The release of Claude Code Security triggered headlines about disruption across the cybersecurity market. Analysts questioned whether traditional security categories were at risk, and public markets reacted quickly.
But much of the reaction reflects noise rather than signal.
Claude Code Security represents a meaningful advancement in developer-centric security. By embedding AI reasoning directly into the coding workflow, it reduces friction, improves vulnerability identification, and helps developers remediate issues before code reaches production. That progress will influence how modern software is built and maintained.
What it does not do, however, is control runtime reality.
Security failures rarely originate from poorly written code alone. They emerge when legitimate application logic interacts with live identity context, state transitions, infrastructure configurations, automation, and adversarial pressure. Clean repositories do not guarantee secure production environments, particularly in distributed, API-driven, and AI-augmented architectures.
This distinction aligns directly with Radware’s focus on runtime protection.
AI Improves Pre-Production Hygiene. Radware Secures Production Outcomes.
Claude analyzes repositories and suggests improvements inside pull requests. Radware analyzes live traffic, behavioral patterns, business workflows, and attack chains across production systems.
Modern attacks often exploit business logic rather than syntax flaws. They abuse legitimate APIs, replay valid calls in malicious sequences, manipulate state transitions, and automate credential abuse at scale. These attacks require no broken code to succeed. They succeed because enforcement at runtime is insufficient.
Application-layer DDoS attacks, bot-driven automation, scraping, and API abuse all operate in production. They interact with real users, real infrastructure, and real data. The ability to distinguish legitimate behavior from adversarial automation, and to mitigate threats without disrupting user experience, is what ultimately determines security outcomes.
Claude improves development velocity. Increased velocity expands runtime exposure. The faster applications evolve, the more critical real-time enforcement becomes.
The False Confidence Risk
A natural question emerges: if AI reviews our code, why do we still need WAF, WAAP, bot protection, and DDoS mitigation?
Because reasoning is not enforcement.
Claude operates within a code context. It does not observe infrastructure misconfigurations, cross-application traffic correlation, live identity interactions, encrypted application-layer floods, or large-scale bot automation. It does not enforce behavioral baselines across hybrid and multi-cloud environments.
Security is ultimately determined by who controls behavior under adversarial pressure in production, not by who writes cleaner code during development.
Protecting the Web and Agent Economy at Runtime
Organizations today operate within two converging domains. The first is the Web and API economy that powers digital business. The second is the emerging agent economy, where AI systems initiate actions, consume services, and interact autonomously with other systems.
Both expand the attack surface in ways that cannot be addressed solely through pre-production code analysis.
Autonomous agents introduce new risks involving tool misuse, unintended privilege escalation, prompt manipulation, and automated abuse of APIs. Meanwhile, traditional threats such as application-layer DDoS, bot attacks, and API business logic exploitation continue to grow in scale and sophistication.
Effective protection now requires continuous enforcement across live traffic, identity context, business logic, and infrastructure layers. It requires visibility into automated interactions, correlation across protection engines, and the architectural performance to mitigate threats at scale without introducing latency or operational complexity.
True runtime security today requires more than intelligent software. It requires infrastructure capable of absorbing and mitigating attacks at scale, whether in the cloud or on dedicated devices. It requires centralized management and unified visibility to understand what is happening across networks, applications, and APIs in real time. And it requires the operational maturity of an established security provider that can deliver consistent protection, support, and accountability. AI-driven automation enhances security, but it does not replace the need for scalable infrastructure, integrated management, and proven expertise.
As AI-generated traffic and automation increase, runtime control becomes the defining layer of security.
This is not a shift away from application security. It is a shift toward recognizing that development hygiene and runtime enforcement serve different purposes. AI-assisted coding reduces certain classes of defects. It does not eliminate adversarial pressure in production.
Claude Code Security represents progress in upstream development security. It does not replace runtime protection. In fact, by accelerating delivery cycles and lowering the barrier to shipping code, it increases the importance of production-layer enforcement.
AI may improve intent during development.
Radware enforces reality in production.
And in cybersecurity, production reality is what defines outcomes.