COURSE FORMAT & DELIVERY DETAILS Self-Paced, On-Demand Access with Lifetime Updates
Enroll in Mastering ISO 27005: Risk Management for AI-Driven Organizations and gain immediate entry into a meticulously structured, expert-led learning environment designed to deliver career-transforming results. This is not just another theoretical course-it is a comprehensive, action-oriented roadmap developed specifically for professionals who lead or support information security, risk governance, and AI system integrity in modern enterprises. Learn Anytime, Anywhere-No Deadlines, No Pressure
The course is fully self-paced and delivered on-demand, allowing you to progress through the material according to your schedule. There are no fixed dates, no mandatory attendance, and no time constraints. Whether you’re balancing a full-time role, managing complex projects, or located across time zones, you maintain complete control over your learning journey. Fast Completion, Immediate Impact
Most learners complete the course within 28 to 35 hours, with many reporting tangible improvements in their risk identification, assessment frameworks, and compliance documentation within the first week. You’ll begin applying high-impact strategies immediately-aligning AI initiatives with ISO 27005 best practices, building defensible risk registers, and refining existing information security management systems (ISMS). Lifetime Access-Including All Future Updates at No Extra Cost
Your enrollment includes permanent access to all course content, even as it evolves. As regulatory guidance shifts and AI technologies advance, we update the course materials proactively. You will always have access to the most current, industry-validated interpretations of ISO 27005, ensuring your knowledge remains ahead of the curve-without ever paying for renewals or upgrades. 24/7 Global Access, Fully Optimized for Mobile Devices
Access your course seamlessly from any device. Whether you’re reviewing risk treatment plans on a tablet during a commute or refining threat models from a mobile device before a board meeting, the platform is fully responsive, fast-loading, and engineered for real-world professional use. You are never locked out by location or device limitations. Expert Guidance and Direct Instructor Support
You are not learning in isolation. Throughout the course, you will have access to structured instructor support. Our lead facilitators-certified risk architects with extensive experience implementing ISO 27005 in AI-centric environments-provide actionable feedback, clarify nuanced standards interpretations, and guide you through complex scenarios such as third-party AI vendor risk and algorithmic bias exposure. Questions are answered promptly, with responses tailored to your organizational context. Receive a Globally Recognized Certificate of Completion
Upon successful completion, you will be issued an official Certificate of Completion by The Art of Service. This credential is trusted by professionals in over 140 countries and recognized by employers, auditors, and compliance officers worldwide. The certificate verifies your mastery of ISO 27005 principles in the context of artificial intelligence, enabling you to demonstrate competence, enhance your resume, and stand out in competitive markets. Transparent, Upfront Pricing-No Hidden Fees
The course fee includes everything: full content access, support, updates, downloadable tools, templates, and the final certificate. There are no hidden charges, no subscription traps, and no surprise costs. What you see is exactly what you get-complete value, clear pricing, and total honesty. Secure Payment Options: Visa, Mastercard, PayPal
We accept all major payment methods including Visa, Mastercard, and PayPal. Transactions are processed securely through certified gateways, ensuring your financial information remains protected at every stage. 100% Money-Back Guarantee-Satisfied or Refunded
Your success is our priority. If the course does not meet your expectations, you are protected by our ironclad money-back guarantee. Request a refund within 30 days of enrollment, no questions asked, and receive every dollar back. There is zero financial risk in starting today. What to Expect After Enrollment
After you enroll, you will receive a confirmation email verifying your registration. Once your course materials are prepared and activated, a separate email will be sent with full access instructions. This process ensures system stability and content readiness for every learner. You will not experience delays or access issues-only a smooth, professional onboarding experience. Will This Work for Me? Real Results Across Roles
Whether you are an information security officer, AI governance lead, IT risk manager, compliance consultant, data protection officer, or technology executive, this course is engineered to deliver value. Our alumni include professionals from financial services, healthcare, government agencies, and global tech firms who now lead ISO 27005-aligned risk management in AI deployments. - A senior risk analyst at a multinational bank used the threat modeling templates to reduce AI model deployment risks by 62% within three months.
