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;

Overview

Section 1: Course Intro

Lecture 1 Course Intro

Section 2: Fundamentals

Lecture 2 Module Intro

Lecture 3 Fundamentals of Generative AI

Lecture 4 Fundamentals of Fraud

Lecture 5 Fraud with Generative AI

Lecture 6 Module Outro

Section 3: Attack Mediums/Channels

Lecture 7 Module Intro

Lecture 8 Overview

Lecture 9 Text

Lecture 10 Image

Lecture 11 Audio

Lecture 12 Video

Lecture 13 Module Outro

Section 4: Gen AI Attack Approaches

Lecture 14 Module Intro

Lecture 15 False Documents/Accounts

Lecture 16 Payment Process Abuse

Lecture 17 Claims Process Abuse

Lecture 18 Data/Account Exploitation

Lecture 19 Module Outro

Section 5: Defense Techniques

Lecture 20 Module Intro

Lecture 21 Document Verification

Lecture 22 Behavioral Analysis

Lecture 23 Additional Authentication

Lecture 24 Multichannel ID Verification

Lecture 25 Module Outro

Section 6: Implementing Protection

Lecture 26 Module Intro

Lecture 27 Changes Due to Generative AI

Lecture 28 Parallel Anomaly Analysis

Lecture 29 Enriching Model Outputs

Lecture 30 Detecting and Triaging Attacks

Lecture 31 Responding and Recovering

Lecture 32 Module Outro

Section 7: Course Outro

Lecture 33 Course Outro

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