Radware shipped the first purpose-built security product for AI agents on February 3, 2026. Not an LLM guardrail. Not a prompt filter bolted onto a WAF. A standalone product that monitors what AI agents actually do at runtime, maps their interactions with other agents and systems, and flags behavior that deviates from legitimate intent. The timing was not accidental: Radware’s own research team had just discovered ZombieAgent, a zero-click attack that hijacks AI agents through memory poisoning and exfiltrates data entirely within the cloud provider’s infrastructure. They built a product because their own vulnerability research proved that existing security tools are structurally blind to agentic threats.
Gartner forecasts $2.5 trillion in worldwide AI spending for 2026, with $51.3 billion earmarked for AI security. Yet only 6% of organizations have an advanced AI security strategy. Radware is betting that the gap between deployment speed and security readiness is where the market will form.
Why Traditional Security Tools Cannot Protect AI Agents
Your firewall does not know what an AI agent is. Your EDR does not monitor LLM memory. Your SIEM cannot parse the difference between an agent summarizing an inbox and an agent obeying hidden exfiltration instructions embedded in an email. This is not a coverage gap you can patch; it is a structural mismatch between how agents operate and what traditional security tools are designed to observe.
AI agents introduce three categories of risk that existing tools were never built to handle:
Threats that live inside the model’s context, not the network. ZombieAgent demonstrated that an attacker can implant persistent malicious instructions in an agent’s long-term memory. The exploit runs entirely inside OpenAI’s cloud. No endpoint logs record it. No network traffic passes through the corporate security stack. The agent processes the malicious instructions as if they were user preferences.
Multi-agent interaction chains. When Agent A calls Agent B, which triggers Agent C to access a database, who is responsible for validating the request? Each individual call might look legitimate. The malicious intent only becomes visible when you trace the full interaction chain across agents, something no SIEM or XDR product was designed to do.
Tool abuse through legitimate permissions. An agent with calendar access can read meeting invites. An agent with email access can forward messages. These are authorized actions. The threat is not unauthorized access but authorized access misused through manipulated intent. Traditional identity and access management does not model this.
The Four Pillars of Radware’s Agentic AI Protection
Radware built the product around four capabilities, each targeting a specific blind spot in current enterprise security architectures.
Discovery and Visibility
Before you can protect AI agents, you need to know they exist. Radware’s first pillar provides real-time identification of all AI agents operating in an environment, both homegrown and SaaS-based. This includes agents built on internal frameworks, Microsoft 365 Copilot instances, AWS Bedrock deployments, and third-party agent platforms.
This matters because shadow AI is already a problem. A 2025 audit found that many enterprises cannot enumerate the AI agents active in their environments. Employees spin up Copilot automations, developers deploy custom agents to staging environments, and vendors embed agents in SaaS products. If your security team cannot inventory what is running, they cannot assess what is exposed.
Intent-Based Security
This is the pillar that distinguishes Radware’s approach from prompt filters and guardrails. Instead of inspecting individual prompts or outputs, the product uses patent-pending behavioral algorithms to detect malicious or abnormal intent within agent interactions at runtime.
The difference is important. A prompt filter checks whether input text contains known attack patterns. Intent-based security monitors the sequence of actions an agent takes and evaluates whether the pattern of behavior matches legitimate use. An agent that reads an email, saves contact details to memory, and then drafts a reply looks normal. An agent that reads an email, saves a hidden instruction to memory, and then begins visiting a series of pre-constructed URLs character by character looks like ZombieAgent.
This runtime behavioral analysis runs externally to the agent itself, meaning it does not depend on the agent’s own safety training or guardrails. It observes what agents do, not what they say they are doing.
Deep Integration
The product is designed to protect agents built on multiple platforms: homegrown frameworks, Microsoft 365 Copilot, Microsoft 365 Copilot Studio, AWS Bedrock, and others. It is available through AWS Marketplace, signaling that Radware is targeting cloud-native deployments rather than on-premise installations.
Deep integration also means the product can monitor tool calls and API interactions that agents make, not just the text they generate. When an agent calls a database API, sends an email through Microsoft Graph, or invokes another agent through a service mesh, Radware’s product captures that telemetry for behavioral analysis.
Continuous AI Security Posture Management
The fourth pillar introduces a dynamic Risk Graph Map that continuously scores an organization’s agentic AI security posture. This graph visualizes multi-agent risk paths: chains of agent interactions that could, if exploited, lead to data exposure or unauthorized actions.