- A CISO in a fast-growing AI startup leveraged the course's risk register framework to pass a critical audit with zero non-conformities.
- A data governance consultant in Australia doubled her consulting fees after adding ISO 27005 certification to her portfolio.
This Works Even If…
You’ve never implemented a formal risk methodology, your organization lacks a mature ISMS, or you’re new to AI governance. The course is built for practical application, step-by-step, with clear examples, annotated templates, and scenario-based exercises that bridge knowledge gaps instantly. You’ll learn how to start from where you are and build toward compliance excellence-no prior mastery required. Zero Risk, Maximum Reward
With lifetime access, ongoing updates, expert support, a globally recognized certificate, and a full refund guarantee, the only risk is not taking action. This is your opportunity to gain a rare, high-value skill: the ability to confidently manage risk in one of the most complex technological landscapes of our time. Enroll now and turn uncertainty into advantage.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of ISO 27005 and AI Risk Context - Introduction to ISO 27005 and its role in information security risk management
- Understanding the relationship between ISO 27001, ISO 27002, and ISO 27005
- Overview of AI-driven systems and their unique risk profiles
- Why traditional risk frameworks fall short in AI environments
- Core principles of risk management in machine learning and AI applications
- Regulatory drivers influencing AI risk governance
- Key challenges in securing AI data pipelines and model outputs
- Defining the scope of AI risk management programs
- Differences between deterministic and probabilistic systems in risk assessment
- Establishing the business case for ISO 27005 adoption in AI initiatives
- Mapping AI use cases to risk exposure levels
- Integrating data ethics into AI risk frameworks
- Identifying high-risk AI applications requiring formal risk treatment
- Role of organizational culture in shaping risk tolerance for AI
- Engaging executive leadership in AI risk oversight
Module 2: ISO 27005 Framework Structure and Core Components - Detailed breakdown of ISO 27005:2018 clause-by-clause
- Understanding the risk management process lifecycle
- Defining risk policy and risk management framework documentation
- Establishing roles and responsibilities for AI risk governance
- Developing a risk management mandate aligned with AI strategy
- Creating asset inventories specific to AI systems
- Classifying data types in training, validation, and inference phases
- Linking AI components to information security objectives
- Mapping AI models as critical information assets
- Setting risk criteria and acceptance thresholds for AI deployments
- Designing risk assessment methodologies for dynamic AI environments
- Documenting assumptions and constraints in AI risk analysis
- Developing a risk communication strategy for technical and non-technical stakeholders
- Ensuring traceability in AI risk decisions
- Integrating risk assessment outcomes into governance reporting
Module 3: AI-Specific Threat Identification and Vulnerability Mapping - Common threat actors targeting AI systems
- Attack vectors in model training and deployment phases
- Model inversion and training data extraction attacks
- Adversarial attacks and input manipulation techniques
- Model stealing and IP theft in AI environments
- Understanding concept drift and its risk implications
- Identifying vulnerabilities in third-party AI APIs and libraries
- Detecting backdoor injections in pre-trained models
- Assessing supply chain risks in AI development tools
- Misuse risks: dual-use concerns in generative AI
- Model bias as a systemic security threat
- Data poisoning and label flipping attacks
- Privacy leakage through model outputs
- Insider threats in AI engineering teams
- Cloud infrastructure vulnerabilities in AI hosting environments
- Weaknesses in model monitoring and logging systems
Module 4: Risk Assessment Methodologies for AI Systems - Choosing between qualitative and quantitative risk assessment approaches
- Applying the OCTAVE Allegro method to AI risk scenarios
- Using NIST SP 800-30 in AI risk contexts
- Adapting ISO 31000 principles for machine learning governance
- Developing AI-specific risk scales and impact matrices
- Measuring likelihood in probabilistic AI behaviors
- Assessing business impact of model failure or manipulation
- Linking risk severity to compliance and reputational damage
- Scenario-based risk modeling for autonomous decision systems
- Threat modeling using STRIDE and PASTA for AI
- Attack tree analysis for generative AI systems
- Developing risk scenarios for real-time inference systems
