ISO 27091 — Privacy in AI Systems

LAPT

London Academy of Professional Training

Master Certificate Level 6-7 Leadership ISO IT & Related Technologies

REF: IIT-AII-27091

Subjects

500

Total Marks

65%

Pass Mark

Lifetime

Validity

Who Is It For

This certification is intended for professionals in leadership roles such as Chief Information Officers, Data Protection Officers, and AI Project Managers who possess significant experience in information security and are looking to enhance their expertise in managing privacy within AI systems.

Prerequisites

None

Awarding Body: LAPT — London Academy of Professional Training

Curriculum Overview

1 Leadership in AI Privacy 5 chapters · 6 classes · 100 marks

Understanding Privacy Principles in AI Systems 6 classes

1.1 Define Key Privacy Principles in AI Context

1.2 Identify Legal and Ethical Frameworks Impacting AI Privacy

1.3 Analyze Case Studies of Privacy Failures in AI Systems

1.4 Assess the Role of Data Minimization in AI Privacy

1.5 Develop Strategies for Enhancing Transparency in AI Systems

1.6 Create an Action Plan for Implementing Privacy Principles in AI

Risk Assessment Framework for AI Privacy

Governance Structures for AI Privacy Management

Implementing Privacy by Design in AI Development

Leading Ethical AI Practices and Compliance

2 Audit and Compliance Processes 5 chapters · 30 classes · 75 marks

Understanding ISO 27091 and Its Importance in AI Privacy 6 classes

1.1 Define ISO 27091 and Its Relevance to AI Privacy

1.2 Identify Key Principles of ISO 27091 in AI Systems

1.3 Explain the Role of Leadership in Implementing ISO 27091

1.4 Analyze Common Compliance Challenges with ISO 27091 in AI

1.5 Evaluate Case Studies: Successful ISO 27091 Implementation in AI

1.6 Develop an Action Plan for ISO 27091 Compliance in Your Organization

Key Audit Principles and Frameworks for AI Systems 6 classes

2.1 Identify Key Audit Principles of AI Systems

2.2 Analyze ISO 27091 Compliance Requirements

2.3 Explore Frameworks for Auditing AI Systems

2.4 Develop Audit Objectives for AI Implementations

2.5 Assess Risks and Controls in AI Audit Processes

2.6 Implement Effective Audit Strategies for AI Privacy

Risk Assessment Methodologies for AI Privacy Compliance 6 classes

3.1 Identify Key Risks in AI Privacy Management

3.2 Analyze Existing Compliance Frameworks for AI Systems

3.3 Evaluate Risk Assessment Tools for AI Privacy

3.4 Develop a Privacy Risk Assessment Matrix for AI Projects

3.5 Formulate Risk Mitigation Strategies for Identified AI Privacy Risks

3.6 Implement a Continuous Monitoring Plan for AI Privacy Compliance

Conducting Compliance Audits in AI Environments 6 classes

4.1 Identify Key Compliance Requirements for AI Systems

4.2 Develop an Audit Framework Tailored for AI Environments

4.3 Plan and Schedule AI Compliance Audits Effectively

4.4 Implement Data Privacy Measures During Audits

4.5 Analyze Audit Findings in the Context of AI Data Usage

4.6 Create Actionable Recommendations to Enhance Compliance

Reporting and Continuous Improvement in AI Privacy Audits 6 classes

5.1 Analyze Current Reporting Standards for AI Privacy Audits

5.2 Identify Key Metrics for Continuous Improvement in AI Privacy

5.3 Develop Effective Reporting Templates for AI Privacy Findings

5.4 Implement a Feedback Loop for AI Privacy Audit Findings

5.5 Create an Action Plan for Addressing Compliance Gaps

5.6 Evaluate the Impact of Improvements on AI Privacy Management

3 Strategic Stakeholder Engagement 5 chapters · 30 classes · 75 marks

Understanding Stakeholder Dynamics in AI Privacy 6 classes

1.1 Identify Key Stakeholders in AI Privacy

1.2 Analyze Stakeholder Needs and Concerns

1.3 Map Stakeholder Influence on AI Privacy Dynamics

1.4 Develop Communication Strategies for Stakeholder Engagement

1.5 Facilitate Stakeholder Workshops on AI Privacy Issues

1.6 Evaluate Stakeholder Feedback to Enhance AI Privacy Policies

Mapping Stakeholder Influence and Interest 6 classes

2.1 Identify Key Stakeholders in AI Privacy