The Risk Graph Map is designed to align with the OWASP Top 10 for Agentic AI and uses the AI Vulnerability Scoring System (AIVSS) to prioritize risks. Instead of a static compliance checklist, it provides a real-time view of where your agent ecosystem is exposed.
From ZombieAgent to Product: Vulnerability Research as Product Strategy
The backstory matters for evaluating Radware’s credibility here. In January 2026, Radware researcher Zvika Babo disclosed ZombieAgent, a zero-click indirect prompt injection that persists in ChatGPT’s long-term memory and exfiltrates data through pre-constructed static URLs. The vulnerability bypassed OpenAI’s post-ShadowLeak defenses by avoiding dynamic URL construction entirely.
The key finding from ZombieAgent was not just the specific exploit. It was the architectural insight that AI agent threats operate in a layer that traditional security tools cannot reach. Endpoint detection does not monitor LLM memory. Network security does not inspect in-cloud agent behavior. Data loss prevention does not flag character-by-character URL exfiltration that happens inside the model provider’s infrastructure.
Radware built the Agentic AI Protection product to address this architectural gap. The product launched exactly four weeks after the ZombieAgent disclosure, suggesting the vulnerability research and product development ran in parallel. This is a pattern seen before in security: a vendor discovers a new attack class, proves that existing tools cannot detect it, and then ships a product that can.
Whether that makes Radware uniquely qualified or creates a conflict of interest depends on your perspective. What is clear is that the team that found ZombieAgent understands agentic attack surfaces at a technical depth that most competing products lack.
Where This Fits in the AI Agent Security Market
Radware is not the only company building AI security products, but the agentic-specific positioning is distinct. Cisco’s AgenticOps focuses on runtime governance for agentic workflows. Snyk’s Agent Scan targets MCP security vulnerabilities in the development pipeline. Darktrace applies anomaly detection to AI-related network traffic. Most existing AI security products, however, treat AI applications as a monolith: model, prompt, output. They do not model the specific risks of autonomous agents that chain tool calls, maintain persistent memory, and interact with other agents.
Radware’s bet is that agents are a distinct enough category to warrant a dedicated security product. Gartner seems to agree: their February 2026 cybersecurity trends report identifies agentic AI as one of the top trends requiring new security approaches, and predicts that by 2028, more than 50% of enterprises will use AI security platforms to protect both third-party AI services and custom-built AI applications.
The practical question for enterprises is timing. If you are deploying AI agents today, with Copilot integrations, custom Bedrock agents, or internal frameworks, waiting for the market to mature means operating without runtime behavioral monitoring, agent discovery, or multi-agent risk mapping. Radware’s product is available now on AWS Marketplace, which removes the procurement friction that slows many enterprise security deployments.
Frequently Asked Questions
What is Radware Agentic AI Protection?
Radware Agentic AI Protection is the industry’s first purpose-built security product for AI agents, launched on February 3, 2026. It monitors agent behavior at runtime using patent-pending behavioral analysis, provides real-time agent discovery, maps multi-agent risk paths through a dynamic Risk Graph Map, and integrates with platforms including Microsoft 365 Copilot, AWS Bedrock, and custom-built agent frameworks.
How does Radware’s AI agent security differ from prompt filters and guardrails?
Prompt filters inspect individual inputs and outputs for known attack patterns. Radware’s intent-based security monitors the sequence of actions an agent takes at runtime and evaluates whether the behavioral pattern matches legitimate use. It runs externally to the agent, so it does not depend on the agent’s own safety training. This approach detects attacks like ZombieAgent that operate through legitimate agent actions rather than malicious prompts.
What is the connection between ZombieAgent and Radware’s Agentic AI Protection?
Radware’s security research team discovered ZombieAgent, a zero-click memory poisoning exploit affecting ChatGPT, in January 2026. The vulnerability demonstrated that traditional security tools cannot detect threats that operate inside an AI agent’s memory and cloud infrastructure. Radware launched Agentic AI Protection four weeks later, built specifically to address the architectural blind spots that ZombieAgent exposed.
Which platforms does Radware Agentic AI Protection support?
Radware Agentic AI Protection integrates with homegrown agent frameworks, Microsoft 365 Copilot, Microsoft 365 Copilot Studio, AWS Bedrock, and other third-party agent platforms. It is available through AWS Marketplace for cloud-native deployment.
Why can’t traditional security tools protect AI agents?
Traditional security tools like firewalls, EDR, and SIEM are designed to monitor network traffic, endpoints, and log events. AI agents introduce threats that live inside model context and memory, span multiple agent interactions, and abuse legitimate permissions through manipulated intent. These threat categories operate in layers that traditional tools were never built to observe.