AI Security
Cyber security for AI with AI
Cyberlinx's helps organisations adopt AI confidently by securing the systems that power it, governing the risks it introduces, and protecting against the new attack techniques it enables.
Our Services
AI-Enabled Threat Defence
Threat actors are using AI to generate more convincing phishing, accelerate vulnerability discovery, and automate attack campaigns. We help you understand and defend against AI-augmented threats, from deepfake social engineering to AI-assisted malware and adapt your detection capabilities accordingly.
AI Application Security Testing
Security testing tailored for AI and ML systems. We assess LLM-integrated applications for prompt injection, jailbreaking, data leakage via inference, insecure plugin/tool integrations, and model inversion attacks.
Shadow AI Discovery & Management
Employees are using AI tools your security team doesn't know about. We help you discover unsanctioned AI usage across your organisation, assess the data exposure risks, and implement controls to govern AI adoption.
AI Risk Assessment
Assessment of the AI tools, models, and platforms your organisation uses identifying security and privacy risks across your AI supply chain, data pipelines, model access controls, and integration points.
AI Security Awareness Training
We deliver targeted training on AI-specific risks, including AI-generated phishing, voice cloning, and deepfakes ensuring your workforce can identify and respond to next-generation social engineering.
Secure AI Architecture & Design
Organisations building or procuring AI systems need security embedded from the ground up. We provide security architecture advisory for AI platforms covering model access controls, data classification, output filtering, audit logging, API security, and secure integration with enterprise systems.
AI Governance Framework
We help organisations develop AI governance policies, usage frameworks, and acceptable use guidelines that balance innovation with accountability. This includes AI risk classification, human oversight requirements, bias and fairness considerations.
