ضوابط الوصول إلى البيانات في أنظمة الذكاء الاصطناعي

Restrict GenAI access to only the data it’s authorized to use.

Pillar

Data – Readiness, Governance, Quality & Ethics

ملخص

This course explores the principles and implementation of data access controls within Generative AI systems to ensure that AI models only access and use authorized data. It covers strategies for safeguarding sensitive information, enforcing role-based permissions, and complying with data privacy regulations in AI workflows.

Learning Objectives

Participants will be able to:

• Understand key concepts of data access control and security in AI

• Design and implement role-based and attribute-based access controls

• Apply best practices for data segmentation and masking in AI systems

• Navigate regulatory requirements affecting data access in AI

• Monitor and audit data usage by GenAI applications

Target Audience

• Data security and compliance officers

• AI system architects and engineers

• IT governance and risk management professionals

• Data stewards and privacy officers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures on data access principles

• Practical workshops on access control configurations

• Case studies on data breaches and mitigation

• Group discussions on compliance challenges

Materials Provided

• Access control policy templates

• Sample role-based access configurations

• Tools for monitoring and auditing AI data access

Outcomes

• Ability to enforce strict data access policies in GenAI environments

• Enhanced data security and privacy compliance

• Reduced risk of unauthorized data exposure in AI workflows

• Practical knowledge of access monitoring and incident response

Outline / Content

Day 1: Fundamentals of Data Access Control in AI

• Overview of access control models (RBAC, ABAC)

• Specific challenges in AI data environments

Day 2: Designing and Implementing Access Controls

• Configuring permissions and data segmentation

• Techniques for data masking and encryption

Day 3: Compliance and Risk Management

• Regulatory frameworks (GDPR, CCPA, HIPAA)

• Policies for AI data governance

Day 4: Monitoring, Auditing, and Incident Response

• Tools for tracking AI data usage

• Handling access violations and breaches