Seven Key Considerations When Assessing AI Systems’ Security | Lawtificial Integrity

Course Content

Section 1: AI Security Fundamentals

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Seven Key Considerations When Assessing AI Systems’ Security

How to Inculcate an Understanding of Data Integrity Amongst Multidisciplinary AI Teams?

Four Key Factors That Impact AI Security

The Importance of Information Sharing Between ML Model Providers and Clients

The Hidden Dangers of ML Framework Dependencies

Recruitment Example of Vulnerability Exploit From Open-Source Programming Library

Three Key Measures to Safeguard Your ML Deployment Pipeline

LAWGAMES: AI FORT KNOX PRO

Section 2: Data Integrity and Attack Scenarios

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Four AI Attack Scenarios That Compromise Data Integrity

Seven Steps to Secure Training Data in ML Systems

Three Techniques to Safeguard Sensitive Training Data in AI Systems

Six Key Considerations When Using External Software In Your AI

Benefits of Using Open Source Code Libraries In Your AI

Hidden Data Dangers of Support Vector Machines and K-Nearest Neighbors

De-Risking Healthcare Data In SVM or KNN Models

Personal Data Within SVM and KNN Models – is it an Attack Vector?

LAWGAMES: AI FORT KNOX PRO

Section 3: Model Inversion and Membership Inference Attacks

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An Introduction to Model Inversion and Membership Inference Privacy Attacks

Understanding Model Inversion Attacks

Anatomy of an AI Model Inversion Attack in Healthcare

Anatomy of a Model Inversion Attack on Facial Recognition AI

Understanding Membership Inference Attacks

How Membership Inference Attacks Reveal People’s Identities in a Training Dataset

The Serious Impact of Membership Inference Attacks on People with HIV or Dementia

LAWGAMES: AI FORT KNOX PRO

Section 4: Black Box and White Box Attacks

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Introduction to White Box and Black Box Model Attacks

Key Differences Between Black Box and White Box ML Attacks

Why You Should Withhold Confidence Scores To Avoid Black and White Box Attacks

How to Detect Privacy Attacks by Monitoring API User Queries

How to Address the Risk of White Box Attacks When Provisioning Full ML Models

LAWGAMES: AI FORT KNOX PRO

Section 5: Addressing Adversarial Attacks

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Introduction to Adversarial Attacks

How Adversarial Attacks on ML Models Threaten People’s Legal Rights

How Adversarial Attacks Jeopardize Statistical Accuracy

Adversarial Attacks and Your Obligations Under the NIS Regulations 2018

Critical Trade-Off Between AI Explainability and Preserving Trade Secrets

LAWGAMES: AI FORT KNOX PRO

Section 6: Data Minimization Techniques

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Five Key Techniques to Help You Achieve Data Minimization

Seven Key Steps to Balance AI Functionality and Data Minimization

Six Data Focused Techniques for a Privacy Conscious Approach to AI Development

The Importance of Integrating a Data Minimal Approach in Your AI And ML Design

Five Key Techniques for Data Minimalist Machine Learning

Four-Step Strategy to Data Minimalism When Training ML Systems

Five-Step Method to Strike a Balance Between Data Minimization and Statistical Accuracy

Three Crucial Data Minimalism Techniques at the ML Inference Stage

Worked Example: Statistical Accuracy and Data Minimization in Sales Behavior Prediction AI

LAWGAMES: AI FORT KNOX PRO

Section 7: Privacy-Enhancing Methods

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Top Three Data Minimalist Privacy Enhancing Methods

Two Steps to the Application and Measurement of the Effectiveness of Privacy-Enhancing Techniques

The Significance and Challenges of Differential Privacy

Introduction to Perturbation and Controlled Randomness

Introduction to Noisy Words and Predictive Text Systems

The Impact of Perturbation on Individual-Level Accuracy

What are Privacy-Preserving Query Approaches and How Do They Work on ML Models?

Seven Key Steps to Effectively Managing Privacy Attacks on ML Models

LAWGAMES: AI FORT KNOX PRO

Section 8: Federated Learning and Synthetic Data

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Practical (Healthcare) Applications of Federated Learning

Building a Banking Fraud Detection Model Through Federated Learning

Two Steps to Synthetic Data Generation

The Importance of Balancing Statistical Realism with Re-Identification Risks

LAWGAMES: AI FORT KNOX PRO

Section 9: Data Protection and Management

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Data Protection Implications of Predictor and Target Variables

Snapshot of the Mechanisms of Prediction Algorithms

Snapshot of Training and Inference Phases Supervised Machine Learning

How to Approach Data Retention and Management in Machine Learning Lifecycle

Three Key Techniques to Address Overfitting in ML Models

LAWGAMES: AI FORT KNOX PRO

Section 10: Legal and Ethical Considerations

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Do Data Protection Laws Apply to Faceprints?

Does Converting Personal Data to Less Human-Readable Formats Mean the Data is no Longer Personal?

Does Local or On-Device Data Processing Exempt Your Organization from Data Protection Laws?

Do Data Protection Laws Apply to Pseudonymized Data?

The Goal of Converting Personal Data to Less Human-Readable Formats

LAWGAMES: AI FORT KNOX PRO

Section 11: Practical Applications and Implementation of ML Models

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Four Key Benefits of Local Inferences by Prediction ML Models

Five Key Steps in the Working of a Personalized News Recommendation Model

Is it Feasible to Run ML Models on User-Owned Smartphones?

LAWGAMES: AI FORT KNOX PRO

LAWGAMES: AI FORT KNOX PRO

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LAWGAMES: AI FORT KNOX PRO

AI Fort Knox Pro: Mastering Data Minimization and Security

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