Course Format & Delivery Details Self-Paced. Immediate Access. Lifetime Value. 24/7 Global Learning.
Welcome to a new standard in professional cybersecurity education—designed specifically for the unique challenges faced by AI-driven enterprises. This is not just another course; it’s a precision-engineered mastery program for professionals who demand clarity, credibility, and immediate applicability in their careers. - Self-Paced Learning Experience: Begin today, progress at your own speed. No rigid timelines, no missed deadlines—only structured guidance tailored to your schedule.
- Immediate Online Access: The instant you enroll, you gain full entry to the complete curriculum. No waiting, no onboarding delays—just instant access to career-advancing knowledge.
- On-Demand Learning, Zero Time Conflicts: Access the entire course 24/7 from any location worldwide. Perfect for global professionals across time zones, busy executives, and high-impact teams operating in dynamic environments.
- Real Results in Weeks, Not Years: Most learners report successfully applying core implementation strategies within the first 30 days. The average completion time is 6–8 weeks with just 5–7 hours per week of focused engagement—ideal for professionals balancing work and growth.
- Lifetime Access, Forever Free Updates: Once enrolled, you never lose access. This includes all future content updates, evolving AI-risk frameworks, and revised compliance guidance—delivered seamlessly at no additional cost. Your investment compounds over time.
- Mobile-Friendly & Platform Agnostic: Seamlessly switch between desktop, tablet, and smartphone. Study during commutes, client waits, or quiet mornings—your learning adapts to your life, not the other way around.
- Expert-Led Guidance & Direct Support: Receive responsive, personalized assistance throughout your journey from certified ISO 27001 architects with proven implementation success across AI-integrated environments. You’re never alone—just one inquiry away from expert clarity.
- Official Certificate of Completion Issued by The Art of Service: Upon finishing the course, you will earn a globally recognized Certificate of Completion, digitally verifiable and highly respected across industries. The Art of Service is synonymous with elite information security training, trusted by professionals in over 156 countries. This credential visibly strengthens your resume, LinkedIn profile, and internal credibility—delivering measurable career ROI.
Every element of this course is designed to remove friction, eliminate guesswork, and maximize your confidence. Whether you’re building an AI governance framework, leading an audit, or preparing for certification, this program ensures you move forward with authority, precision, and proven methodology.
Extensive & Detailed Course Curriculum
Module 1: Foundations of Information Security in the Age of AI - Understanding the evolving threat landscape for AI-driven enterprises
- The critical importance of information security governance in machine learning environments
- Defining sensitive data in AI systems: training sets, models, and inference outputs
- Mapping autonomous decision-making risks to information security principles
- Why legacy security models fail with AI workloads
- The role of data confidentiality, integrity, and availability (CIA triad) in AI pipelines
- Regulatory drivers behind securing AI: GDPR, NIST, AI Act, and ISO/IEC 42001 alignment
- Security-by-design principles for AI development lifecycle
- Identifying key stakeholders in AI security governance
- Establishing executive accountability for AI data protection
Module 2: Introduction to ISO/IEC 27001 and Its Strategic Relevance - Origins and evolution of the ISO/IEC 27001 standard
- Core purpose and value proposition of ISO/IEC 27001 certification
- Differentiating between ISO/IEC 27001 and other compliance frameworks (e.g., SOC 2, NIST CSF)
- How ISO/IEC 27001 supports enterprise risk management (ERM) in AI contexts
- The business advantages of certification: trust, tenders, and market access
- Understanding the Plan-Do-Check-Act (PDCA) cycle in practice
- Mapping ISO/IEC 27001 to organizational strategy and AI governance
- The role of top management in driving ISMS adoption
- Defining the scope of an ISMS for AI research, development, and deployment units
- Aligning ISO/IEC 27001 with existing AI ethics and responsible AI policies
Module 3: Building the Information Security Management System (ISMS) - Step-by-step process for establishing an ISMS tailored to AI operations
- Precise definition of the ISMS scope: delineating AI model zones, data silos, and third-party integrations
- Developing a comprehensive information security policy suite
- Creating roles and responsibilities within the AI-ISMS governance team
- Establishing information classification models for AI-generated content
- Documenting asset inventories: from GPU clusters to training datasets
- Linking AI infrastructure components to security control ownership
- Designing secure development environments for ML model training
- Implementing secure configuration baselines for AI compute instances
