الأمن والخصوصية في أنظمة الذكاء الاصطناعي

Protect sensitive data and ensure safe Generative AI interactions.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

ملخص

This course addresses the critical aspects of security and privacy in AI systems, with a focus on Generative AI applications. Participants will learn best practices to safeguard sensitive data, prevent unauthorized access, and comply with regulatory requirements while deploying and operating GenAI solutions responsibly.

Learning Objectives

Participants will be able to:

• Identify security risks unique to AI and GenAI systems

• Implement data protection techniques and privacy-preserving methods

• Design secure AI architectures and workflows

• Apply governance frameworks and compliance standards (e.g., GDPR, CCPA)

• Monitor and respond to security incidents in AI deployments

Target Audience

• AI and security engineers

• Data privacy officers and compliance managers

• IT and cloud infrastructure teams

• AI project leaders and risk managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies

• Hands-on labs with security tools and configurations

• Group discussions on compliance and governance

Materials Provided

• Security checklist for AI systems

• Privacy policy templates and audit guides

• Lab exercises and sample configurations

• Certificate of completion

Outcomes

• Design AI systems with integrated security and privacy controls

• Ensure compliance with relevant data protection laws

• Protect sensitive information in training, inference, and storage phases

• Respond effectively to security challenges in AI environments

Outline / Content

Day 1: Security Challenges in AI and GenAI

• Overview of AI-specific security threats and vulnerabilities

• Data privacy risks in GenAI models

• Case studies on AI security breaches

Day 2: Data Protection and Privacy Techniques

• Data anonymization, encryption, and secure data handling

• Differential privacy and federated learning concepts

• Access control and identity management for AI systems

Day 3: Secure AI Architecture and Compliance

• Designing secure AI infrastructure and workflows

• Regulatory frameworks and compliance requirements

• Building governance models for AI security

Day 4: Monitoring, Incident Response, and Best Practices

• Tools for monitoring AI system security

• Incident detection, reporting, and mitigation

• Workshop: Implementing a security plan for a GenAI deployment

• Group presentations and feedback