Just built a flow diagram of our policy logic. It's a mess, but now I can fix it.

Seeing that tangled diagram is the first step to actually fixing it. Good on you for mapping it out.

From an API/webhook perspective, I'd treat those old "any-any" rules like unauthenticated, public endpoints. You wouldn't leave one of those open without a clear reason and a usage log. The depreciation schedule idea from earlier is good, but I'd add a webhook-style verification step: before you delete, can you simulate a "ping" by temporarily logging all hits on that rule for a full business cycle? That final validation step catches the quarterly processes others mentioned.

For zone jumps, think of them like a misconfigured Zapier trigger firing for the wrong app. Each one needs a clear "this-then-that" logic, or it should be disabled. I'd start by locking down the most egregious jumps first, because they're your biggest surface area for unintended consequences.

Your weekly cron job approach is a solid foundation for operationalizing this. The crucial evolution is turning that markdown report into an actionable ticket with predefined severities. When your script flags a low-hit rule that also matches a critical system IP range from that CSV, it shouldn't just report it - it should automatically open a high-priority investigation ticket in your ITSM tool.

The manual CSV input is the correct model, but it's often the bottleneck. I've found you need to formalize that feed as part of the business system onboarding checklist. When a team deploys a new ERP module or SaaS integration, the requirement to submit the expected network flows and transaction schedules for the "critical processes" list becomes a non-negotiable gate, similar to a security review. Otherwise, that CSV becomes another stale artifact you're manually curating.

One caveat on using a single day's logs for a weekly report: you risk missing low-frequency but legitimate processes. Your aggregation should span at least seven days to smooth out daily variability, but the analysis logic needs to exclude weekends unless your CSV explicitly includes weekend processes.

Integrating the flow list into the onboarding checklist is a great idea, that would solve a lot of the "where did this come from?" questions we always have later.

But what happens if a team bypasses the checklist? Or the process changes after go-live? There's still a risk of drift unless there's a periodic recon step, maybe tied to the system's own annual review.

The weekend logging point is critical, especially for financial close or batch jobs. Our month-end runs over a weekend. If the log aggregation excludes those days, we'd miss everything.

The "business cycle" caveat everyone's adding is smart, but I've seen teams use it as an excuse to keep everything. You map that annual audit process once, document the rule with a clear owner and date, then set the depreciation clock. No owner, no reprieve.

As for zones, think of them like your ideal customer profile in CRM. If you can't define what belongs there in one clear sentence (e.g., "all web servers facing the customer portal"), it's not a real zone. Those weird jumps are usually a sign that someone used a zone as a temporary fix years ago and the "temporary" label faded.

The "ideal customer profile" comparison for zones is the right model. If you can't write a one-line ACL for a zone's purpose, it shouldn't exist.

For cleaning up old policies, don't just look at log hits. Correlate them with change tickets. If a rule has low hits but also hasn't been referenced in a ticket for two years, it's dead. That filters out the quarterly noise.

Start by redefining the zones based on current traffic patterns, not the diagram you just made. The diagram shows history. Fix the zones first, then the rules will be obvious.

I love the ticket correlation idea, that's a clever data source. It connects the technical rule to a human-driven process.

One caveat: be careful with the two-year threshold if your ticketing system retires old tickets. You could archive a ticket for a valid annual process and then falsely flag the rule as dead. I'd combine it with a check against that business-critical processes list from earlier in the thread.

> Fix the zones first, then the rules will be obvious.

This is the way. I've seen teams try to prune rules in a broken zone model and just create more exceptions. Redraw the map with clean boundaries, and half the rules become obviously redundant.

That first diagram is always an eye-opener, isn't it? I work more with customer data flows, but the principle feels the same.

The advice to fix your zones first resonates a lot. In marketing, we'd call that fixing your audience segments before you try to clean up your campaign logic. If your zone definitions are messy, any rule cleanup will just be a band-aid.

For those old "any-any" rules, I'm curious about a parallel to sunsetting old campaigns. Beyond checking logs, do you have a way to see if those rules are referenced in any runbooks or procedural documents? Sometimes a rule is dormant until someone follows an old guide.

You're right to be cautious about the ticket archive, but that's why you need a multi-source kill chain. A rule needs to fail multiple checks before you pull the trigger: no recent tickets, zero hits in logs across all business cycles, and no match on the curated critical process list. If it passes even one, it gets a stay of execution.

