AI and automation have industrialized modern attack campaigns, shrinking timelines and increasing precision. Akamai's recent State of the lnternet Security Report makes a clear point: DDoS attacks used to require sustained effort can now be launched and adapted quickly, at lower cost, and with less friction. Emerging AI models like Anthropic's Mythos are dramatically reducing the time required to identify vulnerabilities, misconfigurations, and viable attack paths, reducing what once took days or weeks into minutes.
At the same time, AI adoption expands the attack surface—because AI features are delivered as online services that your applications connect to, and those connections are embedded in critical business workflows. The overall result is a threat landscape with tighter coordination across vectors and better targeted attacks.
One of the implications of these shifts is that traditional DDoS testing cannot keep up with what has become an always-present threat model.
The sections below outline three points from the Akamai report that highlight the need for continuous DDoS testing and validation to achieve the end goal: automated DDoS resiliency.
Quote from Akamai: "The convergence of web applications, APls, and distributed denial-of-service (DDoS) attacks that started as a trend is now standard operating procedure."
What this means for business continuity: When attackers move seamlessly across apps, APls, and DDoS, they exploit the most common weakness in enterprise defense: gaps between systems, teams, and controls. ln many organizations, these surfaces are still managed in silos. For example, App security tends to be focused חס the application layer and release cadence; API security is more focused חס discovery and abuse patterns; while DDoS mitigation typically relates to upstream capacity and traffic filtering.
Attackers do not respect these boundaries as they probe across all of them, then apply pressure where controls are inconsistent, misaligned, or drifting. As a result, continuous DDoS testing is key, working as a data and control layer that ties the operating model back together.
ln practical terms, this convergence means that if testing of deployed DDoS defenses is narrow in scope and episodic, the risks remain—and this leaves enterprises exposed to damaging downtime.
Quote from Akamai: "APIs have become the dominant attack surface for modern enterprises, with the average number of daily API attacks up by 113% year over year. Layer 7 DDoS attacks surged by 104% over the last 2 years (2023–2025)."
What this means for business continuity: A Layer 7 DDoS attack is a different business continuity problem, because it targets the same surfaces your customers and partners use. This creates three operational challenges that make continuous DDoS testing mandatory:
Layer 7 weakness is a defense weakness, not a capacity weakness. The primary cause of DDoS downtime is misconfiguration, rule gaps, thresholds that do not trigger, and controls that do not activate as intended. You do not discover those gaps through traditional pen tests and post-incident tuning, because when you're running disruptive tests, you cannot afford to run them חס all online services.
Attacks often show up as degradation, not a complete outage. Layer 7 attacks frequently manifest as slower response times, elevated error rates, and infrastructure scaling, which translates into abandoned transactions and costly customer service calls.
Layer 7 defenses depend חס constant change control: WAF policies, API gateways, rate limits, bot controls, and caching behavior change frequently. Every change can introduce a new bypass condition.
In other words, Layer 7 DDoS attacks are a problem of defense readiness validation. If you can't test continuously without maintenance windows, you don't validate continuously. As a result, vulnerabilities remain hidden.
Quote from Akamai: "Super botnets like Aisuru and Kimwolf (TurboMirai variants) enable accessible DDoS as a service, making sophisticated, large-scale attacks available to anyone."
What this means for business continuity: Attackers are stronger and high-impact disruption is easier to acquire and easier to iterate. This changes the DDoS resilience question from "Do we have a DDoS mitigation vendor?” to:
Is our deployed DDoS mitigation correctly configured?
Are routing, protections, and dependencies still configured dynamically to absorb real-world stress?
Are we confident in the automated behavior of DDoS protections under pressure—i.e., are we protected without the need for manual intervention (which typically only kicks in only after damage occurs)?
Stated differently: when disruptive power is commoditized, DDoS readiness must be operationalized.
We now live in an AI-powered world where attacks are coordinated across surfaces, Layer 7 pressure is accelerating, and scale and disruption are more accessible. The common thread is that availability risk is dynamic.
Continuous DDoS resiliency connects directly to the shifts in the threat landscape. It matches the way modern attack campaigns work by validating end-to-end readiness across the entire exposed environment (not just a subset of services)—and detecting policy drift and configuration mismatch as environments change: new services, new routes, new rules, and new exceptions. Continuous DDoS resiliency turns "we think we are covered" into measurable proof of what is protected, partially protected, or vulnerable.
To learn more about continuous, nondisruptive DDoS Vulnerability Management, download the eBook.