- Assessing cascading effects of AI failures across business units
- Using expert judgment to estimate AI risk when data is scarce
- Creating dynamic risk registers that update with model performance
Module 5: AI Asset Valuation and Criticality Analysis - Classifying AI assets: models, datasets, APIs, logs, and infrastructure
- Assigning business value to trained machine learning models
- Valuing sensitive training datasets and synthetic data outputs
- Assessing model interpretability as a risk control factor
- Determining the criticality of real-time inference systems
- Mapping AI dependencies in business continuity plans
- Evaluating the cost of model retraining and recovery
- Calculating financial exposure from AI decision errors
- Assessing customer harm from biased or erroneous AI outcomes
- Measuring regulatory penalties for non-compliant AI behavior
- Linking AI asset value to business KPIs and SLAs
- Using data lineage to trace high-value information flows
- Impact analysis of model drift on business operations
- Valuing intellectual property in proprietary AI frameworks
- Analyzing reputational damage from AI hallucinations or failures
Module 6: Risk Estimation and Prioritization in Dynamic AI Environments - Estimating risk exposure in continuously learning models
- Handling uncertainty in AI risk probability assessments
- Differentiating between static and adaptive risk profiles
- Prioritizing risks based on mitigation feasibility and impact
- Using risk heat maps to visualize AI threat landscapes
- Incorporating model confidence scores into risk calculations
- Linking anomaly detection alerts to risk levels
- Prioritizing risks involving public-facing AI services
- Addressing time-sensitive risks in real-time decision systems
- Adjusting risk rankings as models retrain and redeploy
- Using risk scoring to justify AI security investments
- Integrating ethical risk into overall severity ratings
- Managing risks in multi-tenant AI platforms
- Handling unanticipated AI behaviors in production
- Monitoring risk prioritization effectiveness over time
Module 7: Risk Treatment Strategies for AI Systems - Four risk treatment options: avoid, transfer, mitigate, accept
- Avoidance tactics for high-risk AI applications
- Risk transfer through insurance and contractual agreements
- Mitigation controls tailored to AI system vulnerabilities
- Acceptance criteria for residual AI risk
- Developing risk treatment plans with clear accountability
- Using model ensembles to reduce single-point failures
- Implementing input sanitization and adversarial defense layers
- Applying differential privacy to protect training data
- Encrypting models and data in use, at rest, and in transit
- Designing human-in-the-loop oversight for critical AI decisions
- Implementing model explainability as a risk control
- Using sandboxing and isolation for untrusted AI components
- Establishing fallback mechanisms for failed AI systems
- Validating third-party models before deployment
Module 8: Control Selection and Implementation Based on ISO 27002 - Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
Module 1: Foundations of ISO 27005 and AI Risk Context - Introduction to ISO 27005 and its role in information security risk management
- Understanding the relationship between ISO 27001, ISO 27002, and ISO 27005
- Overview of AI-driven systems and their unique risk profiles
- Why traditional risk frameworks fall short in AI environments
- Core principles of risk management in machine learning and AI applications
- Regulatory drivers influencing AI risk governance
- Key challenges in securing AI data pipelines and model outputs
- Defining the scope of AI risk management programs
- Differences between deterministic and probabilistic systems in risk assessment
- Establishing the business case for ISO 27005 adoption in AI initiatives
- Mapping AI use cases to risk exposure levels
- Integrating data ethics into AI risk frameworks
- Identifying high-risk AI applications requiring formal risk treatment
- Role of organizational culture in shaping risk tolerance for AI
- Engaging executive leadership in AI risk oversight
Module 2: ISO 27005 Framework Structure and Core Components - Detailed breakdown of ISO 27005:2018 clause-by-clause
- Understanding the risk management process lifecycle
- Defining risk policy and risk management framework documentation
- Establishing roles and responsibilities for AI risk governance
- Developing a risk management mandate aligned with AI strategy
- Creating asset inventories specific to AI systems
- Classifying data types in training, validation, and inference phases
- Linking AI components to information security objectives
- Mapping AI models as critical information assets
- Setting risk criteria and acceptance thresholds for AI deployments