- Developing risk-aware procurement policies for AI tools and APIs
Module 4: AI-Specific Risk Assessment and Treatment - Adapting ISO 27005 risk methodology for AI data ecosystems
- Identifying threat actors targeting AI systems (e.g., data poisoning, model inversion)
- Enumerating AI-specific vulnerabilities: overfitting, drift, backdoors
- Conducting asset-based risk assessments across training, validation, and inference phases
- Assessing risks from third-party AI platforms and open-source models
- Quantifying impact: reputational damage, regulatory penalties, operational disruption
- Calculating likelihood using threat intelligence and historical breach data
- Developing AI risk scenarios: adversarial attacks, prompt injection, bias exploitation
- Selecting risk treatment options: avoidance, mitigation, transfer, acceptance
- Creating AI risk treatment plans with ownership, timelines, and KPIs
- Documenting risk decisions for auditor-ready artefacts
- Establishing risk acceptance criteria for red-team exercises and experimentation zones
- Designing real-time risk dashboards for AI security posture monitoring
- Integrating risk treatment into CI/CD pipelines for ML workflows
- Evaluating residual risk in production AI models
Module 5: Legal, Regulatory, and Contractual Considerations for AI Systems - Mapping ISO 27001 controls to AI-related regulatory obligations
- Ensuring compliance with privacy laws (GDPR, CCPA) in model training data
- Addressing intellectual property rights in trained AI models and datasets
- Managing cross-border data transfers in distributed AI training
- Drafting enforceable data processing agreements for AI vendors
- Complying with sector-specific regulations (e.g., HIPAA for AI in healthcare)
- Handling data subject rights requests in AI systems (right to explanation, erasure)
- Establishing legal defensibility for AI decision-making processes
- Documenting consent and lawful basis for processing personal data in AI
- Meeting accountability requirements through audit trails and logs
- Handling breach notification timelines in automated AI environments
- Aligning AI security practices with corporate governance and fiduciary duties
- Navigating indemnification clauses in AI-as-a-Service contracts
- Incorporating regulatory changes into ongoing ISMS reviews
- Preparing for inspections by data protection authorities
Module 6: Annex A Controls Deep Dive – Part 1: Organizational & People - A.5.1: Policies for information security – tailoring for AI teams
- A.5.2: Segregation of duties in AI development and operations
- A.6.1: Mobile device and remote working policies for AI researchers
- A.6.2: Home working security controls for distributed ML engineers
- A.7.1: Onboarding security training specific to AI data handling
- A.7.2: Role-based access for AI team members (data scientists, MLOps, etc.)
- A.7.3: Security awareness programs focused on AI-specific threats
- A.7.4: Offboarding procedures for access revocation to model repositories
- A.8.1: Classification of AI assets: data, models, APIs, pipelines
- A.8.2: Labelling requirements for training data sensitivity levels
- A.8.3: Handling procedures for confidential model weights and configurations
- A.8.4: Retention policies for model debug logs and intermediary outputs
- A.8.5: Secure disposal of outdated or deprecated AI models
- A.9.1: Access control policy architecture for AI environments
- A.9.2: User access provisioning and de-provisioning for ML platforms
- A.9.3: Managing administrative privileges for model deployment tools
- A.9.4: Secret and API key lifecycle management for AI integrations
- A.9.5: Review of user access rights at scale in containerized AI systems
Module 7: Annex A Controls Deep Dive – Part 2: Physical & Technical - A.10.1: Secure development lifecycle for AI solutions
- A.10.2: Protection of test data in AI experimentation environments
- A.11.1: Physical security of on-premise AI training hardware
- A.11.2: Securing AI computing clusters in data centers
- A.12.1: Event logging standards for AI pipeline activities
- A.12.2: Monitoring access to model inference endpoints
- A.12.3: Vulnerability management for AI runtime environments
- A.12.4: Patch management for deep learning frameworks (TensorFlow, PyTorch)
- A.12.5: Malware protection in model training containers
- A.12.6: Technical controls for detecting AI model theft attempts
- A.13.1: Network security controls for AI API gateways
- A.13.2: Segregation of AI development, staging, and production environments
- A.13.3: Encryption of model weights in transit and at rest
- A.14.1: Secure coding practices for AI service interfaces
- A.14.2: Configuration standards for cloud ML instances (AWS SageMaker, GCP Vertex)
- A.14.3: Protection of API endpoints from adversarial inputs
- A.15.1: Information security in supplier relationships (AI SaaS providers)
- A.15.2: Third-party assurance for pre-trained model vendors
Module 8: Secure AI System Development Lifecycle (SDLC) - Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
