Mastering ISO 27005 Risk Assessment for AI-Driven Organizations
Course Format & Delivery Details Self-Paced, On-Demand Access – Learn Anytime, Anywhere, at Your Own Speed
This course is designed for professionals who demand flexibility without compromise. From the moment you enroll, you gain immediate online access to a powerful, self-paced learning environment that adapts to your schedule, not the other way around. There are no fixed dates, no mandatory live sessions, and no time constraints. Whether you're fitting learning around client work, governance meetings, or global time zones, this course moves at your pace. Designed for Real-World Results – Fast Completion, Lasting Impact
Most learners complete the course in 4 to 6 weeks with consistent engagement, while high-performing professionals report applying core risk assessment techniques within just 7 days of starting. The structured flow ensures you do not waste time on theory alone. From the first module, you are building practical, actionable skills directly mapped to ISO 27005 and the evolving threat landscape of AI systems. This is not abstract knowledge. This is certified, career-advancing execution. Lifetime Access – Learn Now, Revisit Forever, Stay Ahead Forever
Once enrolled, you receive permanent, 24/7 access to all course materials, with ongoing future updates included at no extra cost. As ISO standards evolve, AI risks shift, and regulations tighten, your access evolves with them. This is not a temporary resource. It is a lifelong reference library, continuously refined by our expert curriculum team and embedded with real-time alignment to AI governance frameworks. Mobile-Friendly, Globally Accessible Learning
Access your course from any device – desktop, tablet, or smartphone – anywhere in the world. The system is fully responsive, secure, and optimized for performance. Whether you're reviewing risk treatment options on a morning commute or preparing a compliance brief in a meeting queue, your progress syncs seamlessly across devices. Progress tracking and session memory ensure you never lose momentum. Direct Instructor Guidance & Support – Clarity When You Need It
You are not learning in isolation. Our dedicated team of ISO 27005-certified risk practitioners provides direct, written feedback and guidance throughout your journey. Access expert insights, ask targeted questions, and receive structured responses that deepen your understanding and accelerate implementation. This is not generic forum support. This is personalized mentorship from professionals who have led risk assessments in AI-integrated enterprises across finance, healthcare, and technology sectors. Certificate of Completion – Recognized Expertise You Can Leverage
Upon successful completion, you receive a Certificate of Completion issued by The Art of Service, a globally recognized authority in professional certification and enterprise risk education. This certificate is verifiable, industry-respected, and designed to enhance your C.V., LinkedIn profile, and internal credibility. It signals to employers, auditors, and leadership teams that you possess advanced, standardized competence in managing information security risk within AI environments using ISO 27005. Transparent Pricing – No Hidden Fees, No Surprises
The investment for this course is straightforward, with all costs clearly stated at enrollment. There are no hidden fees, recurring charges, or unexpected add-ons. What you see is what you get – full access, full support, full certification, forever. Accepted payment methods include Visa, Mastercard, and PayPal. Secure transactions are processed through industry-standard encryption, ensuring your financial data remains protected at all times. 100% Money-Back Guarantee – Eliminate All Risk
We are so confident in the value, clarity, and real-world application of this course that we offer a full money-back guarantee. If at any point within 30 days you determine the course does not meet your expectations, simply contact support for a complete refund – no questions asked, no delays, no friction. Your only risk is not taking action. We remove even that. What to Expect After Enrollment
After enrollment, you will receive an automated confirmation email acknowledging your registration. Your access credentials and detailed course navigation instructions will be sent separately once your learner profile is fully configured and your materials are prepared. This ensures accuracy and a seamless onboarding experience. Will This Work for Me? We’ve Designed It to Work – No Matter Where You’re Starting
It doesn’t matter if you’re new to ISO 27005 or have years of experience in information security. This course works even if you’ve never conducted a formal risk assessment in an AI context before. It works even if your organization is still defining what AI governance means. It works even if you're the only one pushing for structured risk methodology in your team. Our curriculum is built on role-specific learning paths. Whether you're a Chief Information Security Officer needing to audit AI system integrity, a Compliance Lead validating risk treatment plans, a Data Protection Officer safeguarding AI training data, or a Risk Analyst implementing ISO 27005 in practice, this course provides you with the exact tools, templates, and decision frameworks your role demands. - A Lead Auditor at a multinational fintech company used Module 5 to redesign risk criteria across 12 AI models, reducing false positive alerts by 68%.
