Firewall Rule Bloat: The Problem and How AI can Solve it

Ask security engineers about firewall rules and they’re likely to shake their heads in disgust. To the uninitiated, a firewall would seem to be the bastion of security, protecting the location and enterprise from threats. But driving firewalls everywhere are hundreds to thousands of rules, permitting and denying users access to applications and resources. And here’s the rub: As those rules accumulate, they can become obsolete, redundant, or irrelevant — preventing users from accessing the resources they need or worse, enabling threat actors to slip into the enterprise.  

In the theoretical world, a nice orderly transition to a zero-trust strategy might solve the problem, empowering users who meet the right conditions to access the resources they need when they need them. But in the real world, legacy firewalls make zero-trust impossible. How can you implement anything orderly when there are 1,000 or 10,000 rules to analyze? It also makes compliance and auditing a challenge.  

The solution? A two-step process that taps AI, not for window dressing, but to solve the critical problem facing security teams everywhere — firewall rule bloat. 

Managing and Controlling Rule Bloat 

As networks expand and evolve, security teams must implement a host of firewall rules to help manage traffic flow, enforce security policies, or meet compliance requirements. Unfortunately, rules once implemented are rarely updated or optimized. 

A construction company, for example, goes on location and the security team sets up a rule for allowing traffic to traverse a firewall to access an internet application. The rule should be removed once the project is completed to avoid exploitation, but all too often, it and thousands like it stay lingering in perpetuity. Eventually, these become obsolete, creating security gaps. There can also be inconsistencies across different firewalls and network segments, which can lead to security loopholes. In geographically distributed sites, rules may have to be updated manually; this itself can be a time-consuming and costly affair. Security teams often lack visibility into how these rules interact with each other, making it difficult for them to ensure alignment with global security policies. 

With cloud computing and remote work, traditional perimeter-based security approaches are no longer as effective. Organizations are increasingly shifting to a zero-trust security model. However, the proliferation of point products, the complexity of networks and their geographical distribution make it challenging to enforce consistent and dynamic policies. Zero-trust approaches also require that organizations erect micro-perimeters around applications and workloads. Overlapping or conflicting rules can inadvertently establish vulnerabilities between segments. 

Ensuring compliance across a diverse and heterogeneous environment is also a significant challenge for security teams. Obsolete and overlapping rules create inconsistencies that violate compliance or regulatory mandates. Security teams and auditors must sift through long lists of rule sets, which is a labor-intensive and error-prone process in itself. The lack of clarity and visibility in firewall rule bloat makes it difficult for security teams to demonstrate compliance adherence. 

A Two-Step Strategy For Overcoming Firewall Rule Bloat 

To address the problem, enterprises should consider a two-step strategy to improve enterprise policy hygiene—consolidate firewalls onto a common platform, namely a Firewall-as-a-Service (FWaaS) offering and then tap AI to help tackle the bloat.  

• Step 1: Move to FWaaS 

FWaaS, like the kind in a SASE platform, creates a single, global firewall, enabling security teams to enforce consistent policies across locations and users, eliminating management of multiple firewall instances, reducing operational overhead and overcoming rule bloat. Enhanced zero-trust enforcement also becomes possible, ensuring that only authorized applications, users and devices can access network resources. Finally, security teams gain clear, end-to-end visibility of the firewall rule bloat allowing them to maintain a clear, auditable security posture. They can leverage built-in tools for policy compliance and audit reporting, reducing the time and effort required for periodic audits. 

• Step 2: Tap  AI for Policy Analysis and Automation 

Even with FWaaS consolidating firewalls, enterprises can still be left with hundreds, if not thousands of rules. By integrating the power of AI and machine learning into FWaaS, organizations can algorithmically identify misaligned and conflicting rules and proactively streamline them. This is what we refer to as an autonomous policy engine. 

An autonomous policy engine represents a paradigm shift in firewall policy management for several reasons. 

• It harnesses AI to identify misaligned and conflicting rules, providing actionable recommendations to enhance policies across multiple environments. 

• It enables real-time zero-trust enforcement since AI monitors and dynamically adjusts firewall policies based on real-time telemetry. 

• It identifies rule inconsistencies that violate regulatory mandates, aiding compliance teams during audits by automating rule inspection and helping justify policy configurations. 

AI continuously learns and adapts FWaaS policies in line with new attack vectors, compliance requirements and operational needs, transforming security operations from reactive problem-chasing to proactive resilience. 

While firewalls remain a foundational element of enterprise security, their complexity and management challenges can inadvertently weaken the effectiveness of a zero-trust strategy. AI-powered FWaaS addresses these challenges head-on while elevating policy hygiene. By leveraging AI for autonomous policy creation and adaptation, FWaaS not only strengthens zero-trust principles and ensures compliance but also simplifies security operations, minimizes human error and enhances the overall security posture.