Module 1: Foundations of Information Security in the Age of AI - Understanding the evolving threat landscape for AI-driven enterprises
- The critical importance of information security governance in machine learning environments
- Defining sensitive data in AI systems: training sets, models, and inference outputs
- Mapping autonomous decision-making risks to information security principles
- Why legacy security models fail with AI workloads
- The role of data confidentiality, integrity, and availability (CIA triad) in AI pipelines
- Regulatory drivers behind securing AI: GDPR, NIST, AI Act, and ISO/IEC 42001 alignment
- Security-by-design principles for AI development lifecycle
- Identifying key stakeholders in AI security governance
- Establishing executive accountability for AI data protection
Module 2: Introduction to ISO/IEC 27001 and Its Strategic Relevance - Origins and evolution of the ISO/IEC 27001 standard
- Core purpose and value proposition of ISO/IEC 27001 certification
- Differentiating between ISO/IEC 27001 and other compliance frameworks (e.g., SOC 2, NIST CSF)
- How ISO/IEC 27001 supports enterprise risk management (ERM) in AI contexts
- The business advantages of certification: trust, tenders, and market access
- Understanding the Plan-Do-Check-Act (PDCA) cycle in practice
- Mapping ISO/IEC 27001 to organizational strategy and AI governance
- The role of top management in driving ISMS adoption
- Defining the scope of an ISMS for AI research, development, and deployment units
- Aligning ISO/IEC 27001 with existing AI ethics and responsible AI policies
Module 3: Building the Information Security Management System (ISMS) - Step-by-step process for establishing an ISMS tailored to AI operations
- Precise definition of the ISMS scope: delineating AI model zones, data silos, and third-party integrations
- Developing a comprehensive information security policy suite
- Creating roles and responsibilities within the AI-ISMS governance team
- Establishing information classification models for AI-generated content
- Documenting asset inventories: from GPU clusters to training datasets
- Linking AI infrastructure components to security control ownership
- Designing secure development environments for ML model training
- Implementing secure configuration baselines for AI compute instances
- Developing risk-aware procurement policies for AI tools and APIs
Module 4: AI-Specific Risk Assessment and Treatment - Adapting ISO 27005 risk methodology for AI data ecosystems
- Identifying threat actors targeting AI systems (e.g., data poisoning, model inversion)
- Enumerating AI-specific vulnerabilities: overfitting, drift, backdoors
- Conducting asset-based risk assessments across training, validation, and inference phases
- Assessing risks from third-party AI platforms and open-source models
- Quantifying impact: reputational damage, regulatory penalties, operational disruption
- Calculating likelihood using threat intelligence and historical breach data
- Developing AI risk scenarios: adversarial attacks, prompt injection, bias exploitation
- Selecting risk treatment options: avoidance, mitigation, transfer, acceptance
- Creating AI risk treatment plans with ownership, timelines, and KPIs
- Documenting risk decisions for auditor-ready artefacts
- Establishing risk acceptance criteria for red-team exercises and experimentation zones
- Designing real-time risk dashboards for AI security posture monitoring
- Integrating risk treatment into CI/CD pipelines for ML workflows
- Evaluating residual risk in production AI models
Module 5: Legal, Regulatory, and Contractual Considerations for AI Systems - Mapping ISO 27001 controls to AI-related regulatory obligations
- Ensuring compliance with privacy laws (GDPR, CCPA) in model training data
- Addressing intellectual property rights in trained AI models and datasets
- Managing cross-border data transfers in distributed AI training
- Drafting enforceable data processing agreements for AI vendors
- Complying with sector-specific regulations (e.g., HIPAA for AI in healthcare)
- Handling data subject rights requests in AI systems (right to explanation, erasure)
- Establishing legal defensibility for AI decision-making processes
- Documenting consent and lawful basis for processing personal data in AI
- Meeting accountability requirements through audit trails and logs
- Handling breach notification timelines in automated AI environments
- Aligning AI security practices with corporate governance and fiduciary duties
- Navigating indemnification clauses in AI-as-a-Service contracts
- Incorporating regulatory changes into ongoing ISMS reviews
- Preparing for inspections by data protection authorities
Module 6: Annex A Controls Deep Dive – Part 1: Organizational & People - A.5.1: Policies for information security – tailoring for AI teams
- A.5.2: Segregation of duties in AI development and operations
- A.6.1: Mobile device and remote working policies for AI researchers
- A.6.2: Home working security controls for distributed ML engineers
- A.7.1: Onboarding security training specific to AI data handling
- A.7.2: Role-based access for AI team members (data scientists, MLOps, etc.)
