Web applications and APIs are now the operational core of most digital services. They process transactions, expose business logic, manage identities, and connect distributed systems that evolve continuously. In parallel, the volume and sophistication of attacks has increased, driven by automation, accessible tooling, and cloud-specific attack vectors.
Web Application Firewalls remain a critical part of the security stack—but in 2026, the challenge is no longer whether a WAF is deployed. The real question is whether it can be evaluated, measured, and trusted under real operating conditions, especially when consumed as a service.
As WAFs move to SaaS models, teams delegate infrastructure, scaling, and maintenance to the provider. This simplifies operations, but it also changes the evaluation criteria. When you no longer control the underlying system, visibility, isolation, and predictable behavior become non-negotiable technical requirements.
Evaluating a WAF in 2026 is fundamentally different
Traditional evaluations focused heavily on rule coverage or whether a solution “covers OWASP Top 10.” Those checks still matter—but they no longer reflect production reality.
A modern evaluation must answer practical, operational questions:
- Can the WAF block malicious traffic without breaking legitimate flows?
- Does it behave consistently in prevention mode and under load?
- Can its decisions be observed, explained, and audited?
In SaaS environments, this becomes even more critical. When a false positive blocks production traffic or latency spikes unexpectedly, there is often no lower layer to compensate. The WAF’s behavior is the system’s behavior. If that behavior cannot be measured and understood, the evaluation is incomplete.
Why most SaaS WAF evaluations fall short
Many WAF evaluations fail not due to lack of expertise, but because the process itself is incomplete.
Common pitfalls include:
- Testing in monitor-only mode instead of prevention
- Relying on default configurations with no real traffic
- Ignoring operational limits until production
- Inability to trace why a request was blocked
In SaaS models, additional constraints often surface late: payload size limits, rule caps, log retention, export restrictions, or rate limits in the control plane. These are not secondary details—they directly affect detection quality and incident response.
A meaningful evaluation must be observable and reproducible. If you cannot trace decisions through logs, correlate them with metrics, and explain them after the fact, the WAF becomes a black box.
Detection quality is defined by false positives, not demos
Detection capability is often summarized by a single number, usually the True Positive Rate (TPR). While important, this metric alone is misleading.
A WAF that aggressively blocks everything will score well in detection tests—and fail catastrophically in production.
Real-world evaluation must consider both sides of the equation: blocking malicious traffic and allowing legitimate traffic to pass. False positives are not a usability issue—especially in API-driven systems, where payload structure, schemas, and request volume amplify the cost of false positives.
At scale, even a low False Positive Rate (FPR) can result in:
- Broken user flows
- Failed API calls
- Increased operational load
- Pressure to weaken or disable protections
This is where most evaluations break down in practice: not on attack detection, but on how much legitimate traffic is disrupted.
A realistic PoC should include scenarios like:
In SaaS environments, false positives are even more costly, as tuning depends on provider capabilities, change latency, and visibility into decisions.
SKUDONET Cloud Solution
SkudoCloud was designed to deliver application delivery and WAF capabilities as a SaaS service while preserving the technical properties advanced teams need to operate safely in production: transparent inspection, predictable isolation, and full visibility into traffic and security decisions. The goal is to remove infrastructure overhead without turning operations into a black box.
That same philosophy shapes how WAFs should be evaluated in 2026. Teams should assess real behavior: prevention mode, realistic traffic patterns, false positives, API payloads, and performance under load—especially when the service is managed and the underlying system is not directly accessible.
To support that evaluation, we have documented the full methodology in our technical guide:
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