Introduction

Artificial Intelligence has become a critical capability in modern fraud detection, enabling organisations to identify suspicious behaviour at scale and with greater accuracy. However, AI-driven fraud detection systems introduce new cybersecurity risks that can undermine their effectiveness if not properly secured. Data manipulation, adversarial attacks, model tampering, and infrastructure weaknesses can compromise fraud outcomes and erode organisational trust.

This Cybersecurity Fundamentals for AI-Driven Fraud Detection training course addresses the security foundations required to protect AI-enabled fraud detection environments. It focuses on how cybersecurity principles must be embedded across data pipelines, model operations, and supporting infrastructure to ensure reliable and defensible fraud outcomes. Participants gain a practical understanding of how cybersecurity failures can distort AI decisions and expose organisations to regulatory, financial, and reputational risk.

Key focus areas include:

 

Key Learning Outcomes

At the end of this training course, participants will be able to:

 

Training Methodology

This training course is delivered through structured instructor-led sessions supported by practical examples and applied explanations. It balances cybersecurity fundamentals with AI fraud detection risks, enabling participants from both technical and non-technical backgrounds to understand, assess, and manage cybersecurity threats affecting intelligent fraud systems.

Cybersecurity Fundamentals for AI-Driven Fraud Detection

Who Should Attend?

This training course is ideal for professionals seeking to strengthen cybersecurity controls around AI-based fraud detection, including:

  • Fraud and financial crime specialists
  • Cybersecurity and information security professionals
  • Risk and compliance officers
  • Data governance and analytics managers
  • Internal audit and assurance professionals
  • Managers overseeing AI-enabled fraud solutions

Course Outline

Day 1

Foundations of Cybersecurity and AI in Fraud Detection

  • 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
Day 2

Securing AI Data and Infrastructure

  • 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
Day 3

Cyber Threats and Risks in AI Fraud Detection

  • 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
Day 4

Risk Management and Governance

  • 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
Day 5

Building Resilient and Secure AI Systems

  • 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

International Standards & Professional Alignment

Our training courses are aligned with internationally recognised professional standards and frameworks across leadership, strategy, finance, governance, risk, compliance, and audit. By integrating globally trusted models, we ensure learners develop practical, relevant, and industry-recognised capabilities.

Our trainings draw on leading international standards and professional frameworks, including ISO, ISACA, COSO, OECD, IIA, FATF, Basel, IFRS/ISSB, GRI, NIST, CPD, ILM and the OECD AI Principles. This alignment ensures consistency with global best practices across financial management, risk oversight, digital governance, sustainability, and strategic decision-making..

Designed in alignment with globally recognised professional bodies, our courses support continuous professional development, strengthen organisational capability, and provide clear pathways toward professional certifications valued worldwide.

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FAQs

AI-driven fraud detection systems rely on large volumes of sensitive data and automated decision-making. Without strong cybersecurity controls, these systems can be manipulated, compromised, or disrupted, leading to false outcomes and increased fraud exposure.  

Common threats include data poisoning, adversarial attacks on models, unauthorised access to datasets, infrastructure misconfigurations, and vulnerabilities in third-party or open-source components used within AI environments.  

No prior programming or AI development experience is required. The training course is designed to explain cybersecurity and AI risks conceptually, making it accessible to both technical and non-technical professionals.  

The course explains how cybersecurity governance, risk assessments, and compliance frameworks can be applied to AI-driven fraud detection systems, supporting regulatory alignment and defensible operational practices.  

Organisations using AI for fraud detection benefit by improving system resilience, reducing cyber risk, strengthening digital trust, and improving collaboration between fraud, cybersecurity, and risk functions.

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