- Designing risk assessment methodologies for dynamic AI environments
- Documenting assumptions and constraints in AI risk analysis
- Developing a risk communication strategy for technical and non-technical stakeholders
- Ensuring traceability in AI risk decisions
- Integrating risk assessment outcomes into governance reporting
Module 3: AI-Specific Threat Identification and Vulnerability Mapping - Common threat actors targeting AI systems
- Attack vectors in model training and deployment phases
- Model inversion and training data extraction attacks
- Adversarial attacks and input manipulation techniques
- Model stealing and IP theft in AI environments
- Understanding concept drift and its risk implications
- Identifying vulnerabilities in third-party AI APIs and libraries
- Detecting backdoor injections in pre-trained models
- Assessing supply chain risks in AI development tools
- Misuse risks: dual-use concerns in generative AI
- Model bias as a systemic security threat
- Data poisoning and label flipping attacks
- Privacy leakage through model outputs
- Insider threats in AI engineering teams
- Cloud infrastructure vulnerabilities in AI hosting environments
- Weaknesses in model monitoring and logging systems
Module 4: Risk Assessment Methodologies for AI Systems - Choosing between qualitative and quantitative risk assessment approaches
- Applying the OCTAVE Allegro method to AI risk scenarios
- Using NIST SP 800-30 in AI risk contexts
- Adapting ISO 31000 principles for machine learning governance
- Developing AI-specific risk scales and impact matrices
- Measuring likelihood in probabilistic AI behaviors
- Assessing business impact of model failure or manipulation
- Linking risk severity to compliance and reputational damage
- Scenario-based risk modeling for autonomous decision systems
- Threat modeling using STRIDE and PASTA for AI
- Attack tree analysis for generative AI systems
- Developing risk scenarios for real-time inference systems
- Assessing cascading effects of AI failures across business units
- Using expert judgment to estimate AI risk when data is scarce
- Creating dynamic risk registers that update with model performance
Module 5: AI Asset Valuation and Criticality Analysis - Classifying AI assets: models, datasets, APIs, logs, and infrastructure
- Assigning business value to trained machine learning models
- Valuing sensitive training datasets and synthetic data outputs
- Assessing model interpretability as a risk control factor
- Determining the criticality of real-time inference systems
- Mapping AI dependencies in business continuity plans
- Evaluating the cost of model retraining and recovery
- Calculating financial exposure from AI decision errors
- Assessing customer harm from biased or erroneous AI outcomes
- Measuring regulatory penalties for non-compliant AI behavior
- Linking AI asset value to business KPIs and SLAs
- Using data lineage to trace high-value information flows
- Impact analysis of model drift on business operations
- Valuing intellectual property in proprietary AI frameworks
- Analyzing reputational damage from AI hallucinations or failures
Module 6: Risk Estimation and Prioritization in Dynamic AI Environments - Estimating risk exposure in continuously learning models
- Handling uncertainty in AI risk probability assessments
- Differentiating between static and adaptive risk profiles
- Prioritizing risks based on mitigation feasibility and impact
- Using risk heat maps to visualize AI threat landscapes
- Incorporating model confidence scores into risk calculations
- Linking anomaly detection alerts to risk levels
- Prioritizing risks involving public-facing AI services
- Addressing time-sensitive risks in real-time decision systems
- Adjusting risk rankings as models retrain and redeploy
- Using risk scoring to justify AI security investments
- Integrating ethical risk into overall severity ratings
- Managing risks in multi-tenant AI platforms
- Handling unanticipated AI behaviors in production
- Monitoring risk prioritization effectiveness over time
Module 7: Risk Treatment Strategies for AI Systems - Four risk treatment options: avoid, transfer, mitigate, accept
- Avoidance tactics for high-risk AI applications
- Risk transfer through insurance and contractual agreements
- Mitigation controls tailored to AI system vulnerabilities
- Acceptance criteria for residual AI risk
- Developing risk treatment plans with clear accountability
- Using model ensembles to reduce single-point failures
- Implementing input sanitization and adversarial defense layers
- Applying differential privacy to protect training data
- Encrypting models and data in use, at rest, and in transit
- Designing human-in-the-loop oversight for critical AI decisions
- Implementing model explainability as a risk control
