AI features your security team can sign off on.

LLM features create a new attack surface: prompt injection through user content, data leakage through model context, unsafe or off-brand output in front of customers. These risks are manageable — but only if they are engineered for, not patched afterwards.

When you need this

• You are shipping an LLM feature and security review is blocking it
• Your AI feature touches customer data or takes actions in real systems
• You need evidence — evals, logs, filters — not just assurances
• An incident or near-miss made AI risk suddenly concrete

What we deliver

• Threat review of your AI feature: injection paths, data exposure, abuse cases
• Guardrail design: input handling, output filtering, permission boundaries
• Red-team testing with adversarial prompts before launch
• Evaluation suites and regression tests for safety behaviour
• Monitoring and alerting for anomalous AI behaviour in production

Technologies we typically use

Questions we hear often