An Interactive 5-Day Training Course

Cybersecurity Fundamentals for AI-Driven Fraud Detection

Securing Intelligent Systems to Strengthen Fraud Prevention and Digital Trust

08 - 12 Dec 2025
London
| $5950
04 - 08 May 2026
Dubai
| $5950
13 - 17 Jul 2026
London
| $5950
28 Sep - 02 Oct 2026
Amsterdam
| $5950
07 - 11 Dec 2026
London
| $5950

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.

Key Learning Outcomes

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

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.

Cybersecurity Fundamentals for AI-Driven Fraud Detection

Who Should Attend?

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

Learning Journey Breakdown

  • Introduction to AI in fraud detection systems
  • Basic cybersecurity principles and frameworks (e.g., CIA triad, NIST)
  • Key components of secure AI-driven fraud platforms
  • Understanding threats and vulnerabilities in digital fraud systems
  • Cybersecurity roles and responsibilities in AI environments
  • Data integrity, confidentiality, and availability in AI systems
  • Security controls for data ingestion, processing, and storage
  • Managing access controls and identity for fraud detection tools
  • Cloud security considerations for AI deployments
  • Monitoring and logging for fraud analytics environments
  • Adversarial machine learning: threats to AI models
  • Data poisoning and model inversion attacks
  • Insider threats and system misconfigurations
  • Vulnerabilities in open-source and third-party tools
  • Case examples of cyber incidents involving AI systems
  • Cyber risk assessments for AI-powered fraud solutions
  • Establishing cybersecurity governance frameworks
  • Compliance requirements: GDPR, ISO, and regional standards
  • Aligning fraud detection systems with IT and risk policies
  • Incident response planning for AI-related breaches
  • Best practices for secure AI model development and deployment
  • Ensuring transparency, accountability, and explainability
  • Integrating security into the fraud detection lifecycle
  • Future challenges and trends in securing intelligent fraud systems
  • Course review and next steps for implementation

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