- Using sandboxing and isolation for untrusted AI components
- Establishing fallback mechanisms for failed AI systems
- Validating third-party models before deployment
Module 8: Control Selection and Implementation Based on ISO 27002 - Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Detailed breakdown of ISO 27005:2018 clause-by-clause
- Understanding the risk management process lifecycle
- Defining risk policy and risk management framework documentation
- Establishing roles and responsibilities for AI risk governance
- Developing a risk management mandate aligned with AI strategy
- Creating asset inventories specific to AI systems
- Classifying data types in training, validation, and inference phases
- Linking AI components to information security objectives
- Mapping AI models as critical information assets
- Setting risk criteria and acceptance thresholds for AI deployments
- Designing risk assessment methodologies for dynamic AI environments
- Documenting assumptions and constraints in AI risk analysis
- Developing a risk communication strategy for technical and non-technical stakeholders
- Ensuring traceability in AI risk decisions
- Integrating risk assessment outcomes into governance reporting
Module 3: AI-Specific Threat Identification and Vulnerability Mapping - Common threat actors targeting AI systems
- Attack vectors in model training and deployment phases
- Model inversion and training data extraction attacks
- Adversarial attacks and input manipulation techniques
- Model stealing and IP theft in AI environments
- Understanding concept drift and its risk implications
- Identifying vulnerabilities in third-party AI APIs and libraries
- Detecting backdoor injections in pre-trained models
- Assessing supply chain risks in AI development tools
- Misuse risks: dual-use concerns in generative AI
- Model bias as a systemic security threat
- Data poisoning and label flipping attacks
- Privacy leakage through model outputs
- Insider threats in AI engineering teams
- Cloud infrastructure vulnerabilities in AI hosting environments
- Weaknesses in model monitoring and logging systems
Module 4: Risk Assessment Methodologies for AI Systems - Choosing between qualitative and quantitative risk assessment approaches
- Applying the OCTAVE Allegro method to AI risk scenarios
- Using NIST SP 800-30 in AI risk contexts
- Adapting ISO 31000 principles for machine learning governance
- Developing AI-specific risk scales and impact matrices
- Measuring likelihood in probabilistic AI behaviors
- Assessing business impact of model failure or manipulation
- Linking risk severity to compliance and reputational damage
- Scenario-based risk modeling for autonomous decision systems
- Threat modeling using STRIDE and PASTA for AI
- Attack tree analysis for generative AI systems
- Developing risk scenarios for real-time inference systems
- Assessing cascading effects of AI failures across business units
- Using expert judgment to estimate AI risk when data is scarce
- Creating dynamic risk registers that update with model performance
Module 5: AI Asset Valuation and Criticality Analysis - Classifying AI assets: models, datasets, APIs, logs, and infrastructure
- Assigning business value to trained machine learning models
- Valuing sensitive training datasets and synthetic data outputs
- Assessing model interpretability as a risk control factor
- Determining the criticality of real-time inference systems
- Mapping AI dependencies in business continuity plans
- Evaluating the cost of model retraining and recovery
- Calculating financial exposure from AI decision errors
- Assessing customer harm from biased or erroneous AI outcomes
- Measuring regulatory penalties for non-compliant AI behavior
- Linking AI asset value to business KPIs and SLAs
- Using data lineage to trace high-value information flows
- Impact analysis of model drift on business operations
- Valuing intellectual property in proprietary AI frameworks
- Analyzing reputational damage from AI hallucinations or failures
Module 6: Risk Estimation and Prioritization in Dynamic AI Environments - Estimating risk exposure in continuously learning models
- Handling uncertainty in AI risk probability assessments
- Differentiating between static and adaptive risk profiles
- Prioritizing risks based on mitigation feasibility and impact
- Using risk heat maps to visualize AI threat landscapes
- Incorporating model confidence scores into risk calculations
- Linking anomaly detection alerts to risk levels
- Prioritizing risks involving public-facing AI services
- Addressing time-sensitive risks in real-time decision systems
- Adjusting risk rankings as models retrain and redeploy
- Using risk scoring to justify AI security investments
- Integrating ethical risk into overall severity ratings
- Managing risks in multi-tenant AI platforms
