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

Excellent first step. Visualizing the policy flow is the only reliable method for understanding cumulative rule precedence, which the CLI obscures.

Your two primary issues, the "any-any" rules and the zone jumps, are interconnected. While others have correctly warned about dependency checking, I'd add a structural point: before you even begin decommissioning permissive rules, you must define your zone-based trust model. A "weird" zone jump often indicates an architectural flaw, like a server zone communicating directly to a user zone, bypassing a DMZ. Clean that up first, as it will naturally invalidate certain "any-any" rules and clarify which traffic paths are legitimate.

For the cleanup itself, the "deny and log" approach is sound but logistically heavy. A more methodical alternative is to first create a new, clean policy set in a separate security context or policy stanza, migrate your validated business flows to it with explicit rules, and apply it in parallel with a lower priority. This gives you a functional baseline. Then, you can methodically shift traffic to the new policy set rule by rule, using scheduler-based policies to enforce change windows, rather than relying solely on manual log review. This provides rollback capability for each discrete step.

Excellent first step. Visualizing the policy flow is the only reliable method for understanding cumulative rule precedence, which the CLI obscures.

Your two primary issues, the "any-any" rules and the zone jumps, are interconnected. While others have correctly warned about dependency checking, I'd add a structural point: before you even begin decommissioning permissive rules, you must define your zone-based trust model. A "weird" zone jump often indicates an architectural flaw, like a server zone communicating directly to a user zone, bypassing a DMZ. Clean that up first, as it will naturally invalidate certain "any-any" rules and clarify which traffic paths are legitimate.

For the cleanup itself, the "deny and log" approach is sound but logistically heavy. A more methodical alternative is to augment your flow diagram with empirical data. Export session logs over a significant period, map the actual flow tuples back to the rules in your diagram, and tag each rule with its observed utilization. This creates a data-driven overlay that distinguishes between truly dead rules and those with intermittent, critical use. My process typically involves:
- Correlating logs with diagrammed paths.
- Creating a separate policy context for "legacy-candidate" rules.
- Implementing a structured change window where these are set to `deny` with enhanced logging, but with a pre-defined, immediate rollback procedure.

Regarding zone and rule structure, enforce a naming convention that encodes intent, such as `from-zone-to-zone-service-purpose`. This turns the policy list into a self-documenting artifact. The rulebase within a context should follow a strict order: explicit denies for known bad traffic, specific permits for business services, then a final implicit deny-all. This minimizes hidden permissiveness and makes audits straightforward.

I love this idea of a data-driven overlay, it's the perfect bridge between the theory and reality. The part about "intermittent, critical use" is so key. I've seen a rule fire once a quarter for a critical financial batch job, and without that log correlation, it just looks like noise.

But exporting and mapping session logs can turn into its own data pipeline project. My tip? Start by sampling. Pull a day of peak business activity logs and a day of weekend/low activity logs. Map those to your diagram first. You'll quickly see the 80/20 split - the heavy hitters and the weird outliers. It keeps the initial analysis sprint manageable.

That's such a good point about the reusable scripts being the real asset. I've seen similar things happen with old campaign analytics containers. The tool gets retired, but the data transformations live on.

How do you handle versioning for those containerized scripts? Just tagging the image, or something more?

Good on you for diagramming the policy flow. That visual is indispensable for spotting architectural flaws, like those zone jumps. Before touching any rules, solidify your zone trust model. Each zone should represent a distinct security boundary, such as internet, DMZ, internal services, and user networks. Weird jumps often mean servers are in the wrong zone.

For rule structure, I prefer a matrix: source zone, destination zone, and application. This mirrors how traffic actually flows and makes policies predictable. Avoid 'any-any' by defining specific services. When cleaning up, don't just rely on hit counts. Correlate logs with business events; a rule that fires only during month-end closing is critical, even if it's used sparingly.

Consider scripting the analysis. Export policies and logs, then use a simple script to match rules against active sessions. This data-driven approach prevents guesswork and prioritizes rules that can be safely removed.

Totally agree on scripting the analysis. Exporting the policy and logs is key, but you can make that script part of a pipeline to keep the analysis fresh. I've got a weekly cron job that pulls the config, pulls a day's worth of aggregated logs, and spits out a simple markdown report of low-hit-count rules with a flag if they matched any business-critical systems. Stops it from being a one-off archaeology project.

Your point about correlating with business events is huge, though. The script needs a manual input layer, a CSV of "critical processes and their IPs/times," because the firewall won't know what "month-end closing" looks like. You have to feed that context in.

Oh, I feel you on the tangled diagram - it's always a shock to see it all laid out, but that visual is the most valuable tool you have now.

I'm a huge fan of turning that diagram into a living document. Everyone's right about hit counts and logging, but I'd layer on a step from my A/B testing days: treat the cleanup like a phased rollout. Take a section of your diagram, maybe starting with those weird zone jumps for a single application, and define what a "clean" state would look like for just that path. Then, implement those specific changes as a batch and monitor like you would a feature flag. It turns an overwhelming architectural rewrite into a series of measurable, reversible experiments.

For the old "any-any" policies, have you considered tagging them in your config with a comment like "#LEGACY_CANDIDATE" first? Then you can filter for that tag in your scripts when you pull logs. It creates a separate bucket to analyze, which makes correlating their hits to business processes a bit more manageable than sifting through the entire policy set.

That point about the manual merge forcing you to spot the outlier is something I've run into with survey data, too. You can have all the dashboards in the world, but the act of stitching different data sources yourself often surfaces the critical anomaly a pre-built report would average out.