- A Security Consultant in Dubai completed the course in 5 weeks and won a six-figure contract auditing an AI-driven healthcare platform using the assessment templates provided.
- An IT Manager in Singapore applied the risk communication strategies from Module 10 and secured board approval for a critical AI risk mitigation budget within days.
This course works because it is not theoretical. It is a proven system, used by professionals in regulated industries worldwide. Our graduates report increased confidence, faster decision-making, and immediate recognition from leadership and auditors. The content is field-tested, expert-reviewed, and built for immediate ROI. You are not purchasing information. You are investing in a professional transformation with measurable outcomes. And if for any reason it doesn’t deliver, our money-back guarantee protects your investment completely. There is no downside – only growth, clarity, and career momentum waiting on the other side.
Extensive and Detailed Course Curriculum
Module 1: Foundations of Information Security Risk in AI Environments - Understanding the evolving threat landscape for AI systems
- Key differences between traditional IT risk and AI-driven risk
- The role of data integrity, model bias, and adversarial attacks
- How AI dependencies expand the attack surface
- Overview of ISO 27000 family standards and their interrelationships
- Placement and purpose of ISO 27005 within the ISMS framework
- Linking ISO 27005 to ISO 27001 and ISO 27002
- Core concepts of risk assessment, risk treatment, and risk acceptance
- Defining risk in terms of AI context, assets, threats, and vulnerabilities
- Differentiating between risk identification, analysis, and evaluation
- Understanding the AI development lifecycle and risk injection points
- Role of data quality and completeness in AI risk outcomes
- Regulatory drivers influencing AI risk practices (GDPR, NIST, EU AI Act)
- Integrating compliance requirements into risk assessment scope
- Fundamental terminology as defined in ISO/IEC 27000 and ISO 27005
- Establishing the foundation for risk ownership and accountability
- Recognizing common misconceptions about AI risk and security
- Introduction to risk treatment plans in AI contexts
- Setting the strategic context for AI risk governance
- Building stakeholder alignment across technical and business teams
Module 2: ISO 27005 Principles and Risk Management Frameworks - Historical development and purpose of ISO 27005
- Structure and clause-by-clause breakdown of ISO 27005
- Understanding risk management policy alignment
- Establishing a risk management framework (RMF)
- Components of a robust risk management framework
- Defining roles and responsibilities in the RMF
- How to integrate the RMF with organizational governance
- Selecting risk assessment methods based on AI project complexity
- Understanding risk appetite and tolerance thresholds
- Setting risk criteria tailored to AI system deployment
- Linking risk criteria to business objectives and AI outcomes
- Documentation requirements for full compliance
- Establishing measurement scales for likelihood and impact
- Quantitative vs qualitative risk assessment approaches
- Semi-quantitative methods for AI-specific risk scoring
- Calibrating risk scales to reflect AI model lifecycle stages
- Using risk registers to track and report AI-related issues
- Integrating third-party risk data into assessment criteria
- Audit readiness and evidence collection for risk decisions
- Ensuring framework consistency across multiple AI use cases
Module 3: Risk Identification in AI Systems - Systematic asset identification in AI environments
- Classifying assets: data, models, APIs, infrastructure, algorithms
- Mapping data flows across AI training, tuning, and inference phases
- Identifying threats specific to machine learning models
- Threat modeling techniques for AI pipelines (STRIDE, LINDDUN)
- Recognizing insider threats in model development teams
- Assessing adversarial machine learning risks
- Evaluating data poisoning and model inversion attacks
- Identifying vulnerabilities in open-source AI libraries
- Exposure points in model deployment and API exposure
- Automated risk discovery tools and their human oversight
- Using checklists to ensure comprehensive risk identification
- Structured workshops for cross-functional risk discovery
- Involving AI engineers in risk identification sessions
- Documenting assumptions and scope boundaries
- Identifying interdependencies between AI components
- Risk of third-party AI model integration (LLMs, APIs)
- Assessing supply chain risks in pre-trained models
- Human factors in AI risk: overreliance, automation bias
- Risk of model drift and concept drift over time
Module 4: Risk Analysis Techniques for AI Contexts - Selecting appropriate risk analysis methods: qualitative, quantitative, hybrid
- Applying risk matrices to AI scenarios with calibrated scales
- Failure Mode and Effects Analysis (FMEA) for AI pipelines
- Threat trees and attack path modeling for AI infrastructure
- Scenario-based risk assessment for AI decision systems
- Using expert judgment to evaluate AI risk likelihood
- Incorporating real-world incident data into analysis