- A.7.3: Security awareness programs focused on AI-specific threats
- A.7.4: Offboarding procedures for access revocation to model repositories
- A.8.1: Classification of AI assets: data, models, APIs, pipelines
- A.8.2: Labelling requirements for training data sensitivity levels
- A.8.3: Handling procedures for confidential model weights and configurations
- A.8.4: Retention policies for model debug logs and intermediary outputs
- A.8.5: Secure disposal of outdated or deprecated AI models
- A.9.1: Access control policy architecture for AI environments
- A.9.2: User access provisioning and de-provisioning for ML platforms
- A.9.3: Managing administrative privileges for model deployment tools
- A.9.4: Secret and API key lifecycle management for AI integrations
- A.9.5: Review of user access rights at scale in containerized AI systems
Module 7: Annex A Controls Deep Dive – Part 2: Physical & Technical - A.10.1: Secure development lifecycle for AI solutions
- A.10.2: Protection of test data in AI experimentation environments
- A.11.1: Physical security of on-premise AI training hardware
- A.11.2: Securing AI computing clusters in data centers
- A.12.1: Event logging standards for AI pipeline activities
- A.12.2: Monitoring access to model inference endpoints
- A.12.3: Vulnerability management for AI runtime environments
- A.12.4: Patch management for deep learning frameworks (TensorFlow, PyTorch)
- A.12.5: Malware protection in model training containers
- A.12.6: Technical controls for detecting AI model theft attempts
- A.13.1: Network security controls for AI API gateways
- A.13.2: Segregation of AI development, staging, and production environments
- A.13.3: Encryption of model weights in transit and at rest
- A.14.1: Secure coding practices for AI service interfaces
- A.14.2: Configuration standards for cloud ML instances (AWS SageMaker, GCP Vertex)
- A.14.3: Protection of API endpoints from adversarial inputs
- A.15.1: Information security in supplier relationships (AI SaaS providers)
- A.15.2: Third-party assurance for pre-trained model vendors
Module 8: Secure AI System Development Lifecycle (SDLC) - Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Origins and evolution of the ISO/IEC 27001 standard
- Core purpose and value proposition of ISO/IEC 27001 certification
- Differentiating between ISO/IEC 27001 and other compliance frameworks (e.g., SOC 2, NIST CSF)
- How ISO/IEC 27001 supports enterprise risk management (ERM) in AI contexts
- The business advantages of certification: trust, tenders, and market access
- Understanding the Plan-Do-Check-Act (PDCA) cycle in practice
- Mapping ISO/IEC 27001 to organizational strategy and AI governance
- The role of top management in driving ISMS adoption
- Defining the scope of an ISMS for AI research, development, and deployment units
- Aligning ISO/IEC 27001 with existing AI ethics and responsible AI policies
Module 3: Building the Information Security Management System (ISMS) - Step-by-step process for establishing an ISMS tailored to AI operations
- Precise definition of the ISMS scope: delineating AI model zones, data silos, and third-party integrations
- Developing a comprehensive information security policy suite
- Creating roles and responsibilities within the AI-ISMS governance team
- Establishing information classification models for AI-generated content
- Documenting asset inventories: from GPU clusters to training datasets
- Linking AI infrastructure components to security control ownership
- Designing secure development environments for ML model training
- Implementing secure configuration baselines for AI compute instances
- Developing risk-aware procurement policies for AI tools and APIs
Module 4: AI-Specific Risk Assessment and Treatment - Adapting ISO 27005 risk methodology for AI data ecosystems
- Identifying threat actors targeting AI systems (e.g., data poisoning, model inversion)
- Enumerating AI-specific vulnerabilities: overfitting, drift, backdoors
- Conducting asset-based risk assessments across training, validation, and inference phases
- Assessing risks from third-party AI platforms and open-source models
- Quantifying impact: reputational damage, regulatory penalties, operational disruption
- Calculating likelihood using threat intelligence and historical breach data
- Developing AI risk scenarios: adversarial attacks, prompt injection, bias exploitation
- Selecting risk treatment options: avoidance, mitigation, transfer, acceptance
- Creating AI risk treatment plans with ownership, timelines, and KPIs
- Documenting risk decisions for auditor-ready artefacts
- Establishing risk acceptance criteria for red-team exercises and experimentation zones
