Policy inputs
Formula used
This tool estimates hygiene, risk, and maintainability, then converts them into a single optimizer score. It is a planning model, not a device-specific benchmark.
Any-Any Ratio = Any-Any / Total
Change Hours = (Change Requests × Review Minutes) / 60
Rework Hours = Change Hours × (0.25 + 1.25 × Rework Rate)
Complexity = (0.7×Zones + 1.0×Sections) / 50
Risk Index = 100 × (0.36×Exposure + 0.34×Criticality + 0.20×AnyAny + 0.10×Hygiene) × (1 + 0.35×Compliance)
Avg Checks ≈ min(N, 10 + 3×sqrt(N))
Perf Gain ≈ (Checks − ChecksNew) / Checks
The target rule count removes unused/shadowed/disabled rules, reduces redundant rules by 70%, and applies consolidation potential from object reuse.
How to use this calculator
- Export rule analytics: unused, redundant, shadowed, and broad-scope rules.
- Enter monthly change volume and typical review minutes per change.
- Set exposure, criticality, and compliance strictness to match your environment.
- Submit to get score, target rule count, and prioritized actions.
- Download CSV or PDF to track improvements across review cycles.
Example data table
Sample values below illustrate how the metrics map to outputs.
| Metric | Example value | Why it matters |
|---|---|---|
| Total rules | 850 | Higher counts increase policy review and matching overhead. |
| Unused / redundant / shadowed / disabled | 70 / 55 / 18 / 25 | These inflate complexity and can hide risk hotspots. |
| Any-to-any rules | 9 | Broad scopes raise exposure and reduce audit confidence. |
| Logging enabled | 55% | Excess logging on high-volume rules adds storage and noise. |
| Change requests / month | 40 | Frequent changes amplify the cost of poor hygiene. |
| Automation / documentation / object reuse | 45% / 60% / 50% | Improves consistency, review speed, and long-term control. |
Policy hygiene metrics that drive the optimizer score
Rulebase hygiene is quantified as a Hygiene Ratio: (unused + redundant + shadowed + disabled) ÷ total rules. In mature programs, this ratio is commonly held below 0.10, while ratios above 0.20 usually signal excessive drift. Use a consistent analytics window (30–90 days) so “unused” reflects reality, not seasonality. The optimizer score applies a larger penalty when hygiene inflates review time, increases match ambiguity, and creates “false confidence” during audits.
Risk indexing from exposure, criticality, and broad scopes
The Risk Index blends threat exposure and business criticality with policy signals such as Any-to-Any Ratio and Hygiene Ratio. A practical target is keeping Any-to-Any below 1% of rules, and forcing compensating controls when exceptions are required. Act early. Higher compliance strictness amplifies risk because weak scoping raises audit findings, recertification workload, and remediation urgency.
Change workload and time-to-value forecasting
Change Hours are computed from monthly tickets and average review minutes, then increased by a rework factor tied to reversal and amendment rates. When rework exceeds 15%, organizations often see compounding queues and longer lead times. Time-to-value is estimated by comparing one-time cleanup hours to monthly savings from better processes. Automation reduces the change penalty by shrinking manual checks, standardizing templates, and improving pre-deploy validation.
Consolidation strategy using objects, zones, and sections
The target rule count removes unused, shadowed, and disabled rules, then reduces redundant rules by 70% to reflect safe consolidation. Additional savings are estimated from object reuse, because shared address and service objects reduce duplicate definitions and enable group-based rules. Zone and section counts add complexity pressure, so consolidation should preserve segmentation intent, not flatten it. Prefer small refactors: rename objects, normalize services, and merge only validated overlaps.
Operational reporting for audits and continuous improvement
Logging Load Proxy approximates event volume as (average hits per rule per day × total rules × logging ratio). If proxy volume is high, consider sampling high-traffic allow rules while keeping deny logs intact. Add owners and expiry dates so every rule is traceable to a business request. Track optimizer score, risk index, and hours saved each quarter, and attach exports to change tickets for measurable improvement.
FAQs
1. What counts as an unused rule here?
A rule is treated as unused when it has zero matches in your monitoring window. Use consistent reporting periods, and exclude planned maintenance windows so rare but critical flows are not misclassified.
2. Why are Any-to-Any rules weighted heavily?
Broad scopes reduce intent clarity and increase blast radius. Even if protected by profiles, they complicate audits and incident response. Replace them with scoped objects, tighter services, and explicit source and destination zones.
3. Does a higher logging percentage always improve security?
Not always. Excess allow-logging on high-volume rules can overwhelm storage and analysts. Keep deny logs, log critical allows, and sample noisy permits. The logging load proxy helps you spot when visibility becomes operationally expensive.
4. How should I estimate review time per change?
Use the median minutes spent on analysis, peer review, and documentation for a typical ticket. If you have workflow data, calculate it from timestamps. Overestimate slightly to avoid under-planning cleanup and automation investments.
5. What is the fastest way to raise the optimizer score?
Start by removing disabled and shadowed rules, then decommission validated unused rules. Next consolidate redundant rules with shared objects. Finally improve documentation and automation, which reduces ongoing change penalties and rework.
6. Can I use this output as an audit report?
Use it as supporting evidence, not a substitute. Auditors typically require device exports, approvals, and rule justifications. Attach the CSV or PDF to tickets, and retain rule comments, ownership, and expiry dates for traceability.