ISO 27090 — Cybersecurity AI Security

LAPT

London Academy of Professional Training

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

REF: IIT-AII-27090

Subjects

500

Total Marks

65%

Pass Mark

Lifetime

Validity

Who Is It For

This certification is intended for senior cybersecurity professionals, including Chief Information Security Officers and Heads of AI Security, who possess significant experience in information security and seek to deepen their expertise in managing AI-related cybersecurity risks.

Prerequisites

None

Awarding Body: LAPT — London Academy of Professional Training

Curriculum Overview

1 Evaluating Cybersecurity Strategies Effectiveness 0 chapters · 75 marks

2 Security Awareness and Culture Development 5 chapters · 24 classes · 75 marks

Understanding the Importance of Security Awareness in Cybersecurity 6 classes

1.1 Identify Key Components of Security Awareness

1.2 Assess the Current Security Culture in Your Organization

1.3 Explore Common Cyber Threats and Their Impact

1.4 Develop Strategies to Enhance Security Awareness

1.5 Create Engaging Security Training Programs for Employees

1.6 Evaluate the Effectiveness of Security Awareness Initiatives

Identifying Common Cybersecurity Threats and Vulnerabilities 6 classes

2.1 Define Cybersecurity Threats and Vulnerabilities

2.2 Identify Major Types of Cybersecurity Threats

2.3 Analyze Common Vulnerabilities in Digital Systems

2.4 Explore Real-World Examples of Cybersecurity Breaches

2.5 Assess Organizational Risk Factors Related to Cyber Threats

2.6 Develop Strategies for Mitigating Cybersecurity Risks

Building a Cybersecurity Awareness Program 6 classes

3.1 Identify Key Components of a Cybersecurity Awareness Program

3.2 Assess Current Organizational Security Culture

3.3 Develop Engaging Training Materials for Cybersecurity Awareness

3.4 Implement Effective Communication Strategies for Program Launch

3.5 Measure the Impact of Cybersecurity Awareness Initiatives

3.6 Foster Continuous Improvement in Cybersecurity Culture

Engaging Employees in Cybersecurity Practices 6 classes

4.1 Foster a Cybersecurity Culture in the Workplace

4.2 Identify Common Cyber Threats and Vulnerabilities

4.3 Recognize Security Best Practices for Daily Operations

4.4 Encourage Open Communication About Cybersecurity Concerns

4.5 Develop Engaging Training Programs for Staff

4.6 Implement Feedback Mechanisms to Improve Cyber Awareness

Evaluating and Sustaining Security Awareness Initiatives

3 Data Privacy and Compliance in AI Systems 5 chapters · 30 classes · 50 marks

Understanding Data Privacy Principles in AI Systems 6 classes

1.1 Define Key Data Privacy Principles in AI Systems

1.2 Explore the Importance of Consent in Data Handling

1.3 Analyze Data Minimization Strategies for AI Applications

1.4 Examine User Rights and AI: Access, Deletion, and Corrections

1.5 Assess Compliance Risks and Accountability in AI Systems

1.6 Implement Best Practices for Data Privacy in AI Development

Regulatory Frameworks Impacting AI Data Practices 6 classes

2.1 Analyze Key Regulatory Frameworks Impacting AI Data Practices

2.2 Identify Data Privacy Principles Under GDPR for AI Systems