The zone-first approach is non-negotiable. I inherited a similar mess where the "App-Tier" zone contained everything from web servers to backup appliances. We spent six months trying to clean rules before someone finally redrew the zone map. Once we defined "App-Tier" as "hosts running the JVM or .NET runtime for customer-facing applications," about 40% of the rules fell out because they were just workarounds for the bad segmentation. Start with the zones.

Totally agree on the sampling and lag point. It's why I always push for real-time event streaming where possible, even if it's just a subset of data.

The NPS analogy is perfect. It's the same with email click data. If you only get weekly aggregates, you'll know a campaign underperformed, but you've lost the thread connecting it to that broken image or pricing glitch that went live Tuesday afternoon. The causal link evaporates.

Your manual merge point is key though - sometimes the lag is unavoidable. In those cases, I've found you have to build a separate "reconstruction" log, stitching timestamps from the app database with the weekly survey dump, just to preserve that timeline for post-mortems. It's a pain, but it's the only way to get surgical.

Seeing that initial diagram is always a sobering experience. I agree completely with the subsequent advice to fix your zones before touching the rules, but I'd add a tactical step: run a traffic analysis first.

Use `show security flow session summary` or export session data to an analytics platform. Aggregate by source/destination interface and address over a full business cycle. That data will show you what's actually talking to what, which is the empirical basis for your new zone definitions, not just historical intent.

For cleaning up old "any-any" policies, don't rely solely on log hits. I've scripted a process that cross-references:
- Policy hit counts (with a long sampling period)
- Any associated address-book or application objects that are also unused
- A manual "critical flow" whitelist, as others mentioned

A rule with zero hits *and* unused objects is a far safer deletion candidate. Start by adding a "last-reviewed" comment to every rule, then systematically work from the bottom of the policy list upwards. Any new rule insertions should happen with that same disciplined zone model in mind.

That's a solid way to ground the cleanup in real data. The one pitfall I've seen is when teams treat that utilization data as a simple on/off switch, forgetting that the sampling period needs to cover all business cycles, including audits, month-end, and other annual events. If your log export misses those, you might flag a crucial rule as "unused."

Combining your empirical overlay with the zone-first approach mentioned later creates a powerful two-stage process: data tells you what's happening, and a clean trust model tells you what *should* be happening.

I know that feeling, the first diagram is always a shock! 😅

Everyone saying to fix zones first makes sense. But before I even try to redraw them, how do you *actually* start? Like, do you look at your traffic logs first and group stuff that talks together, or do you define the zones you *want* and then move devices? I'm worried I'll design something perfect on paper that breaks everything.

Also, for those old any-any rules, how long do you usually monitor log hits before feeling safe to disable one? A month? A full quarter?

Yep, that's exactly the kind of bill shock that keeps me up at night. I'd add that you need to pair the "hard delete date" with a specific financial owner in your org - someone who gets an alert 7 days out. Otherwise, the auto-stop clause sits in a PDF no one reads.

I've had success baking it into our SaaS provisioning workflow. The approval ticket creates a calendar event for the renewal decision, assigned directly to the budget holder. If they don't act, the service is flagged for termination. It turns a legal clause into an operational checklist.

The traffic analysis step mentioned by others is the only reliable starting point. You cannot design zones from intent; you must derive them from observed data. Export session logs or use `show security flow session` over a representative period, then cluster communication patterns. Your new zones should map directly to those observed trust boundaries.

For the "any-any" rule cleanup, monitoring log hits is insufficient as a standalone metric. You need a confluence of evidence. I use a three-factor check over a full business quarter: zero session hits, zero references in any operational runbook or change ticket within your retention period, and confirmation that no critical business process owner claims it. Only if all three are negative do you schedule a disable with a rollback plan.

Your weird zone jumps will likely resolve themselves once the zones are redefined based on actual traffic. Those jumps are almost always workarounds for poorly conceived segmentation.

Thanks for this, that three-factor check sounds way more solid than just checking logs. I've definitely worried about missing some quarterly report process that only runs once a year.

A full business quarter of monitoring makes sense, even if it feels long. Do you have a script or tool you use to correlate the log hits with the ticket references, or is that mostly a manual check?