AI Security

From red teaming to governance: secure your AI systems from model to deployment.

AI Red Teaming

Simulate adversarial attacks against your AI systems. We test LLMs, AI APIs, autonomous agents, and ML pipelines using the same techniques attackers use.

LLM prompt injection & jailbreaking
Adversarial input generation
Model extraction & data exfiltration testing
Agentic AI security assessments
OWASP LLM Top 10 alignment

AI Strategy & Governance

Frameworks for the safe adoption of Generative AI. We help you navigate emerging standards without stifling innovation.

EU AI Act readiness assessment
NIST AI RMF implementation
ISO 42001 gap analysis
AI risk register development
Regulatory compliance mapping

AI Security Architecture Review

Review your AI system architecture before deployment. We identify design-level weaknesses in ML pipelines, data flow, model serving infrastructure, and access controls.

ML pipeline security review
Model deployment architecture assessment
Data flow & access control analysis
Supply chain security for AI components
Threat modelling for AI systems

Common Questions

What is AI red teaming and who needs it?

AI red teaming is adversarial testing of AI systems: LLMs, chatbots, AI agents, and any system that uses machine learning to make decisions. It tests for prompt injection, data leakage, jailbreaks, and AI-specific attack vectors.

You need it if you deploy customer-facing AI, use LLMs in tools that access sensitive data, or integrate AI into decision-making workflows.

What's the difference between AI red teaming and penetration testing?

Penetration testing targets infrastructure, applications, and networks. AI red teaming targets the model itself and its integration points. The attacker exploits how the AI thinks, not how the server is configured.

They're complementary. A fully secured AI deployment needs both: traditional penetration testing for the infrastructure, and AI red teaming for the model and application layer.

Do I need an AI security assessment if I use third-party AI tools?

Yes, if those tools process your data or interact with your customers. When you integrate a third-party LLM API, your prompt handling, data sanitisation, output filtering, and access controls all become attack surface. The model provider secures the model. You secure how your application uses it.