2.3 Examine Compliance Requirements for AI in Data Protection Legislation

2.4 Evaluate the Role of National Data Protection Authorities in AI Governance

2.5 Assess the Impact of Global Regulatory Trends on AI Deployment

2.6 Develop a Compliance Checklist for AI Data Privacy Practices

Risk Assessment and Management for AI Data Compliance 6 classes

3.1 Identify Key Risks in AI Data Processing

3.2 Evaluate Compliance Requirements for AI Systems

3.3 Develop a Risk Assessment Framework for AI

3.4 Analyze Common Vulnerabilities in AI Data Security

3.5 Implement Mitigation Strategies for Identified Risks

3.6 Review and Update Risk Management Practices Regularly

Implementing Data Protection by Design in AI Development 6 classes

4.1 Identify Key Principles of Data Protection by Design in AI

4.2 Analyze Regulatory Requirements for AI Data Protection

4.3 Evaluate Existing AI Systems for Data Privacy Compliance

4.4 Design an Implementation Plan for Data Protection in AI Projects

4.5 Develop Risk Assessment Strategies for AI Data Privacy

4.6 Create a Data Protection Audit Checklist for AI Applications

Monitoring and Auditing AI Systems for Data Privacy Compliance 6 classes

5.1 Identify Key Regulations Impacting AI Data Privacy

5.2 Understand the Role of Monitoring in Compliance Frameworks

5.3 Develop a Data Privacy Audit Checklist for AI Systems

5.4 Implement Continuous Monitoring Techniques for AI Compliance

5.5 Analyze Case Studies of AI Compliance Failures

5.6 Create a Compliance Reporting Strategy for AI Systems

4 Incident Response Planning for AI 5 chapters · 30 classes · 75 marks

Understanding Incident Response in AI Contexts 6 classes

1.1 Define Incident Response in AI Contexts

1.2 Identify Key Components of an AI Incident Response Plan

1.3 Assess Risks and Threats Unique to AI Systems

1.4 Develop Response Strategies for AI Incidents

1.5 Implement Communication Protocols During an AI Incident

1.6 Evaluate and Improve Incident Response Plans for AI

Frameworks and Standards for AI Incident Response Planning 6 classes

2.1 Analyze Existing AI Incident Response Frameworks

2.2 Identify Key Standards for AI Security Compliance

2.3 Evaluate the Role of Stakeholders in Incident Response

2.4 Develop a Tailored Incident Response Plan for AI

2.5 Practice Incident Response Scenarios in AI Contexts

2.6 Assess and Improve Incident Response Plans Post-Incident

Risk Assessment and Threat Modeling for AI Systems 6 classes

3.1 Identify Key AI Risks in Incident Response

3.2 Analyze Threat Vectors Specific to AI Systems

3.3 Evaluate Vulnerabilities in AI Model Architectures

3.4 Assess Impact and Likelihood of AI Threats

3.5 Create a Risk Mitigation Strategy for AI Incidents

3.6 Implement Continuous Monitoring for AI Threats

Developing AI-Specific Incident Response Procedures 6 classes

4.1 Understand the Unique Challenges of AI in Incident Response

4.2 Identify Key Stakeholders for AI Incident Management

4.3 Develop AI-Specific Incident Detection Techniques

4.4 Create AI Incident Response Team Roles and Responsibilities

4.5 Design an AI Incident Response Playbook

4.6 Conduct a Tabletop Exercise for AI Incident Scenarios

Testing, Training, and Continuous Improvement for AI Incident Responses 6 classes