Your warning on sampling and lag is the killer for surgical work. It's like trying to diagnose churn with a NPS score that's only updated weekly - the trend might be right, but you've lost all ability to connect it to the specific feature change that caused it.

Mapping that flow is the smartest thing you can do, and the initial shock is normal. I've seen too many teams try to clean up policies without that visual map first.

For the cleanup phase, don't just rely on logs and hit counts - get procurement involved. Pull the vendor invoices for any SaaS apps or external services that have been onboarded in the last few years. Cross-reference those IP ranges or FQDNs with the rules in your tangled diagram. You'd be surprised how many "any-any" or overly broad rules were created to enable a specific vendor tool that's since been decommissioned. The finance paper trail often spots what the logs miss.

And on zone structure, think of it as a contract: each zone-to-zone pair should represent a clear business relationship, like "internal users can access the HR SaaS app." If you can't write that contract in a simple sentence, the zone jump is probably wrong.

Brilliant point about pulling procurement and vendor invoices. That's a step so many technical teams miss. The finance system becomes a de facto audit log for business intent.

I'd add one nuance: the correlation window. A vendor might have been decommissioned, but the rule could have later been repurposed for something else entirely. So while the invoice trail is a fantastic starting point for identifying orphaned rules, you still need to validate with recent logs that the rule isn't now enabling some other, undocumented workflow before you pull the plug.

Thinking of zone-to-zone as a contract is a perfect analogy. I use a similar checklist in my playbook: can you name the primary business service, the owning team, and the renewal date for that cross-zone communication? If you can't, it's a candidate for immediate scrutiny.

Seeing that tangle laid out is honestly the hardest step, so congrats on powering through it. Everyone's hitting the right notes on logging and vendor checks, but I'd add one angle from the RevOps side: treat the cleanup like a sales territory realignment.

You wouldn't just delete old accounts without checking if they're assigned to a rep or part of a forecast. Same with policies. Before you decommission any "any-any" rule, do a quick sanity check with your business systems teams. Ask, "Does any critical process, like the CRM sync, quarterly reporting extract, or customer portal payment flow, depend on this path?" Sometimes the most dangerous rules are the ones that only fire during a business cycle nobody told IT about.

On zones, I've found it helps to sketch them not just as security boundaries, but as 'data domains'. If a zone holds customer PII, its contract with the marketing zone is probably 'read-only, via these two APIs'. That mindset flips it from a network puzzle to a data flow one, which often makes the weird jumps obvious.

Mapping the policy flow is the most critical step, and your reaction is precisely why it's necessary - the visual dissonance forces action where abstract lists do not.

While others have covered logs and vendor checks, I'd add a parallel from cloud cost optimization: treat policies like idle compute resources. An "any-any" rule is functionally identical to an over-provisioned cloud instance with no utilization alarms. The analysis method should be similar. You need to establish a formal depreciation schedule. Any rule without a verified business owner (like a service team or application) and zero logged hits over a defined, business-cycle-aware period (90 days is common) should be flagged for decommissioning, not just reviewed.

For zone structure, anchor it to tangible infrastructure changes. If you cannot map a zone-to-zone policy directly to a specific application's documented network requirements, the zone model itself is likely flawed. Weird zone jumps often indicate servers placed in zones based on network topology from five years ago, not current security requirements. Re-evaluate the host placements before you rearrange the policies; otherwise, you're just codifying the existing architectural debt.

You're spot on about the depreciation schedule, and I like tying it to a verified business owner. That's crucial for governance. But I'd push on the 90-day window being standard. For some of our financial systems, a quarterly process means a rule could be legitimate and still show zero hits for 89 days. The period needs to match your longest business cycle, which for us meant looking at annual audit processes too.

Your point about anchoring zones to tangible infrastructure changes is the core of the problem. Teams often try to fix the map without moving the territory. If the server placement is wrong, all you're doing is documenting a historical mistake.

Oh, that first diagram shock is real, but it's the best kind of pain - you can't fix what you can't see! Your question about structuring zones really hits home for me.

From an email automation standpoint, I always think of zones like customer segments. You wouldn't send the same message to everyone, right? A zone should be a group of systems with a unified "need to know." Those weird zone jumps in your diagram are like a segment rule that fires for the wrong audience. I'd start by locking those down first, treating each jump as its own mini-project to validate or eliminate.

For the "any-any" rules, everyone's advice on logs and vendor checks is golden. My addition would be to borrow from lead scoring: assign every rule a simple "risk score" based on its breadth and destination. The any-any rules get the highest score automatically, which makes them your top priority for cleanup. It turns that messy list into a clear action queue. And please, tag those old policies with a comment like #Review_2024_Q4 before you touch them. It creates a paper trail in the config itself.

That diagram shock is the best motivator, isn't it? Seeing it all mapped out is half the battle.

You're getting great tactical advice here. My two cents from the marketing automation side: I'd treat those weird zone jumps like bad lead routing. Each one is a leak where intent doesn't match action. I'd isolate and validate them one by one before touching the broad "any-any" rules. If a jump can't be tied to a specific, active business process, it's a candidate for immediate lockdown.

For the cleanup, the depreciation schedule idea from earlier is solid, but I'd frame it like sunsetting a legacy email campaign. You don't just delete it; you'd look at open rates (hit counts), check if any automations still reference it (those business system checks), and then schedule the deactivation. Makes the process feel less like archaeology and more like standard ops.