AI Cybersecurity

Design and implementation of security controls specific to AI systems — covering model access, input validation, output filtering, integration security, and monitoring for adversarial behaviour.

Structured threat modelling for LLM-powered applications, identifying prompt injection attack surfaces, data exfiltration vectors, insecure plugin integrations, and misuse scenarios specific to your deployment.

Data governance controls for AI workloads — covering training data handling, data minimisation, access controls, retention, and compliance alignment for AI systems that process sensitive information.

Risk assessment and ongoing management frameworks for LLM deployments. Includes risk registers, acceptable use boundaries, residual risk documentation, and escalation paths for emerging AI threats.

Development of AI security policies, acceptable use standards, and vendor assessment criteria. Aligned to emerging regulatory frameworks including the Australian Government's AI governance guidance.

Point-in-time risk assessments of existing generative AI tool deployments — including shadow AI usage. Identifies what's in use, what data it can access, and what risks that creates for your organisation.