- Estimating impact on confidentiality, integrity, availability
- Measuring impact beyond data loss: reputational, legal, operational
- Assessing cascading failures in AI-integrated systems
- Dependency analysis between models and data sources
- Conducting sensitivity analysis for model parameters
- Understanding uncertainty in probabilistic risk estimates
- Weighting multiple risk factors in complex AI environments
- Mapping results to business impact categories
- Avoiding common analysis pitfalls: overconfidence, confirmation bias
- Using peer reviews to strengthen risk analysis quality
- Integrating red teaming perspectives into risk analysis
- Aligning analysis depth with risk significance
- Determining when analysis is sufficient for decision-making
Module 5: Risk Evaluation and Prioritization - Establishing risk acceptance criteria for AI projects
- Determining risk levels using predefined evaluation thresholds
- Prioritizing risks based on business-criticality of AI applications
- Distinguishing between high, medium, and low priority risks
- Using multi-criteria decision analysis for risk ranking
- Creating risk evaluation reports for executive audiences
- Presenting risk findings with clarity and actionable insights
- Handling residual risks after treatment planning
- Defining what constitutes acceptable risk in AI systems
- Documenting justification for risk acceptance
- Managing stakeholder disagreement on risk prioritization
- Aligning risk decisions with organizational risk appetite
- Evaluating trade-offs between innovation speed and security
- Using heat maps to visualize AI risk concentration
- Incorporating ethical impact into risk evaluation
- Assessing fairness, transparency, and accountability in risk terms
- Linking risk levels to incident response preparedness
- Determining escalation paths for critical AI risks
- Setting thresholds for mandatory mitigation actions
- Establishing review cycles for risk re-evaluation
Module 6: Risk Treatment Planning and Implementation - Overview of the four risk treatment options: avoid, transfer, mitigate, accept
- Selecting optimal treatment strategies for AI risks
- Differentiating between technical and procedural controls
- Mapping ISO 27001 controls to AI-specific risk scenarios
- Customizing controls for data preprocessing and model training
- Implementing access controls for model and data repositories
- Configuring monitoring systems for real-time anomaly detection
- Using explainability tools as risk treatment mechanisms
- Establishing model versioning and rollback capabilities
- Setting up automated drift detection and retraining triggers
- Third-party risk transfer through contractual obligations
- Insurance considerations for AI system failures
- Developing risk treatment plans with assigned owners and timelines
- Integrating treatment plans into AI project roadmaps
- Creating budgets and resource allocation for mitigation activities
- Using Gantt charts and milestone tracking for accountability
- Ensuring risk treatment aligns with model lifecycle phases
- Verifying control effectiveness through testing and audits
- Documenting evidence of control implementation
- Building feedback loops for continuous treatment improvement
Module 7: Monitoring, Review, and Continuous Improvement - Defining key risk indicators (KRIs) for AI systems
- Setting up dashboards for real-time AI risk visibility
- Frequency and triggers for risk reassessment
- Conducting periodic risk review meetings
- Incorporating audit findings into risk monitoring
- Using incident logs to refine risk models
- Tracking changes in AI system architecture and dependencies
- Updating risk assessments after model retraining
- Handling changes in data sources and data quality
- Reviewing third-party service changes affecting AI risk
- Updating threat libraries with emerging attack patterns
- Integrating feedback from security operations teams
- Conducting post-incident risk reassessments
- Ensuring risk assessment remains proportional to system changes
- Automating assessment updates where appropriate
- Documenting review outcomes and decisions
- Storing historical risk data for trend analysis
- Linking monitoring to internal and external reporting
- Ensuring independence and objectivity in review processes
- Aligning reviews with internal audit and compliance schedules
Module 8: Risk Communication and Stakeholder Engagement - Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
Module 1: Foundations of Information Security Risk in AI Environments - Understanding the evolving threat landscape for AI systems
- Key differences between traditional IT risk and AI-driven risk
- The role of data integrity, model bias, and adversarial attacks
- How AI dependencies expand the attack surface
- Overview of ISO 27000 family standards and their interrelationships
- Placement and purpose of ISO 27005 within the ISMS framework
- Linking ISO 27005 to ISO 27001 and ISO 27002
- Core concepts of risk assessment, risk treatment, and risk acceptance
- Defining risk in terms of AI context, assets, threats, and vulnerabilities
- Differentiating between risk identification, analysis, and evaluation
