Introduction
Cloud-based services today face increasingly sophisticated and dynamic DDoS threats. While detection engines have evolved—leveraging behavioral baselines and automated mitigation—one core operational challenge remains: real-time visibility and context during an ongoing attack.
Modern mitigation systems may effectively block malicious traffic, but for SOC teams trying to understand what’s happening during an event, the experience is often fragmented. What’s missing isn’t just mitigation of power—often, it's attack clarity.
The Challenge
DDoS attacks aren’t static. They often shift across sectors, geographies, and traffic signatures. Even with intelligent detection and mitigation mechanisms in place, SOC teams are often flooded with partial snapshots of the threat landscape. Each tool provides data, but without a unifying context, understanding how an attack evolves and whether it’s fully mitigated remains time-consuming.
In large-scale environments, SOC analysts need to track not just traffic spikes or protocol anomalies—they need to see the attack lifecycle as it progresses. Without a clear timeline of changes in attacker behavior, remediation efforts—automated or otherwise—can lack optimal precision. This isn’t due to a lack of mitigation logic, but rather to fragmented visibility.
The Visibility Gap
The current state of many SOC workflows is characterized by fragmentation. Analysts might have:
- A graph showing a spike.
- A separate log entry showing a protocol change.
- A policy that triggered a drop action.
But only a limited correlation between these events. There may be timelines, logs, and alerts, but they’re often disconnected—making it harder to track the full attack sequence and understand the response in context.
This visibility gap leads to slower decisions, inaccurate assumptions, and missed opportunities to optimize protection.
Why AI Context is the Missing Link
SOC teams today need more than alerts. They need insights. They need answers to questions like:
- What type of attack is this now?
- What was it 10 minutes ago?
- Is mitigation keeping up with changes?
- What still needs attention?
This is where AI brings real value.
Instead of relying on analysts to piece together these insights manually, an AI-driven system can:
- Detect changes in attack behavior in real time.
- Group related activity into structured attack “waves.”
- Provide a clear narrative of how the attack evolved.
- Recommend mitigation tuning based on live traffic behavior.
Organizing the incident into a coherent timeline—with labeled phases, shifts in vector, and mitigation coverage—AI helps SOCs move from reactive monitoring to proactive, contextual response.
A Better Way to Defend
Imagine a scenario where instead of isolated alerts, the SOC receives a structured summary that shows what vectors were mitigated and where traffic may still be leaking:
- Wave 1: TCP SYN flood observed—mitigated successfully.
- Wave 2: UDP fragments with partial leakage—remediation filters applied.
- Wave 3: DNS reflection attempt detected— mitigated successfully.
Each wave is timestamped, with sources, vectors, and actions taken—all in one view.
That’s the kind of clarity AI brings to DDoS operations.
Conclusion: From Data to Understanding
Radware already leads in automated, behavior-based DDoS defense. But we believe that Actionable AI and Visibility are just as important as mitigation. In today’s high-pressure SOC environments, having the best protection engine is only part of the equation. What analysts truly need is the ability to understand what is happening and why.
The shift we’re making is toward context-first defense. Empowering SOCs with insight, not just alerts. Providing narratives, not just metrics. And enabling decisions that are informed, precise, and faster.
In Part 2, we will explore how we’ve built this into our platform with SOC-X—Radware’s new AI-powered capability that transforms recommendations into real-time remediation actions. Stay tuned.