AI Integration Security Testing

Find AI-specific weaknesses conventional testing may not reach

Prompt injection, jailbreaks, manipulated context and guardrail bypass are not covered fully by a standard penetration test. Purpose-built testing shows whether those routes can change the system's behaviour or expose something sensitive.

Protect the data moving through your AI stack

Customer, employee and business information may pass through model providers, retrieval systems, logs and observability tools. We test where that data can be exposed, retained too broadly or accessed by the wrong user.

Check what the AI can access and do

AI features increasingly call tools, query internal systems and take actions for users. We test whether permissions, approval steps and technical controls limit those capabilities as intended.

Test the integration, not only the model

Security is a shared responsibility. Model providers protect their platforms, while your team controls how the model is connected, what information it receives, how outputs are used and which systems it can reach.

Reduce misuse, cost and availability risk

Poor limits can allow a small number of requests to drive excessive model use, disrupt the service or create unexpected cost. We test how consumption, rate limits and failure controls behave under deliberate misuse.

Give stakeholders clear evidence before wider deployment

A structured assessment helps security, product and leadership teams understand the real risk, prioritise fixes and make better-informed decisions about launch or expansion.