- Understanding the AI development lifecycle and risk injection points
- Role of data quality and completeness in AI risk outcomes
- Regulatory drivers influencing AI risk practices (GDPR, NIST, EU AI Act)
- Integrating compliance requirements into risk assessment scope
- Fundamental terminology as defined in ISO/IEC 27000 and ISO 27005
- Establishing the foundation for risk ownership and accountability
- Recognizing common misconceptions about AI risk and security
- Introduction to risk treatment plans in AI contexts
- Setting the strategic context for AI risk governance
- Building stakeholder alignment across technical and business teams
Module 2: ISO 27005 Principles and Risk Management Frameworks - Historical development and purpose of ISO 27005
- Structure and clause-by-clause breakdown of ISO 27005
- Understanding risk management policy alignment
- Establishing a risk management framework (RMF)
- Components of a robust risk management framework
- Defining roles and responsibilities in the RMF
- How to integrate the RMF with organizational governance
- Selecting risk assessment methods based on AI project complexity
- Understanding risk appetite and tolerance thresholds
- Setting risk criteria tailored to AI system deployment
- Linking risk criteria to business objectives and AI outcomes
- Documentation requirements for full compliance
- Establishing measurement scales for likelihood and impact
- Quantitative vs qualitative risk assessment approaches
- Semi-quantitative methods for AI-specific risk scoring
- Calibrating risk scales to reflect AI model lifecycle stages
- Using risk registers to track and report AI-related issues
- Integrating third-party risk data into assessment criteria
- Audit readiness and evidence collection for risk decisions
- Ensuring framework consistency across multiple AI use cases
Module 3: Risk Identification in AI Systems - Systematic asset identification in AI environments
- Classifying assets: data, models, APIs, infrastructure, algorithms
- Mapping data flows across AI training, tuning, and inference phases
- Identifying threats specific to machine learning models
- Threat modeling techniques for AI pipelines (STRIDE, LINDDUN)
- Recognizing insider threats in model development teams
- Assessing adversarial machine learning risks
- Evaluating data poisoning and model inversion attacks
- Identifying vulnerabilities in open-source AI libraries
- Exposure points in model deployment and API exposure
- Automated risk discovery tools and their human oversight
- Using checklists to ensure comprehensive risk identification
- Structured workshops for cross-functional risk discovery
- Involving AI engineers in risk identification sessions
- Documenting assumptions and scope boundaries
- Identifying interdependencies between AI components
- Risk of third-party AI model integration (LLMs, APIs)
- Assessing supply chain risks in pre-trained models
- Human factors in AI risk: overreliance, automation bias
- Risk of model drift and concept drift over time
Module 4: Risk Analysis Techniques for AI Contexts - Selecting appropriate risk analysis methods: qualitative, quantitative, hybrid
- Applying risk matrices to AI scenarios with calibrated scales
- Failure Mode and Effects Analysis (FMEA) for AI pipelines
- Threat trees and attack path modeling for AI infrastructure
- Scenario-based risk assessment for AI decision systems
- Using expert judgment to evaluate AI risk likelihood
- Incorporating real-world incident data into analysis
- Estimating impact on confidentiality, integrity, availability
- Measuring impact beyond data loss: reputational, legal, operational
- Assessing cascading failures in AI-integrated systems
- Dependency analysis between models and data sources
- Conducting sensitivity analysis for model parameters
- Understanding uncertainty in probabilistic risk estimates
- Weighting multiple risk factors in complex AI environments
- Mapping results to business impact categories
- Avoiding common analysis pitfalls: overconfidence, confirmation bias
- Using peer reviews to strengthen risk analysis quality
- Integrating red teaming perspectives into risk analysis
- Aligning analysis depth with risk significance
- Determining when analysis is sufficient for decision-making
Module 5: Risk Evaluation and Prioritization - Establishing risk acceptance criteria for AI projects
- Determining risk levels using predefined evaluation thresholds
- Prioritizing risks based on business-criticality of AI applications
- Distinguishing between high, medium, and low priority risks
- Using multi-criteria decision analysis for risk ranking
- Creating risk evaluation reports for executive audiences
- Presenting risk findings with clarity and actionable insights
- Handling residual risks after treatment planning
- Defining what constitutes acceptable risk in AI systems
- Documenting justification for risk acceptance
- Managing stakeholder disagreement on risk prioritization
- Aligning risk decisions with organizational risk appetite
- Evaluating trade-offs between innovation speed and security
- Using heat maps to visualize AI risk concentration
- Incorporating ethical impact into risk evaluation
- Assessing fairness, transparency, and accountability in risk terms
