Defending Against Generative AI-Based Fraud

How to defend against fraudulent emails, deepfakes, and many other types of fraud attempts done with generative AI.

What you'll learn
How to prevent fraud attempts performed with generative AI
How to identify generative/false/synthetic content
How to develop new defense mechanisms against these new generative threats
How to integrate new defense mechanisms into your current organization

Requirements
A basic knowledge of fraud and defenses against it is advised (e.g. what is payment fraud and how to prevent it) is recommended, but not necessary
A basic knowledge of what generative AI is, and the content it can create is recommended, but not necessary

Description
BEATING FRAUDFraud, including payment fraud and insurance fraud, is one of the biggest problems for organizations.And new advances in terms of generative AI have only made this worse.In the world of today, organizations and individuals must be able to not only resist fraud attempts, but resist when they leverage generative AI - which, in many cases, means faster, larger-scale and more sophisticated attacks.This course will teach you how to protect against fraud that leverages generative AI.LET ME TELL YOU… EVERYTHING.Some people - including me - love to know what they're getting in a package.And by this, I mean, EVERYTHING that is in the package.So, here is a list of everything that this course covers:You'll learn the basics of generative AI and what it can do, including common models and families of models, the characteristics of generative content, and how it can be misused due to negligence or active malevolence (including biases, misinformation, impersonation and more);You'll learn the basics of fraud and its main types (identity theft, payment fraud, investment fraud, insurance fraud, account takeover), and the main factors enabling it (technological gaps, human error, process weaknesses, data breaches);You'll learn how fraud is accelerated by generative AI (mass automation, increased authenticity, pattern evasion, synthetic identity creation, etc) and its effect on the major approaches (document forgery, transaction manipulation, synthetic identity fraud, claims fraud, etc);You'll learn about an overview of the major generative content types used in fraud attacks (text, image, audio and video), including the specific approaches that each leverage, the model training requirements and data required for attackers to train such models, and how each type can be detected;You'll learn about generative text in fraud, including the models that allow it such as LLMs, the distribution channels such as email, SMS, chats, the data required to train such models, and detection mechanisms such as behavioral analysis, authentication and text validations;You'll learn about generative image in fraud, including the models that allow it such as GANs or diffusion models, the distribution channels such as deceptive documents and images in email attachments or submission portals, the data required to train such models, and detection mechanisms such as watermarks, behavioral detection or MFA;You'll learn about generative audio in fraud, including the models that allow it such as GANs or TTS, the distribution channels such as voice messaging software or for calls, the data required to train such models, and detection mechanisms such as MFA factors, callbacks, or training employees;You'll learn about generative video in fraud, including the models that allow it such as GANs for video or deep learning models, the distribution channels such as communication tools or submission portals, the data required to train such models, and detection mechanisms such as verifying communications, anti-deepfake software, MFA;

Who this course is for:
Fraud prevention/cybersecurity engineers focusing on preventing fraud, Data privacy and security professionals that want to better protect their organization's data, Employees of any organization that want to be better protected against fraud