Data Privacy, Security, and AI Ethics

Protect confidential data used in GenAI systems.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course covers essential principles and best practices for ensuring data privacy and security in Generative AI applications. Participants will explore ethical considerations around AI data usage, learn strategies to safeguard sensitive information, and understand compliance with data protection regulations in AI environments.

Learning Objectives

Participants will be able to:

• Understand key data privacy laws and regulations affecting GenAI

• Identify risks related to data security in AI workflows

• Implement data protection measures specific to AI systems

• Apply ethical frameworks to responsible AI data usage

• Manage AI projects with a focus on privacy and security compliance

Target Audience

• Data privacy officers and security professionals

• AI developers and data scientists

• Compliance and risk managers

• Business leaders overseeing AI initiatives

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies on privacy and ethics

• Practical workshops on data security tools for AI

• Group discussions on ethical dilemmas in AI data use

• Compliance checklist and policy drafting exercises

Materials Provided

• Privacy and security best practice guides

• Compliance frameworks and regulatory resources

• Sample policies for AI data governance

• Tools for securing GenAI datasets

Outcomes

• Mastery of data privacy and security challenges in GenAI

• Ability to develop and enforce AI data protection policies

• Enhanced ethical awareness in AI data handling

• Practical skills for mitigating privacy risks in AI systems

Outline / Content

Day 1: Foundations of Data Privacy and AI Ethics

• Overview of privacy laws (GDPR, CCPA, etc.)

• Ethical principles in AI data usage

Day 2: Data Security Challenges in GenAI

• Common vulnerabilities and threats

• Techniques for securing AI datasets

Day 3: Implementing Privacy and Security Controls

• Data anonymization, encryption, and access controls

• Privacy-preserving AI methods

Day 4: Governance and Compliance for AI Systems

• Creating privacy policies and compliance strategies

• Auditing and monitoring AI data practices