AI Testing

What is artificial intelligence testing?

The rapid adoption of artificial intelligence (AI) technologies has introduced new security considerations for organisations integrating these solutions into their operations. Many businesses now use AI-powered systems, including large language models (LLMs) provided by major cloud vendors such as Microsoft, Amazon, and Google, as well as LLM-specialist firms like OpenAI and Anthropic.

As AI becomes increasingly embedded in critical workflows, organisations must ensure that their AI implementations remain secure against evolving threats. Our AI security testing services are designed to identify and mitigate vulnerabilities in AI integrations, safeguarding sensitive data and preventing unauthorised access.

Our team has experience across various AI deployment models, with services which can cater to the risks associated with each. Our internal methodologies are aligned with industry standard methodologies, such as OWASP and the Top 10 for LLM Applications 2025.

Our testing is classified in to one of four AI implementation types:

• Third-Party AI Integration
• Cloud-Based AI Hosting
• On-Premises AI Deployment
• Custom AI Deployments

For each of these, different risks are apparent. For example, supply-chain risk and data security concerns when using third-party implementations, to infrastructure security risks and design considerations for on-premises deployments.

Our AI specific testing services

We have multiple AI specific services, as well as offering custom services for assessing specific risks of AI implementations.

Prompt Injection Attacks

System prompts and input restrictions play a critical role in controlling AI-generated outputs. However, prompt injection attacks can be used to bypass these restrictions, enabling attackers to manipulate AI behaviour, extract restricted information, or alter decision-making logic.

We can perform prompt injection security testing to evaluate the resilience of AI models against adversarial manipulation. This includes testing whether system prompts can be overridden, metadata can be modified, or security policies can be circumvented through direct or indirect input manipulation.

Training Data Security

AI models trained on company data must be carefully curated to prevent unintended data exposure. If sensitive information is included in training datasets, it may be retrievable through interactions with the AI system, potentially leading to data leaks involving credentials, encryption keys, or personally identifiable information (PII).

We can conduct training data security assessments to identify and mitigate risks associated with model training. This includes reviewing datasets for sensitive information and implementing robust data sanitisation techniques to prevent leakage through AI-generated responses.

Custom Services

As with most of our service lines, we can also accommodate custom requirements. This is often scenario based, where certain questions need to be answered, or specific goals achieved. An example set of scenarios may be: “Can an internal user use our AI to get access to data they shouldn’t be able to?”, or “Can someone on our network see all of our AI chat logs?”.

How does it work in conjunction with other testing services?

One of the goals with testing of complex services such as AI is to understand your needs. In many situations, AI is deployed in tandem with a classic application, or standard infrastructure which fits in to another service.

In cases like this, we carry out testing as part of the relevant core service. For example, if AI is used as a feature within a web application, we would include web application testing. From there, we expand the methodology with additional test cases to address the risks introduced by the AI component. This approach ensures we assess both the security of the underlying application and the AI itself, while taking into account the context in which the AI is used instead of treating it as a standalone system.