2.2 Analyze Stakeholder Influence on AI Privacy Decisions

2.3 Assess Stakeholder Interest Levels in AI Privacy Policies

2.4 Develop a Stakeholder Influence Matrix for AI Systems

2.5 Prioritize Stakeholders Based on Influence and Interest

2.6 Create a Strategic Engagement Plan for Key Stakeholders

Developing Stakeholder Engagement Strategies 6 classes

3.1 Identify Key Stakeholders for AI Privacy

3.2 Analyze Stakeholder Interests and Concerns

3.3 Develop a Stakeholder Engagement Matrix

3.4 Create Effective Communication Strategies for Stakeholders

3.5 Implement Feedback Mechanisms for Stakeholder Input

3.6 Evaluate and Adjust Engagement Strategies Based on Feedback

Communicating Privacy Measures to Stakeholders 6 classes

4.1 Identify Key Stakeholders for Privacy Communication

4.2 Analyze Stakeholder Concerns Regarding AI Privacy

4.3 Develop Clear Messages on Privacy Measures in AI

4.4 Utilize Effective Communication Channels for Stakeholder Engagement

4.5 Address Stakeholder Feedback on Privacy Initiatives

4.6 Evaluate the Impact of Communication Strategies on Stakeholder Trust

Evaluating and Adapting Stakeholder Engagement Approaches 6 classes

5.1 Identify Key Stakeholder Characteristics and Needs

5.2 Assess Current Stakeholder Engagement Strategies

5.3 Analyze the Impact of AI on Stakeholder Engagement

5.4 Develop Adaptive Stakeholder Engagement Frameworks

5.5 Implement Feedback Mechanisms for Stakeholder Involvement

5.6 Evaluate and Refine Engagement Approaches Based on Outcomes

4 Legal Frameworks for AI 5 chapters · 30 classes · 75 marks

Understanding AI and Privacy: Key Concepts and Definitions 6 classes

1.1 Define Key AI Privacy Terms

1.2 Explore the Concept of Data Minimization

1.3 Identify Privacy Risks in AI Systems

1.4 Analyze Legal Frameworks Governing AI and Privacy

1.5 Evaluate the Role of Consent in AI Privacy

1.6 Apply Privacy-by-Design Principles in AI Development

Regulatory Landscape: Global and UK-Specific Legal Frameworks for AI 6 classes

2.1 Explore the Evolution of AI Regulations Globally

2.2 Analyze Key UK Legislation Impacting AI Development

2.3 Identify Core Principles of Data Protection in AI Systems

2.4 Evaluate Compliance Challenges in AI Regulation

2.5 Compare International AI Regulatory Approaches

2.6 Propose Best Practices for Navigating the AI Regulatory Landscape

Risk Management in AI: Identifying and Mitigating Privacy Risks 6 classes

3.1 Understand Key Privacy Risks in AI Systems

3.2 Analyze Legal Frameworks Impacting AI Privacy

3.3 Identify Stakeholder Responsibilities in Privacy Risk Management

3.4 Assess Current Risk Management Practices in AI

3.5 Develop Strategies for Mitigating Privacy Risks

3.6 Implement a Risk Management Plan for AI Privacy

Ethics and Accountability in AI: Legal Obligations and Best Practices 6 classes

4.1 Define Key Ethical Principles in AI Systems

4.2 Explore Legal Obligations for AI Developers

4.3 Identify Best Practices for Accountability in AI

4.4 Analyze Real-world Case Studies of AI Ethical Dilemmas

4.5 Evaluate Impact of Non-compliance with AI Regulations

4.6 Develop an Accountability Framework for AI Implementation

The Future of AI Legislation: Emerging Trends and Challenges 6 classes

5.1 Analyze Global Trends in AI Legislation

5.2 Examine Key Privacy Standards Impacting AI Development

5.3 Identify Emerging Regulatory Challenges in AI Implementation

5.4 Discuss the Role of Stakeholders in Shaping AI Policies

5.5 Evaluate Case Studies of AI Legislative Responses

5.6 Propose Strategic Approaches to Comply with Future AI Laws

5 ISO 27091 Compliance 5 chapters · 30 classes · 100 marks

Understanding ISO 27091: Framework and Principles 6 classes

1.1 Define ISO 27091 and Its Importance in AI Privacy

1.2 Explore Key Principles of ISO 27091 Compliance

1.3 Identify Stakeholders in ISO 27091 Implementation

1.4 Analyze the Framework of ISO 27091 Compliance

1.5 Assess Risks and Challenges in Implementing ISO 27091

1.6 Develop a Plan for Achieving ISO 27091 Compliance