- Handling unanticipated AI behaviors in production
- Monitoring risk prioritization effectiveness over time
Module 7: Risk Treatment Strategies for AI Systems - Four risk treatment options: avoid, transfer, mitigate, accept
- Avoidance tactics for high-risk AI applications
- Risk transfer through insurance and contractual agreements
- Mitigation controls tailored to AI system vulnerabilities
- Acceptance criteria for residual AI risk
- Developing risk treatment plans with clear accountability
- Using model ensembles to reduce single-point failures
- Implementing input sanitization and adversarial defense layers
- Applying differential privacy to protect training data
- Encrypting models and data in use, at rest, and in transit
- Designing human-in-the-loop oversight for critical AI decisions
- Implementing model explainability as a risk control
- Using sandboxing and isolation for untrusted AI components
- Establishing fallback mechanisms for failed AI systems
- Validating third-party models before deployment
Module 8: Control Selection and Implementation Based on ISO 27002 - Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Choosing between qualitative and quantitative risk assessment approaches
- Applying the OCTAVE Allegro method to AI risk scenarios
- Using NIST SP 800-30 in AI risk contexts
- Adapting ISO 31000 principles for machine learning governance
- Developing AI-specific risk scales and impact matrices
- Measuring likelihood in probabilistic AI behaviors
- Assessing business impact of model failure or manipulation
- Linking risk severity to compliance and reputational damage
- Scenario-based risk modeling for autonomous decision systems
- Threat modeling using STRIDE and PASTA for AI
- Attack tree analysis for generative AI systems
- Developing risk scenarios for real-time inference systems
- Assessing cascading effects of AI failures across business units
- Using expert judgment to estimate AI risk when data is scarce
- Creating dynamic risk registers that update with model performance
Module 5: AI Asset Valuation and Criticality Analysis - Classifying AI assets: models, datasets, APIs, logs, and infrastructure
- Assigning business value to trained machine learning models
- Valuing sensitive training datasets and synthetic data outputs
- Assessing model interpretability as a risk control factor
- Determining the criticality of real-time inference systems
- Mapping AI dependencies in business continuity plans
- Evaluating the cost of model retraining and recovery
- Calculating financial exposure from AI decision errors
- Assessing customer harm from biased or erroneous AI outcomes
- Measuring regulatory penalties for non-compliant AI behavior
- Linking AI asset value to business KPIs and SLAs
- Using data lineage to trace high-value information flows
- Impact analysis of model drift on business operations
- Valuing intellectual property in proprietary AI frameworks
- Analyzing reputational damage from AI hallucinations or failures
Module 6: Risk Estimation and Prioritization in Dynamic AI Environments - Estimating risk exposure in continuously learning models
- Handling uncertainty in AI risk probability assessments
- Differentiating between static and adaptive risk profiles
- Prioritizing risks based on mitigation feasibility and impact
- Using risk heat maps to visualize AI threat landscapes
- Incorporating model confidence scores into risk calculations
- Linking anomaly detection alerts to risk levels
- Prioritizing risks involving public-facing AI services
- Addressing time-sensitive risks in real-time decision systems
- Adjusting risk rankings as models retrain and redeploy
- Using risk scoring to justify AI security investments
- Integrating ethical risk into overall severity ratings
- Managing risks in multi-tenant AI platforms
- Handling unanticipated AI behaviors in production
- Monitoring risk prioritization effectiveness over time
Module 7: Risk Treatment Strategies for AI Systems - Four risk treatment options: avoid, transfer, mitigate, accept
- Avoidance tactics for high-risk AI applications
- Risk transfer through insurance and contractual agreements
- Mitigation controls tailored to AI system vulnerabilities
- Acceptance criteria for residual AI risk
- Developing risk treatment plans with clear accountability
- Using model ensembles to reduce single-point failures
- Implementing input sanitization and adversarial defense layers
- Applying differential privacy to protect training data
- Encrypting models and data in use, at rest, and in transit
- Designing human-in-the-loop oversight for critical AI decisions
- Implementing model explainability as a risk control
- Using sandboxing and isolation for untrusted AI components
- Establishing fallback mechanisms for failed AI systems
