Cybersecurity Fundamentals for AI-Driven Fraud Detection

Introduction

As organisations increasingly turn to Artificial Intelligence (AI) for fraud detection, new cybersecurity challenges and risks are emerging. AI models are only as effective as the systems that support them — and without a secure environment, these advanced tools can become vulnerable to manipulation, data breaches, and adversarial attacks.This GLOMACS training course, *Cybersecurity Fundamentals for AI-Driven Fraud Detection*, bridges the knowledge gap between cybersecurity and AI-based fraud prevention. It equips professionals with the foundational cybersecurity knowledge needed to secure AI systems, protect sensitive data, and build resilience against evolving threats. The training course is tailored for professionals involved in implementing or managing AI fraud detection systems, with a focus on practical cybersecurity concepts and controls.

Objectives

By the end of this Cybersecurity Fundamentals for AI-Driven Fraud Detection training course, participants will be able to:

• Understand the intersection of cybersecurity and AI in fraud detection
• Identify vulnerabilities in AI-powered fraud detection systems
• Apply key cybersecurity principles to protect AI data, models, and infrastructure
• Recognise risks such as adversarial attacks and data poisoning
• Implement governance and risk management practices to secure AI environments

Training Methodology

This training course is delivered through instructor-led sessions, supported by real-world examples and conceptual explanations. It offers a balanced mix of cybersecurity fundamentals and AI system risks, presented in a format accessible to both technical and non-technical professionals. No programming or AI development experience is required.

Organisational Impact

By attending this Cybersecurity Fundamentals for AI-Driven Fraud Detection training course, organisations will be able to:

• Strengthen the security posture of AI-driven fraud detection systems
• Reduce the risk of data breaches, adversarial attacks, and system manipulation
• Align AI fraud detection with cybersecurity and compliance frameworks
• Build greater trust in AI tools used for fraud detection and risk assessment
• Improve collaboration between fraud, cybersecurity, and data science teams

Personal Impact

Participants will gain:

• A working knowledge of cybersecurity concepts relevant to AI and fraud detection
• Insight into securing data pipelines, AI models, and detection algorithms
• Awareness of cyber threats specific to AI systems in fraud prevention
• Skills to assess and mitigate AI-related cybersecurity risks
• Confidence to contribute to secure design and implementation of AI fraud platforms