- Designing real-time risk dashboards for AI security posture monitoring
- Integrating risk treatment into CI/CD pipelines for ML workflows
- Evaluating residual risk in production AI models
Module 5: Legal, Regulatory, and Contractual Considerations for AI Systems - Mapping ISO 27001 controls to AI-related regulatory obligations
- Ensuring compliance with privacy laws (GDPR, CCPA) in model training data
- Addressing intellectual property rights in trained AI models and datasets
- Managing cross-border data transfers in distributed AI training
- Drafting enforceable data processing agreements for AI vendors
- Complying with sector-specific regulations (e.g., HIPAA for AI in healthcare)
- Handling data subject rights requests in AI systems (right to explanation, erasure)
- Establishing legal defensibility for AI decision-making processes
- Documenting consent and lawful basis for processing personal data in AI
- Meeting accountability requirements through audit trails and logs
- Handling breach notification timelines in automated AI environments
- Aligning AI security practices with corporate governance and fiduciary duties
- Navigating indemnification clauses in AI-as-a-Service contracts
- Incorporating regulatory changes into ongoing ISMS reviews
- Preparing for inspections by data protection authorities
Module 6: Annex A Controls Deep Dive – Part 1: Organizational & People - A.5.1: Policies for information security – tailoring for AI teams
- A.5.2: Segregation of duties in AI development and operations
- A.6.1: Mobile device and remote working policies for AI researchers
- A.6.2: Home working security controls for distributed ML engineers
- A.7.1: Onboarding security training specific to AI data handling
- A.7.2: Role-based access for AI team members (data scientists, MLOps, etc.)
- A.7.3: Security awareness programs focused on AI-specific threats
- A.7.4: Offboarding procedures for access revocation to model repositories
- A.8.1: Classification of AI assets: data, models, APIs, pipelines
- A.8.2: Labelling requirements for training data sensitivity levels
- A.8.3: Handling procedures for confidential model weights and configurations
- A.8.4: Retention policies for model debug logs and intermediary outputs
- A.8.5: Secure disposal of outdated or deprecated AI models
- A.9.1: Access control policy architecture for AI environments
- A.9.2: User access provisioning and de-provisioning for ML platforms
- A.9.3: Managing administrative privileges for model deployment tools
- A.9.4: Secret and API key lifecycle management for AI integrations
- A.9.5: Review of user access rights at scale in containerized AI systems
Module 7: Annex A Controls Deep Dive – Part 2: Physical & Technical - A.10.1: Secure development lifecycle for AI solutions
- A.10.2: Protection of test data in AI experimentation environments
- A.11.1: Physical security of on-premise AI training hardware
- A.11.2: Securing AI computing clusters in data centers
- A.12.1: Event logging standards for AI pipeline activities
- A.12.2: Monitoring access to model inference endpoints
- A.12.3: Vulnerability management for AI runtime environments
- A.12.4: Patch management for deep learning frameworks (TensorFlow, PyTorch)
- A.12.5: Malware protection in model training containers
- A.12.6: Technical controls for detecting AI model theft attempts
- A.13.1: Network security controls for AI API gateways
- A.13.2: Segregation of AI development, staging, and production environments
- A.13.3: Encryption of model weights in transit and at rest
- A.14.1: Secure coding practices for AI service interfaces
- A.14.2: Configuration standards for cloud ML instances (AWS SageMaker, GCP Vertex)
- A.14.3: Protection of API endpoints from adversarial inputs
- A.15.1: Information security in supplier relationships (AI SaaS providers)
- A.15.2: Third-party assurance for pre-trained model vendors
Module 8: Secure AI System Development Lifecycle (SDLC) - Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Adapting ISO 27005 risk methodology for AI data ecosystems
- Identifying threat actors targeting AI systems (e.g., data poisoning, model inversion)
- Enumerating AI-specific vulnerabilities: overfitting, drift, backdoors
- Conducting asset-based risk assessments across training, validation, and inference phases
- Assessing risks from third-party AI platforms and open-source models
- Quantifying impact: reputational damage, regulatory penalties, operational disruption
- Calculating likelihood using threat intelligence and historical breach data
- Developing AI risk scenarios: adversarial attacks, prompt injection, bias exploitation