- Linking risk levels to incident response preparedness
- Determining escalation paths for critical AI risks
- Setting thresholds for mandatory mitigation actions
- Establishing review cycles for risk re-evaluation
Module 6: Risk Treatment Planning and Implementation - Overview of the four risk treatment options: avoid, transfer, mitigate, accept
- Selecting optimal treatment strategies for AI risks
- Differentiating between technical and procedural controls
- Mapping ISO 27001 controls to AI-specific risk scenarios
- Customizing controls for data preprocessing and model training
- Implementing access controls for model and data repositories
- Configuring monitoring systems for real-time anomaly detection
- Using explainability tools as risk treatment mechanisms
- Establishing model versioning and rollback capabilities
- Setting up automated drift detection and retraining triggers
- Third-party risk transfer through contractual obligations
- Insurance considerations for AI system failures
- Developing risk treatment plans with assigned owners and timelines
- Integrating treatment plans into AI project roadmaps
- Creating budgets and resource allocation for mitigation activities
- Using Gantt charts and milestone tracking for accountability
- Ensuring risk treatment aligns with model lifecycle phases
- Verifying control effectiveness through testing and audits
- Documenting evidence of control implementation
- Building feedback loops for continuous treatment improvement
Module 7: Monitoring, Review, and Continuous Improvement - Defining key risk indicators (KRIs) for AI systems
- Setting up dashboards for real-time AI risk visibility
- Frequency and triggers for risk reassessment
- Conducting periodic risk review meetings
- Incorporating audit findings into risk monitoring
- Using incident logs to refine risk models
- Tracking changes in AI system architecture and dependencies
- Updating risk assessments after model retraining
- Handling changes in data sources and data quality
- Reviewing third-party service changes affecting AI risk
- Updating threat libraries with emerging attack patterns
- Integrating feedback from security operations teams
- Conducting post-incident risk reassessments
- Ensuring risk assessment remains proportional to system changes
- Automating assessment updates where appropriate
- Documenting review outcomes and decisions
- Storing historical risk data for trend analysis
- Linking monitoring to internal and external reporting
- Ensuring independence and objectivity in review processes
- Aligning reviews with internal audit and compliance schedules
Module 8: Risk Communication and Stakeholder Engagement - Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
- Historical development and purpose of ISO 27005
- Structure and clause-by-clause breakdown of ISO 27005
- Understanding risk management policy alignment
- Establishing a risk management framework (RMF)
- Components of a robust risk management framework
- Defining roles and responsibilities in the RMF
- How to integrate the RMF with organizational governance
- Selecting risk assessment methods based on AI project complexity
- Understanding risk appetite and tolerance thresholds
- Setting risk criteria tailored to AI system deployment
- Linking risk criteria to business objectives and AI outcomes
- Documentation requirements for full compliance
- Establishing measurement scales for likelihood and impact
- Quantitative vs qualitative risk assessment approaches
- Semi-quantitative methods for AI-specific risk scoring
- Calibrating risk scales to reflect AI model lifecycle stages
- Using risk registers to track and report AI-related issues
- Integrating third-party risk data into assessment criteria
- Audit readiness and evidence collection for risk decisions
- Ensuring framework consistency across multiple AI use cases
Module 3: Risk Identification in AI Systems - Systematic asset identification in AI environments
- Classifying assets: data, models, APIs, infrastructure, algorithms
- Mapping data flows across AI training, tuning, and inference phases
- Identifying threats specific to machine learning models
- Threat modeling techniques for AI pipelines (STRIDE, LINDDUN)
- Recognizing insider threats in model development teams
- Assessing adversarial machine learning risks
- Evaluating data poisoning and model inversion attacks
- Identifying vulnerabilities in open-source AI libraries
- Exposure points in model deployment and API exposure
- Automated risk discovery tools and their human oversight
- Using checklists to ensure comprehensive risk identification
- Structured workshops for cross-functional risk discovery
- Involving AI engineers in risk identification sessions
- Documenting assumptions and scope boundaries
- Identifying interdependencies between AI components
- Risk of third-party AI model integration (LLMs, APIs)
- Assessing supply chain risks in pre-trained models
- Human factors in AI risk: overreliance, automation bias
- Risk of model drift and concept drift over time
Module 4: Risk Analysis Techniques for AI Contexts - Selecting appropriate risk analysis methods: qualitative, quantitative, hybrid
- Applying risk matrices to AI scenarios with calibrated scales
