AI Systems Hardening

Prompt injection is the leading vulnerability class in LLM-based applications. We implement multi-layered injection defences: input sanitisation and classification before model processing, system prompt hardening to reduce override surface area, context isolation to prevent instructions embedded in user data from affecting system behaviour, and post-processing validation to catch manipulated outputs before they reach end users.

Model outputs can carry harmful content, sensitive data, and policy violations — regardless of how the input arrived. We build output safety pipelines that classify, filter, and redact model responses in real time. Content moderation classifiers, PII detection and redaction, toxicity scoring, and policy-based blocklists are combined into a defence layer that sits between your model and your users, ensuring what gets surfaced is safe and appropriate.

Unrestricted access to AI inference endpoints is a significant security risk. We implement authentication and authorisation frameworks for your AI APIs — enforcing rate limiting, per-user and per-application scoping, token-based access controls, and audit logging of all model interactions. We also implement privilege separation so that different user tiers receive access to different model capabilities, reducing the blast radius of any single compromised credential.

The infrastructure hosting your AI models carries its own attack surface. We harden your deployment environment — containerised workloads, API gateways, model registries, and serving infrastructure — applying the principle of least privilege throughout. Network policies restrict lateral movement, secrets management replaces hardcoded credentials, and container images are hardened against known exploit chains. We apply infrastructure hardening checklists specific to AI workloads running on Kubernetes, cloud-managed services, and bare-metal GPU environments.

AI system security is not static — new attack techniques emerge continuously, and models update as your data and use cases evolve. We implement continuous security posture management for your AI environment: automated scanning of inference logs for anomalous patterns, alerting on behavioural drift that may indicate model tampering, regular re-assessment of prompt injection defences as new bypass techniques are published, and quarterly hardening reviews to keep your defences current.