CompliQuest

Course Overview

Traditional cybersecurity practices don't protect AI systems. Firewalls and encryption won't stop adversarial attacks that fool your model with crafted inputs. Penetration testing won't catch poisoned training data that corrupts your AI from the inside.

AI introduces entirely new attack surfaces, and most security teams don't know how to defend them.

This course shows you the threats unique to AI systems and the specific defences you need to implement.

You'll learn:

• How adversarial attacks work,
• What data poisoning looks like and how it corrupts training, 
• Model inversion and extraction attacks that steal your IP, 
• Prompt injection vulnerabilities in LLM applications, 
• Supply chain risks in ML libraries and pre-trained models, and 
• How attackers exploit AI system APIs and endpoints.

We’ll also explore traditional threats in AI context like man-in-the-middle attacks on model serving infrastructure, social engineering targeting AI researchers and data scientists, privilege escalation in ML platforms, and unencrypted data in training pipelines.

You'll learn to implement AI-specific defences like input validation and sanitization for models, adversarial training to build robust models, differential privacy techniques for training data, model monitoring and drift detection, secure model serving architectures, and ML pipeline security.

You'll cover compliance requirements like GDPR technical measures for AI data processing, EU AI Act security obligations for high-risk systems, privacy impact assessments for AI projects, and documentation requirements for AI security.

By the end, you'll know what attacks target AI specifically versus general infrastructure, how to test AI systems for adversarial robustness, how to secure ML pipelines from data collection to deployment, and how to implement monitoring that detects AI-specific threats.

Stop applying traditional security to AI systems. Start defending the actual attack surface.

Modules

• AI and Cybersecurity - Explore the unique security challenges that come with AI systems. This module covers how AI can introduce new vulnerabilities—from adversarial attacks and data poisoning to model inversion and misuse of generative tools. You'll learn core cybersecurity principles as they apply to AI, including system hardening, access controls, secure deployment practices, and incident response. By the end, you’ll know how to identify risks, protect AI assets, and build more resilient, trustworthy systems.