- Failure Mode and Effects Analysis (FMEA) for AI pipelines
- Threat trees and attack path modeling for AI infrastructure
- Scenario-based risk assessment for AI decision systems
- Using expert judgment to evaluate AI risk likelihood
- Incorporating real-world incident data into analysis
- Estimating impact on confidentiality, integrity, availability
- Measuring impact beyond data loss: reputational, legal, operational
- Assessing cascading failures in AI-integrated systems
- Dependency analysis between models and data sources
- Conducting sensitivity analysis for model parameters
- Understanding uncertainty in probabilistic risk estimates
- Weighting multiple risk factors in complex AI environments
- Mapping results to business impact categories
- Avoiding common analysis pitfalls: overconfidence, confirmation bias
- Using peer reviews to strengthen risk analysis quality
- Integrating red teaming perspectives into risk analysis
- Aligning analysis depth with risk significance
- Determining when analysis is sufficient for decision-making
Module 5: Risk Evaluation and Prioritization - Establishing risk acceptance criteria for AI projects
- Determining risk levels using predefined evaluation thresholds
- Prioritizing risks based on business-criticality of AI applications
- Distinguishing between high, medium, and low priority risks
- Using multi-criteria decision analysis for risk ranking
- Creating risk evaluation reports for executive audiences
- Presenting risk findings with clarity and actionable insights
- Handling residual risks after treatment planning
- Defining what constitutes acceptable risk in AI systems
- Documenting justification for risk acceptance
- Managing stakeholder disagreement on risk prioritization
- Aligning risk decisions with organizational risk appetite
- Evaluating trade-offs between innovation speed and security
- Using heat maps to visualize AI risk concentration
- Incorporating ethical impact into risk evaluation
- Assessing fairness, transparency, and accountability in risk terms
- Linking risk levels to incident response preparedness
- Determining escalation paths for critical AI risks
- Setting thresholds for mandatory mitigation actions
- Establishing review cycles for risk re-evaluation
Module 6: Risk Treatment Planning and Implementation - Overview of the four risk treatment options: avoid, transfer, mitigate, accept
- Selecting optimal treatment strategies for AI risks
- Differentiating between technical and procedural controls
- Mapping ISO 27001 controls to AI-specific risk scenarios
- Customizing controls for data preprocessing and model training
- Implementing access controls for model and data repositories
- Configuring monitoring systems for real-time anomaly detection
- Using explainability tools as risk treatment mechanisms
- Establishing model versioning and rollback capabilities
- Setting up automated drift detection and retraining triggers
- Third-party risk transfer through contractual obligations
- Insurance considerations for AI system failures
- Developing risk treatment plans with assigned owners and timelines
- Integrating treatment plans into AI project roadmaps
- Creating budgets and resource allocation for mitigation activities
- Using Gantt charts and milestone tracking for accountability
- Ensuring risk treatment aligns with model lifecycle phases
- Verifying control effectiveness through testing and audits
- Documenting evidence of control implementation
- Building feedback loops for continuous treatment improvement
Module 7: Monitoring, Review, and Continuous Improvement - Defining key risk indicators (KRIs) for AI systems
- Setting up dashboards for real-time AI risk visibility
- Frequency and triggers for risk reassessment
- Conducting periodic risk review meetings
- Incorporating audit findings into risk monitoring
- Using incident logs to refine risk models
- Tracking changes in AI system architecture and dependencies
- Updating risk assessments after model retraining
- Handling changes in data sources and data quality
- Reviewing third-party service changes affecting AI risk
- Updating threat libraries with emerging attack patterns
- Integrating feedback from security operations teams
- Conducting post-incident risk reassessments
- Ensuring risk assessment remains proportional to system changes
- Automating assessment updates where appropriate
- Documenting review outcomes and decisions
- Storing historical risk data for trend analysis
- Linking monitoring to internal and external reporting
- Ensuring independence and objectivity in review processes
- Aligning reviews with internal audit and compliance schedules
Module 8: Risk Communication and Stakeholder Engagement - Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
- Selecting appropriate risk analysis methods: qualitative, quantitative, hybrid
- Applying risk matrices to AI scenarios with calibrated scales
- Failure Mode and Effects Analysis (FMEA) for AI pipelines
- Threat trees and attack path modeling for AI infrastructure
- Scenario-based risk assessment for AI decision systems
- Using expert judgment to evaluate AI risk likelihood
- Incorporating real-world incident data into analysis
