Radware LLM Firewall

Radware LLM Firewall

Secure generative AI use with real-time, AI-based protection at the prompt level.

How Radware LLM Firewall Works

No.1

LLMs follow open-ended prompts to satisfy requests, risking attacks, data loss, compliance violations and inaccurate or off-brand output.

No.2

Radware LLM Firewall secures generative AI at the prompt level, stopping threats before they reach your origin servers.

No.3

Our real-time, AI-powered protection secures AI use across platforms without disrupting workflows or innovation.

No.4

Ensure safe, responsible artificial intelligence for your organization.

ラドウェアAIのご紹介

Secure and Control Your AI Use

Protect at the Prompt Level

Protect at the Prompt Level

Prevent prompt injection, resource abuse and other OWASP Top 10 risks.

Secure Any LLM Without Friction

Secure Any LLM Without Friction

Integrate frictionless protection across all types of LLMs.

Comply With Global Policy Regulations

Comply With Global Policy Regulations

Detect and block PII in real time, before it reaches your LLM.

Protect Your Brand—and Your Reputation

Protect Your Brand—and Your Reputation

Stop toxic, biased or off-brand responses that alienate users and damage brand.

Enforce Company Policies and Ensure Responsible Use

Enforce Company Policies and Ensure Responsible Use

Control AI use across your organization, ensuring precision and transparency.

Save Money and Resources

Save Money and Resources

Use fewer LLM tokens, compute and network resources because blocked prompts never reach your infrastructure.

API保護ソリューションの概要

Solution Brief: Radware LLM Firewall

Find out how our LLM Firewall solution lets you to navigate the future of AI and LLM use with confidence.

ソリューション概要を読む

特長

Inline, Pre-origin Protection

Catches user prompt before it reaches the server, blocking malicious use early on

Zero-friction Onboarding and Assimilation

Requires virtually no integrations or customer interruptions. Configure and go!

Easy Configuration

Offers master-configuration templates for multiple LLM models, prompts and applications

Visibility With Tuning

Allows extensive visibility, LLM activity dashboards and the ability to tune, adjust and improve

GigaOm gives Radware a five-star AI score and names it a Leader in its Radar Report for Application and API Security.

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Security Spotlight: What New Risks Come With LLM Use?

Extraction of Data

Extraction of Data

Attackers steal sensitive data from LLMs, exposing PII and confidential business data.

Manipulation of Outputs

Manipulation of Outputs

Manipulated LLMs create false or harmful content, spreading misinformation or hurting the brand.

Model Inversion Attacks

Model Inversion Attacks

Reverse-engineered LLMs reveal training data, exposing personal or confidential data.

Prompt Injection and System Control Hacking

Prompt Injection and System Control Hacking

Prompt injections alter the behavior of LLMs, bypassing security or leaking sensitive data.

一目でわかる

30%

Applications using AI to drive personalized adaptive user interfaces by 2026—up from 5% today

77%

Hackers that use generative AI tools in modern attacks

17%

Cyberattacks and data leaks that will include involvement from GenAI technology by 2027

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