What is AI / LLM Red Teaming?

AI / LLM Red Teaming is a security assessment that evaluates how artificial intelligence systems behave under adversarial conditions. The goal is to identify weaknesses, unintended behaviors, and potential security risks before they can be exploited.

How is AI / LLM Red Teaming different from a traditional penetration test?

Traditional penetration testing focuses on infrastructure, applications, networks, and security controls. AI / LLM Red Teaming focuses specifically on the behavior, controls, integrations, and security risks associated with AI systems and large language models.

What types of AI systems can be tested?

Internally developed AI applications, public-facing AI systems, chatbots, AI assistants, retrieval-augmented generation (RAG) implementations, and other AI-enabled workflows can all be tested.

What risks can AI / LLM Red Teaming identify?

Testing may uncover prompt injection vulnerabilities, data exposure risks, unsafe outputs, authorization issues, insecure integrations, excessive permissions, and other AI-specific security concerns.

When should organizations conduct AI / LLM Red Teaming?

Organizations should consider testing before deployment, after significant model or application changes, and whenever AI systems are granted access to sensitive data, business processes, or customer interactions.

AI / LLM Red Teaming

AI and Large Language Models (LLMs) introduce new attack surfaces, new risks, and new ways for attackers to influence systems. AI / LLM Red Teaming helps identify weaknesses, test safeguards, and understand how AI systems behave under real-world adversarial conditions.

REALITY

AI changes the rules

Traditional security programs were built to protect applications, infrastructure, and data. AI allows organizations a new way to generate content, make recommendations, process sensitive information, and interact directly with users. Those systems require a different type of testing.

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RISK

AI systems can be manipulated

Prompt injection, data leakage, unsafe outputs, excessive permissions, insecure integrations, and unintended behavior can create risk even when the underlying infrastructure is secure. The real test is whether AI models behave as intended when challenged.

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RESPONSE

Put your AI under pressure

We simulate real-world adversarial techniques to test how AI systems respond when users, attackers, or unexpected inputs attempt to bypass safeguards. Our team evaluates controls, identifies weaknesses, and helps organizations understand where AI introduces risk.

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OUTCOME

Build trust through testing

AI adoption moves quickly. AI / LLM Red Teaming helps identify weaknesses before deployment, validate security controls, and improve confidence in the systems they are putting in front of customers, employees, and stakeholders.

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Asking if AI works is the easy question. The harder question is asking what happens when someone intentionally tries to make it fail.

DOMINION DELIVERS

Innovation without blind trust

AI attack surface assessment

We identify how AI systems interact with users, data, applications, integrations, and supporting infrastructure to understand potential areas of exposure.