- Selecting risk treatment options: avoidance, mitigation, transfer, acceptance
- Creating AI risk treatment plans with ownership, timelines, and KPIs
- Documenting risk decisions for auditor-ready artefacts
- Establishing risk acceptance criteria for red-team exercises and experimentation zones
- Designing real-time risk dashboards for AI security posture monitoring
- Integrating risk treatment into CI/CD pipelines for ML workflows
- Evaluating residual risk in production AI models
Module 5: Legal, Regulatory, and Contractual Considerations for AI Systems - Mapping ISO 27001 controls to AI-related regulatory obligations
- Ensuring compliance with privacy laws (GDPR, CCPA) in model training data
- Addressing intellectual property rights in trained AI models and datasets
- Managing cross-border data transfers in distributed AI training
- Drafting enforceable data processing agreements for AI vendors
- Complying with sector-specific regulations (e.g., HIPAA for AI in healthcare)
- Handling data subject rights requests in AI systems (right to explanation, erasure)
- Establishing legal defensibility for AI decision-making processes
- Documenting consent and lawful basis for processing personal data in AI
- Meeting accountability requirements through audit trails and logs
- Handling breach notification timelines in automated AI environments
- Aligning AI security practices with corporate governance and fiduciary duties
- Navigating indemnification clauses in AI-as-a-Service contracts
- Incorporating regulatory changes into ongoing ISMS reviews
- Preparing for inspections by data protection authorities
Module 6: Annex A Controls Deep Dive – Part 1: Organizational & People - A.5.1: Policies for information security – tailoring for AI teams
- A.5.2: Segregation of duties in AI development and operations
- A.6.1: Mobile device and remote working policies for AI researchers
- A.6.2: Home working security controls for distributed ML engineers
- A.7.1: Onboarding security training specific to AI data handling
- A.7.2: Role-based access for AI team members (data scientists, MLOps, etc.)
- A.7.3: Security awareness programs focused on AI-specific threats
- A.7.4: Offboarding procedures for access revocation to model repositories
- A.8.1: Classification of AI assets: data, models, APIs, pipelines
- A.8.2: Labelling requirements for training data sensitivity levels
- A.8.3: Handling procedures for confidential model weights and configurations
- A.8.4: Retention policies for model debug logs and intermediary outputs
- A.8.5: Secure disposal of outdated or deprecated AI models
- A.9.1: Access control policy architecture for AI environments
- A.9.2: User access provisioning and de-provisioning for ML platforms
- A.9.3: Managing administrative privileges for model deployment tools
- A.9.4: Secret and API key lifecycle management for AI integrations
- A.9.5: Review of user access rights at scale in containerized AI systems
Module 7: Annex A Controls Deep Dive – Part 2: Physical & Technical - A.10.1: Secure development lifecycle for AI solutions
- A.10.2: Protection of test data in AI experimentation environments
- A.11.1: Physical security of on-premise AI training hardware
- A.11.2: Securing AI computing clusters in data centers
- A.12.1: Event logging standards for AI pipeline activities
- A.12.2: Monitoring access to model inference endpoints
- A.12.3: Vulnerability management for AI runtime environments
- A.12.4: Patch management for deep learning frameworks (TensorFlow, PyTorch)
- A.12.5: Malware protection in model training containers
- A.12.6: Technical controls for detecting AI model theft attempts
- A.13.1: Network security controls for AI API gateways
- A.13.2: Segregation of AI development, staging, and production environments
- A.13.3: Encryption of model weights in transit and at rest
- A.14.1: Secure coding practices for AI service interfaces
- A.14.2: Configuration standards for cloud ML instances (AWS SageMaker, GCP Vertex)
- A.14.3: Protection of API endpoints from adversarial inputs
- A.15.1: Information security in supplier relationships (AI SaaS providers)
- A.15.2: Third-party assurance for pre-trained model vendors
Module 8: Secure AI System Development Lifecycle (SDLC) - Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- A.5.1: Policies for information security – tailoring for AI teams
- A.5.2: Segregation of duties in AI development and operations
- A.6.1: Mobile device and remote working policies for AI researchers
- A.6.2: Home working security controls for distributed ML engineers
- A.7.1: Onboarding security training specific to AI data handling
- A.7.2: Role-based access for AI team members (data scientists, MLOps, etc.)
