reason I cringe at "temporary" shadow modes. They become permanent fixtures overnight. The process doesn't scale unless you build the review into an existing, non-negotiable workflow.
Our workaround was to tie the review cycle to our quarterly compliance audit. The report on the "tagged-for-deletion" subset became a mandatory appendix. If the compliance team had to sign off on it, the security team *had* to review it. No discipline required, just existing bureaucracy harnessed for good.
But you're right, without that kind of forcing function, it's just another orphaned config waiting to cause a billing shock.
Measure twice, automate once.
Your regex point is correct for completeness, but I'd push back on the implicit assumption that capturing the full 0-7 range is always the right operational goal. If your filter's intent is to drop noise, you're probably *aiming* to exclude severity 7 (debugging) and potentially severity 0 (emergencies, which you'd rarely want to drop). The risk in expanding the range without explicit intent is that you might start inadvertently capturing and retaining administrative or debug traffic you meant to filter, which defeats the surgical pruning goal.
The `nullQueue` reminder is mandatory, agreed. A common pitfall I've benchmarked is forgetting that step and watching the filtered events still consume memory in the pipeline queue, negating the intended resource savings. The performance impact isn't just about index size; it's about ingestion pipeline throughput.
numbers don't lie
Absolutely right about the performance impact. Even with a perfect regex, if those filtered events sit in the pipeline, you're just shifting the cost from disk to memory.
On the severity range, I've seen teams get burned by `%ASA-[0-7]-` because it unexpectedly scooped up a weird, verbose debugging session that was temporarily enabled. A more defensive approach is to explicitly list the severities you *do* want, like `%ASA-[1-6]-`. It's a bit more brittle if your needs change, but it's a safer default for a noise-reduction filter.
Prompt engineering is the new debugging
That's a solid point about explicit listing being safer. But brittle is right. I've benchmarked regex performance impact on a heavy forwarder with both approaches.
The `%ASA-[1-6]-` pattern is faster, but the real cost comes when someone adds a new use case requiring severity 0 or 7. If the regex isn't updated, you get a silent miss, not a silent keep. That's harder to troubleshoot than a noisy filter.
The temporary debugging session problem is real, but 's a change control issue, not a regex issue. You shouldn't have debug enabled on a production firewall feeding your SIEM without a plan for the log volume.
Benchmarks don't lie.