5.1 Assess Current AI Incident Response Models

5.2 Develop Simulation Scenarios for AI Incidents

5.3 Conduct Tabletop Exercises for Team Preparedness

5.4 Analyze Response Outcomes and Identify Gaps

5.5 Implement Continuous Training Programs for Staff

5.6 Review and Revise Incident Response Plans Regularly

5 Implementing ISO 27090 Standards 5 chapters · 30 classes · 125 marks

Understanding ISO 27090: Overview and Importance in Cybersecurity AI 6 classes

1.1 Define ISO 27090 and its Role in Cybersecurity AI

1.2 Identify Key Components and Principles of ISO 27090

1.3 Analyze the Importance of ISO 27090 for AI Security

1.4 Explore Case Studies: ISO 27090 Implementation in Organizations

1.5 Evaluate the Benefits and Challenges of Adopting ISO 27090

1.6 Develop a Strategy for Implementing ISO 27090 Standards in Your Organization

Key Principles and Frameworks of ISO 27090 Implementation 6 classes

2.1 Understand Key Principles of ISO 27090 Standards

2.2 Explore the Framework for ISO 27090 Implementation

2.3 Identify Stakeholders in ISO 27090 Compliance

2.4 Assess Current Cybersecurity Posture Against ISO 27090

2.5 Develop an Action Plan for ISO 27090 Adoption

2.6 Evaluate the Effectiveness of ISO 27090 Implementation

Risk Assessment and Management in Cybersecurity AI Under ISO 27090 6 classes

3.1 Identify Key Risks in Cybersecurity AI Systems

3.2 Analyze Vulnerabilities in AI Implementation

3.3 Evaluate Threats Specific to Cybersecurity AI

3.4 Develop a Risk Assessment Framework for AI

3.5 Mitigate Risks Through Effective Management Strategies

3.6 Implement Continuous Monitoring for AI Risk Management

Developing Policies and Procedures for ISO 27090 Compliance 6 classes

4.1 Identify Core Components of ISO 27090 Policies

4.2 Develop Risk Assessment Procedures for Compliance

4.3 Establish Data Protection Guidelines under ISO 27090

4.4 Create Incident Response Policies for AI Security

4.5 Integrate Training and Awareness Programs for ISO Compliance

4.6 Evaluate and Revise Policies for Continuous Improvement

Assessing and Maintaining ISO 27090 Compliance in Cybersecurity AI 6 classes

5.1 Evaluate Current Cybersecurity AI Practices Against ISO 27090 Standards

5.2 Identify Gaps in Compliance and Risk Management Strategies

5.3 Develop a Compliance Assessment Framework for Cybersecurity AI

5.4 Implement Continuous Monitoring Techniques for ISO 27090 Compliance

5.5 Conduct Regular Audits to Ensure Ongoing ISO 27090 Compliance

5.6 Create an Action Plan for Addressing Non-Compliance Issues in Cybersecurity AI

6 AI Threat Assessment and Risk Management 5 chapters · 30 classes · 100 marks

Understanding AI Threat Landscape 6 classes

1.1 Identify Key AI Threats in Cybersecurity

1.2 Analyze the Impact of AI Vulnerabilities on Systems

1.3 Evaluate Real-World Case Studies of AI Threats

1.4 Assess Risk Levels Associated with AI Technologies

1.5 Develop Strategies for Mitigating AI-Related Risks

1.6 Create an AI Threat Assessment Framework for Organizations

Identifying AI-Specific Risks 6 classes

2.1 Analyze the Unique Risks Associated with AI Technologies

2.2 Evaluate Case Studies of AI Security Breaches

2.3 Identify Vulnerabilities in AI Systems and Algorithms

2.4 Assess the Impact of AI on Organizational Risk Profiles

2.5 Develop Strategies to Mitigate AI-Specific Risks

2.6 Create an AI Risk Assessment Framework for Implementation

Risk Assessment Frameworks for AI Systems 6 classes

3.1 Identify Key Components of Risk Assessment Frameworks for AI

3.2 Analyze Different Risk Assessment Models Applied to AI Systems

3.3 Evaluate Vulnerabilities Specific to AI Technologies

3.4 Develop Risk Scenarios Relevant to AI Implementation

3.5 Prioritize Risks Using a Risk Matrix for AI Systems

3.6 Formulate Mitigation Strategies for Identified AI Risks

Mitigation Strategies for AI Threats 6 classes

4.1 Identify Common AI Threats in Cybersecurity

4.2 Analyze Vulnerabilities of AI Systems

4.3 Develop Risk Assessment Frameworks for AI

4.4 Implement Mitigation Techniques for Identified Risks

4.5 Monitor AI Systems for Emerging Threats

4.6 Evaluate the Effectiveness of Mitigation Strategies

Implementing an AI Risk Management Program 6 classes

5.1 Identify Key Components of an AI Risk Management Program

5.2 Conduct a Threat Assessment for AI Systems

5.3 Develop Risk Assessment Methodologies for AI Applications

5.4 Establish Metrics for Evaluating AI Risks

5.5 Create an Action Plan for Mitigating AI Risks

5.6 Communicate AI Risk Management Strategies to Stakeholders

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

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