- A.7.3: Security awareness programs focused on AI-specific threats
- A.7.4: Offboarding procedures for access revocation to model repositories
- A.8.1: Classification of AI assets: data, models, APIs, pipelines
- A.8.2: Labelling requirements for training data sensitivity levels
- A.8.3: Handling procedures for confidential model weights and configurations
- A.8.4: Retention policies for model debug logs and intermediary outputs
- A.8.5: Secure disposal of outdated or deprecated AI models
- A.9.1: Access control policy architecture for AI environments
- A.9.2: User access provisioning and de-provisioning for ML platforms
- A.9.3: Managing administrative privileges for model deployment tools
- A.9.4: Secret and API key lifecycle management for AI integrations
- A.9.5: Review of user access rights at scale in containerized AI systems
Module 7: Annex A Controls Deep Dive – Part 2: Physical & Technical - A.10.1: Secure development lifecycle for AI solutions
- A.10.2: Protection of test data in AI experimentation environments
- A.11.1: Physical security of on-premise AI training hardware
- A.11.2: Securing AI computing clusters in data centers
- A.12.1: Event logging standards for AI pipeline activities
- A.12.2: Monitoring access to model inference endpoints
- A.12.3: Vulnerability management for AI runtime environments
- A.12.4: Patch management for deep learning frameworks (TensorFlow, PyTorch)
- A.12.5: Malware protection in model training containers
- A.12.6: Technical controls for detecting AI model theft attempts
- A.13.1: Network security controls for AI API gateways
- A.13.2: Segregation of AI development, staging, and production environments
- A.13.3: Encryption of model weights in transit and at rest
- A.14.1: Secure coding practices for AI service interfaces
- A.14.2: Configuration standards for cloud ML instances (AWS SageMaker, GCP Vertex)
- A.14.3: Protection of API endpoints from adversarial inputs
- A.15.1: Information security in supplier relationships (AI SaaS providers)
- A.15.2: Third-party assurance for pre-trained model vendors
Module 8: Secure AI System Development Lifecycle (SDLC) - Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Integrating ISO 27001 controls into MLOps pipelines
- Embedding security requirements in AI project initiation
- Threat modelling for AI architectures (STRIDE applied to ML)
- Secure data sourcing and anonymization techniques for training sets
- Vetting upstream data providers for security and provenance
- Implementing model explainability as a security control
- Secure version control for AI models and datasets (using Git LFS, DVC)
- Static and dynamic code analysis for ML scripts and notebooks
- Hardening container images for ML inference services
- Automated security gates in CI/CD for model deployment
- Configuration drift detection in AI infrastructure as code
- Secure documentation standards for model cards and datasheets
- Penetration testing scope definition for AI applications
- Red teaming techniques specific to machine learning systems
- Validating model robustness against evasion and poisoning attacks
Module 9: Monitoring, Review, and Continuous Improvement - Designing ISMS monitoring mechanisms for AI workloads
- Establishing key security indicators (KSIs) for AI operations
- Tracking metrics: unauthorized access attempts, model drift events, inference anomalies
- Conducting internal audits of AI-ISMS compliance
- Audit planning: sampling AI projects and data flows
- Developing checklists for AI-specific audit evidence collection
- Preparing for external certification audits in AI enterprises
- Conducting management reviews with AI performance and risk data
- Reporting ISMS performance to executive leadership and boards
- Driving continual improvement through AI incident retrospectives
- Updating risk assessments with new AI capabilities and use cases
- Revising policies based on emerging AI threats and controls
- Measuring the ROI of ISO 27001 implementation in AI contexts
- Scaling the ISMS across multiple AI product lines
- Documenting improvement actions and closure verification
Module 10: Incident Management and AI-Specific Breach Response - Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Developing an AI-specific incident response plan
- Identifying indicators of compromise in AI systems
- Responding to dataset poisoning and model contamination
- Handling unauthorized model extraction attempts
- Containment procedures for compromised inference APIs
- Forensic investigation of ML pipeline tampering
- Preserving logs and artefacts for AI incident analysis
- Notifying regulators of AI-related data breaches
- Communicating incidents to stakeholders without revealing IP
- Recovery strategies for retraining and redeploying models
