The European Union recently confirmed that its long-awaited guidance for high-risk AI systems will be delayed again, after already missing the 2 February 2026 deadline for Article 6 guidance on how to determine whether an AI system qualifies as high-risk. That guidance is supposed to give companies practical clarity on which systems face stricter obligations, yet the delay leaves many organizations in a gray zone just months before the high-risk rules are due to take effect in August 2026.
While regulators continue to debate frameworks and timelines, organizations are moving in the opposite direction. They are accelerating AI adoption.
And that adoption is quickly moving beyond chatbots and copilots.
The next wave is being shaped by AI agents, systems that do more than generate content. They can make decisions, access tools, interact with APIs, and take actions across business environments. As organizations move from AI-powered applications toward more autonomous systems, the attack surface expands, the potential blast radius grows, and the consequences of weak controls become much harder to contain.
That gap between delayed guidance and accelerating deployment is exactly where risk grows fastest.
From Web Security to AI Security
Applications have evolved in stages. First came traditional web applications. Then API-driven architectures. Now organizations are deploying AI-powered applications, copilots, and increasingly, agentic systems.
Each wave created new business value. Each wave also introduced new exposure.
With AI, the shift is more fundamental. Prompts become a new input layer. Models become reasoning engines. Agents become actors that can trigger workflows, retrieve data, and interact with external tools. Security is no longer only about protecting the app or the API. It is about protecting the AI layer itself.
That matters because as AI becomes more autonomous, risk moves beyond what a model says and into what a system can do.
The AI Risk Gap Is Growing
The industry is already recognizing that AI introduces a distinct category of risks. OWASP’s Top 10 for LLM Applications highlights threats such as prompt injection, sensitive information disclosure, excessive agency, system prompt leakage, and unbounded consumption.
These are not theoretical concerns. They reflect how AI systems can be manipulated in ways that traditional security tools were never designed to handle.
And with agentic AI, the challenge grows even further.
The risk is no longer limited to prompt abuse or unsafe outputs. It now includes misuse of connected tools, unauthorized actions across systems, memory and context poisoning, hidden prompt injection embedded in ordinary content, rogue agents, and behavior that drifts from the agent’s intended purpose.
In other words, the move to agentic AI raises the stakes from content risk to operational risk.
Securing the AI Layer
This is why organizations need security controls designed specifically for the AI layer.
Radware’s LLM Firewall is built to protect AI-powered applications at the prompt and response layer. It helps block prompt injection and jailbreak attempts, detect sensitive data such as PII before it reaches external models, enforce usage and topic restrictions, and reduce the risk of harmful, misleading, or brand-damaging outputs. It also gives organizations a practical way to apply policy and responsible-use guardrails without slowing down adoption or forcing them into a specific model architecture.
But securing prompts alone is not enough once AI systems begin to act with greater autonomy.
That is where Agentic AI Protection comes in. As AI agents connect to enterprise tools, SaaS platforms, APIs, and internal workflows, organizations need visibility into what those agents can access, how they behave, and what actions they are taking in real time.
Agentic AI Protection addresses that broader challenge by helping organizations discover agents and their tools, map relationships across the AI ecosystem, monitor runtime activity, identify anomalous behavior, and enforce guardrails that reduce the risk of misuse or unintended actions.
Together, these two layers address both sides of the problem. LLM Firewall helps secure what enters and leaves the model. Agentic AI Protection helps secure what autonomous AI systems do once they are connected to the business.
Regulation Will Come. Attacks Will Not Wait.
Guidance will eventually arrive. Regulations will mature. Definitions will become clearer.
But none of that changes what is already happening now.
Organizations are actively deploying AI-powered applications and increasingly exploring agentic AI. That means security cannot remain a future compliance discussion. It has to become part of the deployment strategy from the start.
Regulation may take time. Attackers won’t.