Identifying Privacy Risks in AI Implementations 6 classes

2.1 Define Privacy Risks in AI Systems

2.2 Identify Key Stakeholders in AI Privacy

2.3 Assess AI Data Handling Practices

2.4 Evaluate Legal and Regulatory Requirements

2.5 Analyze Potential Impact of Privacy Risks

2.6 Develop Mitigation Strategies for Privacy Risks

Establishing Compliance and Governance Structures 6 classes

3.1 Assess Current Governance Structures for Privacy Compliance

3.2 Identify Key Stakeholders in ISO 27091 Implementation

3.3 Develop a Privacy Governance Policy Framework

3.4 Define Roles and Responsibilities for Compliance

3.5 Establish Monitoring and Reporting Mechanisms

3.6 Create an Action Plan for Continuous Improvement in Governance

Developing Privacy Policies and Procedures for AI 6 classes

4.1 Identify Key Components of Privacy Policies for AI Systems

4.2 Analyze Regulatory Requirements for AI Privacy Compliance

4.3 Develop Risk Assessment Procedures for AI Privacy

4.4 Create Draft Privacy Policies Tailored for AI Applications

4.5 Review and Revise Draft Policies Based on Stakeholder Feedback

4.6 Implement Procedures for Ongoing Privacy Policy Evaluation

Auditing and Continuous Improvement in Compliance 6 classes

5.1 Identify Key Stakeholders for Auditing ISO 27091 Compliance

5.2 Establish Audit Objectives and Scope for AI Systems

5.3 Develop an Audit Plan and Timeline for Continuous Improvement

5.4 Implement Effective Data Collection Techniques for Auditing

5.5 Analyze Findings and Measure Compliance Against ISO 27091 Standards

5.6 Create a Continuous Improvement Framework Post-Audit

6 Privacy Risks in AI 5 chapters · 30 classes · 75 marks

Understanding Privacy Fundamentals in AI Systems 6 classes

1.1 Define Privacy and Its Importance in AI Systems

1.2 Identify Key Privacy Risks Associated with AI Technologies

1.3 Explore Regulatory Frameworks Impacting AI Privacy

1.4 Analyze Real-World Case Studies of Privacy Breaches in AI

1.5 Assess Strategies for Mitigating Privacy Risks in AI Systems

1.6 Implement Best Practices for Privacy Governance in AI Development

Identifying Privacy Risks in AI Development 6 classes

2.1 Define Key Privacy Concepts in AI

2.2 Identify Common Privacy Risks in AI Development

2.3 Analyze Real-World Examples of Privacy Failures in AI

2.4 Assess Privacy Risks Using Risk Assessment Frameworks

2.5 Recommend Best Practices for Mitigating Privacy Risks in AI

2.6 Develop a Privacy Risk Management Plan for AI Projects

Assessing Impact of AI Algorithms on Privacy 6 classes

3.1 Identify Key Privacy Concerns in AI Algorithms

3.2 Analyze Data Collection Methods of AI Systems

3.3 Evaluate the Impact of Bias in AI on Privacy

3.4 Assess Legal and Ethical Implications of AI Privacy

3.5 Develop Strategies for Mitigating Privacy Risks in AI

3.6 Create a Privacy Impact Assessment Framework for AI Projects

Mitigating Privacy Risks in AI Deployment 6 classes

4.1 Identify Key Privacy Risks in AI Systems

4.2 Assess Impact of Privacy Risks on Stakeholders

4.3 Explore Legal and Ethical Frameworks for Privacy in AI

4.4 Develop Mitigation Strategies for Privacy Risks

4.5 Implement Best Practices for Data Protection in AI

4.6 Evaluate the Effectiveness of Privacy Risk Mitigation Measures

Establishing Governance and Compliance for AI Privacy 6 classes

5.1 Identify Key Privacy Regulations Affecting AI Governance

5.2 Assess Privacy Risks Associated with AI Deployment

5.3 Develop a Framework for AI Privacy Compliance

5.4 Implement Data Protection Impact Assessments for AI Systems

5.5 Establish Roles and Responsibilities for AI Privacy Governance

5.6 Evaluate and Enhance AI Privacy Practices Through Continuous Monitoring

Assessment Breakdown

50%

Theory

35%

Practical

15%

Project

Passing Mark: 325 / 500 (65%)

Methods: Written Examination, Practical Assignment, Portfolio Assessment

How to Enrol

Website: lapt.org

Email: info@lapt.org

Phone: +44 7513 283044

Address: 85 Great Portland Street, W1W 7LT, United Kingdom

Hours: Monday – Friday, 9AM – 5PM

📩 Send Enquiry