- Estimating impact on confidentiality, integrity, availability
- Measuring impact beyond data loss: reputational, legal, operational
- Assessing cascading failures in AI-integrated systems
- Dependency analysis between models and data sources
- Conducting sensitivity analysis for model parameters
- Understanding uncertainty in probabilistic risk estimates
- Weighting multiple risk factors in complex AI environments
- Mapping results to business impact categories
- Avoiding common analysis pitfalls: overconfidence, confirmation bias
- Using peer reviews to strengthen risk analysis quality
- Integrating red teaming perspectives into risk analysis
- Aligning analysis depth with risk significance
- Determining when analysis is sufficient for decision-making
Module 5: Risk Evaluation and Prioritization - Establishing risk acceptance criteria for AI projects
- Determining risk levels using predefined evaluation thresholds
- Prioritizing risks based on business-criticality of AI applications
- Distinguishing between high, medium, and low priority risks
- Using multi-criteria decision analysis for risk ranking
- Creating risk evaluation reports for executive audiences
- Presenting risk findings with clarity and actionable insights
- Handling residual risks after treatment planning
- Defining what constitutes acceptable risk in AI systems
- Documenting justification for risk acceptance
- Managing stakeholder disagreement on risk prioritization
- Aligning risk decisions with organizational risk appetite
- Evaluating trade-offs between innovation speed and security
- Using heat maps to visualize AI risk concentration
- Incorporating ethical impact into risk evaluation
- Assessing fairness, transparency, and accountability in risk terms
- Linking risk levels to incident response preparedness
- Determining escalation paths for critical AI risks
- Setting thresholds for mandatory mitigation actions
- Establishing review cycles for risk re-evaluation
Module 6: Risk Treatment Planning and Implementation - Overview of the four risk treatment options: avoid, transfer, mitigate, accept
- Selecting optimal treatment strategies for AI risks
- Differentiating between technical and procedural controls
- Mapping ISO 27001 controls to AI-specific risk scenarios
- Customizing controls for data preprocessing and model training
- Implementing access controls for model and data repositories
- Configuring monitoring systems for real-time anomaly detection
- Using explainability tools as risk treatment mechanisms
- Establishing model versioning and rollback capabilities
- Setting up automated drift detection and retraining triggers
- Third-party risk transfer through contractual obligations
- Insurance considerations for AI system failures
- Developing risk treatment plans with assigned owners and timelines
- Integrating treatment plans into AI project roadmaps
- Creating budgets and resource allocation for mitigation activities
- Using Gantt charts and milestone tracking for accountability
- Ensuring risk treatment aligns with model lifecycle phases
- Verifying control effectiveness through testing and audits
- Documenting evidence of control implementation
- Building feedback loops for continuous treatment improvement
Module 7: Monitoring, Review, and Continuous Improvement - Defining key risk indicators (KRIs) for AI systems
- Setting up dashboards for real-time AI risk visibility
- Frequency and triggers for risk reassessment
- Conducting periodic risk review meetings
- Incorporating audit findings into risk monitoring
- Using incident logs to refine risk models
- Tracking changes in AI system architecture and dependencies
- Updating risk assessments after model retraining
- Handling changes in data sources and data quality
- Reviewing third-party service changes affecting AI risk
- Updating threat libraries with emerging attack patterns
- Integrating feedback from security operations teams
- Conducting post-incident risk reassessments
- Ensuring risk assessment remains proportional to system changes
- Automating assessment updates where appropriate
- Documenting review outcomes and decisions
- Storing historical risk data for trend analysis
- Linking monitoring to internal and external reporting
- Ensuring independence and objectivity in review processes
- Aligning reviews with internal audit and compliance schedules
Module 8: Risk Communication and Stakeholder Engagement - Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
- Overview of the four risk treatment options: avoid, transfer, mitigate, accept
- Selecting optimal treatment strategies for AI risks
- Differentiating between technical and procedural controls
- Mapping ISO 27001 controls to AI-specific risk scenarios
- Customizing controls for data preprocessing and model training
- Implementing access controls for model and data repositories
- Configuring monitoring systems for real-time anomaly detection
- Using explainability tools as risk treatment mechanisms
- Establishing model versioning and rollback capabilities
- Setting up automated drift detection and retraining triggers
- Third-party risk transfer through contractual obligations
- Insurance considerations for AI system failures
