Cyber Security · Secure AI
Protecting AI systems and data
Secure AI
AI systems introduce risks that need to be managed. Sensitive data can be exposed through prompts, models can be manipulated and outputs can be unreliable. Secure AI puts controls in place to protect your systems and data throughout the AI lifecycle.
The risks
Understanding the risks
Most AI projects open new paths into your systems. Prompts, training data, model interfaces and connected tools all need protection.
01
Data leakage
Sensitive data sent to a model, or returned by one, can leave the boundary you set.
02
Prompt injection and misuse
Attackers can use crafted inputs to override instructions or pull out information they should not see.
03
Model and supply chain risk
Third-party models, libraries and plug-ins can carry weaknesses that were never part of your plan.
04
Unchecked outputs
Wrong or biased outputs cause harm when people act on them without a check.
Our approach
Security controls throughout the AI lifecycle
01
Assess the AI estate
We map the AI systems you use or plan to use, and rate each one by risk.
02
Harden the build
We apply access control, secrets management, logging and input checks across the AI stack.
03
Test before release
We test for prompt injection, data exposure and harmful output, using recognised methods such as the OWASP Top 10 for LLM Applications.
04
Monitor in operation
We watch model behaviour and access, and respond to incidents using a tested playbook.
Next step
Want to know where your AI stands?
Start with the AI readiness assessment.