- Post-incident reviews focused on AI system resilience
- Updating ISMS based on incident learnings
- Preparing tabletop exercises for AI attack scenarios
- Building organizational muscle memory for AI security events
- Integrating AI incident response with enterprise SOC operations
Module 11: Integrating ISO 27001 with AI Governance Frameworks - Mapping ISO 27001 controls to NIST AI Risk Management Framework
- Aligning with OECD AI Principles for trustworthy systems
- Integrating with ISO/IEC 42001: AI Management System standard
- Linking information security to AI model risk assessment processes
- Using ISO 27001 to support AI impact assessments (AIIAs)
- Harmonizing with internal AI review boards and ethics committees
- Supporting responsible AI initiatives through documented controls
- Establishing cross-functional ISMS-AI governance working groups
- Leveraging ISO 27001 for AI compliance reporting dashboards
- Creating unified policy frameworks across security and AI governance
- Ensuring consistency in control implementation across departments
- Developing audit trails that satisfy both security and AI ethics requirements
- Preparing for combined audits (security, privacy, AI compliance)
- Using ISO 27001 as a foundation for AI assurance certifications
- Scaling governance across AI product portfolios
Module 12: Certification Preparation and Audit Readiness - Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Understanding the two-stage certification audit process
- Selecting an accredited certification body with AI experience
- Preparing documentation for Stage 1 readiness review
- Conducting a pre-audit gap assessment for AI environments
- Aligning AI-specific controls with auditor expectations
- Responding to nonconformities in AI-related clauses
- Training internal teams for audit interviews and walkthroughs
- Demonstrating control effectiveness through AI operational evidence
- Presenting risk treatment plans involving AI assets
- Handling auditor inquiries about autonomous decision-making risks
- Ensuring completeness of policy, procedure, and record documentation
- Creating audit-ready digital folders with instant access
- Simulating full certification audit with AI use case scenarios
- Negotiating scope and exclusions involving experimental AI zones
- Maintaining certification through surveillance audits
Module 13: Real-World Implementation Projects - Project 1: Conduct an AI asset inventory for a fintech recommendation engine
- Project 2: Perform a full risk assessment on an autonomous customer service bot
- Project 3: Develop ISO-compliant security policies for an AI research lab
- Project 4: Design access control matrix for a medical imaging AI system
- Project 5: Create incident response playbooks for model sabotage events
- Project 6: Map controls to a large language model (LLM) deployment pipeline
- Project 7: Conduct internal audit of an AI-powered HR screening tool
- Project 8: Develop management review presentation for board-level AI security reporting
- Project 9: Build monitoring dashboard for real-time AI ISMS KPIs
- Project 10: Prepare full documentation suite for external certification audit
- Project 11: Draft contractual clauses for secure AI model transfer agreements
- Project 12: Implement secure deployment pipeline for computer vision models
- Project 13: Design data classification schema for multimodal AI training sets
- Project 14: Execute tabletop exercise for AI supply chain compromise
- Project 15: Develop continual improvement plan based on AI breach scenario
Module 14: Career Advancement and Certification Leverage - Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations
- Positioning your ISO 27001 Implementation Mastery in your resume and CV
- Using the Official Certificate of Completion to stand out in job applications
- Leveraging your expertise in salary negotiations and promotions
- Transitioning from technical roles to governance and leadership positions
- Networking with other certified professionals through The Art of Service community
- Adding verifiable certification badges to LinkedIn and professional profiles
- Preparing for advanced roles: AI Security Officer, ISMS Manager, CISO
- Using project work as portfolio evidence for consulting engagements
- Becoming a recognized internal expert in AI-secure transformation
- Leading ISO 27001 implementations across departments or subsidiaries
- Gaining recognition as a cross-functional leader in digital transformation
- Delivering measurable ROI through risk reduction and compliance assurance
- Building personal brand as an AI governance authority
- Advancing into advisory and board-level discussions on AI risk
- Establishing yourself as indispensable in high-growth AI organizations