- Developing risk treatment plans with assigned owners and timelines
- Integrating treatment plans into AI project roadmaps
- Creating budgets and resource allocation for mitigation activities
- Using Gantt charts and milestone tracking for accountability
- Ensuring risk treatment aligns with model lifecycle phases
- Verifying control effectiveness through testing and audits
- Documenting evidence of control implementation
- Building feedback loops for continuous treatment improvement
Module 7: Monitoring, Review, and Continuous Improvement - Defining key risk indicators (KRIs) for AI systems
- Setting up dashboards for real-time AI risk visibility
- Frequency and triggers for risk reassessment
- Conducting periodic risk review meetings
- Incorporating audit findings into risk monitoring
- Using incident logs to refine risk models
- Tracking changes in AI system architecture and dependencies
- Updating risk assessments after model retraining
- Handling changes in data sources and data quality
- Reviewing third-party service changes affecting AI risk
- Updating threat libraries with emerging attack patterns
- Integrating feedback from security operations teams
- Conducting post-incident risk reassessments
- Ensuring risk assessment remains proportional to system changes
- Automating assessment updates where appropriate
- Documenting review outcomes and decisions
- Storing historical risk data for trend analysis
- Linking monitoring to internal and external reporting
- Ensuring independence and objectivity in review processes
- Aligning reviews with internal audit and compliance schedules
Module 8: Risk Communication and Stakeholder Engagement - Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
- Identifying risk communication audiences and objectives
- Tailoring risk messages for technical, executive, and board levels
- Creating risk summaries that drive informed decisions
- Using visual aids to explain complex AI risk concepts
- Incorporating risk communication into change management
- Establishing regular risk reporting cadence
- Preparing risk briefings for board and audit committees
- Handling communication during AI-related incidents
- Training staff on risk awareness and reporting procedures
- Developing FAQs for common AI risk concerns
- Ensuring transparency without exposing sensitive data
- Managing external communication with regulators and partners
- Building trust through consistent, accurate reporting
- Using storytelling techniques to convey risk impact
- Engaging legal and compliance teams in communication planning
- Documenting all communication for audit purposes
- Establishing feedback mechanisms for stakeholders
- Using surveys to measure risk communication effectiveness
- Aligning messaging across departments and regions
- Creating risk communication templates and playbooks
Module 9: Integrating ISO 27005 with AI Governance Frameworks - Mapping ISO 27005 to NIST AI Risk Management Framework
- Aligning with the EU AI Act's risk classification system
- Integrating with OECD AI Principles
- Using ISO 38507 for governance of AI in ICT
- Linking risk assessment to model cards and data sheets
- Supporting Responsible AI initiatives through structured risk
- Connecting risk outcomes to AI ethics review boards
- Ensuring fairness and bias assessments are risk-informed
- Incorporating human oversight requirements into risk evaluations
- Assessing risks of autonomous decision-making systems
- Integrating with software development lifecycle (SDLC) practices
- Embedding risk checks into MLOps pipelines
- Using CI/CD gates for risk compliance validation
- Linking to DevSecOps principles for AI systems
- Coordinating with data governance and privacy teams
- Ensuring risk assessments align with DPIA requirements
- Integrating with enterprise risk management (ERM) systems
- Feeding AI risk data into business continuity planning
- Supporting cyber resilience strategies with risk insights
- Building a unified governance model across security, privacy, and AI
Module 10: Practical Application and Real-World Implementation - Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation
- Conducting a full ISO 27005 risk assessment for a case study AI system
- Defining scope for a recommendation engine in e-commerce
- Identifying assets in a medical diagnosis AI model
- Threat modeling a facial recognition deployment
- Applying risk analysis to a fraud detection algorithm
- Evaluating risks in a generative AI content system
- Developing treatment plans for a credit scoring model
- Creating a risk register with prioritized AI risks
- Designing monitoring procedures for a real-time AI API
- Producing an executive risk summary for board presentation
- Simulating a risk review meeting with cross-functional roles
- Responding to audit questions on risk documentation
- Revising risk assessments after a model update
- Handling third-party model integration risks
- Managing legacy system dependencies in AI projects
- Applying lessons from real-world AI security breaches
- Using templates to standardize future assessments
- Conducting peer reviews of risk deliverables
- Validating control effectiveness in test environments
- Building institutional knowledge through documentation