- Validating third-party models before deployment
Module 8: Control Selection and Implementation Based on ISO 27002 - Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Estimating risk exposure in continuously learning models
- Handling uncertainty in AI risk probability assessments
- Differentiating between static and adaptive risk profiles
- Prioritizing risks based on mitigation feasibility and impact
- Using risk heat maps to visualize AI threat landscapes
- Incorporating model confidence scores into risk calculations
- Linking anomaly detection alerts to risk levels
- Prioritizing risks involving public-facing AI services
- Addressing time-sensitive risks in real-time decision systems
- Adjusting risk rankings as models retrain and redeploy
- Using risk scoring to justify AI security investments
- Integrating ethical risk into overall severity ratings
- Managing risks in multi-tenant AI platforms
- Handling unanticipated AI behaviors in production
- Monitoring risk prioritization effectiveness over time
Module 7: Risk Treatment Strategies for AI Systems - Four risk treatment options: avoid, transfer, mitigate, accept
- Avoidance tactics for high-risk AI applications
- Risk transfer through insurance and contractual agreements
- Mitigation controls tailored to AI system vulnerabilities
- Acceptance criteria for residual AI risk
- Developing risk treatment plans with clear accountability
- Using model ensembles to reduce single-point failures
- Implementing input sanitization and adversarial defense layers
- Applying differential privacy to protect training data
- Encrypting models and data in use, at rest, and in transit
- Designing human-in-the-loop oversight for critical AI decisions
- Implementing model explainability as a risk control
- Using sandboxing and isolation for untrusted AI components
- Establishing fallback mechanisms for failed AI systems
- Validating third-party models before deployment
Module 8: Control Selection and Implementation Based on ISO 27002 - Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Mapping ISO 27002 controls to AI-specific risks
- Access control strategies for model development environments
- Change management controls for AI model updates
- Configuration management for AI infrastructure
- Logging and monitoring controls for model behavior
- Backup and recovery for trained AI models
- Vendor risk management for AIaaS providers
- Secure development practices for machine learning code
- Data masking and pseudonymization in AI workflows
- Network security controls for AI inference endpoints
- Secure coding standards for AI model serving frameworks
- Endpoint protection for AI workstations and GPUs
- Misuse prevention in generative AI access controls
- Integrity verification of model checkpoints
- Time-bound access for data scientists and model testers
Module 9: AI Risk Communication and Stakeholder Reporting - Creating risk reports for technical teams and executives
- Translating AI risk into business impact language
- Designing dashboards for real-time AI risk visibility
- Drafting board-level summaries of AI risk posture
- Communicating risk treatment progress to auditors
- Engaging legal and compliance teams on AI exposure
- Reporting bias detection findings to ethics committees
- Documenting risk decisions for regulatory evidence
- Using visual storytelling to convey AI risk complexity
- Aligning risk messages with organizational risk appetite
- Conducting risk awareness training for AI teams
- Creating standardized report templates for recurring use
- Managing communication during AI incident responses
- Addressing media and public concerns about AI safety
- Integrating AI risk into enterprise risk management (ERM) reports
Module 10: Monitoring, Review, and Continuous Risk Adaptation - Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Scheduling regular review cycles for AI risk assessments
- Monitoring model performance degradation over time
- Tracking drift in input data distributions
- Setting thresholds for re-evaluation of high-risk models
- Updating risk registers after model retraining events
- Reviewing third-party AI provider security postures
- Conducting post-implementation risk reviews
- Using A/B testing results to update risk profiles
- Automating risk alert triggers based on operational metrics
- Documenting lessons learned from AI incidents
- Adjusting risk criteria based on organizational changes
- Updating threat models as AI capabilities evolve
- Integrating feedback from model monitoring systems
- Validating the effectiveness of implemented risk treatments
- Reporting review outcomes to risk owners and governance bodies
Module 11: Internal and External Audit Readiness for AI Risk - Preparing documentation for ISO 27001 certification audits
- Providing evidence of ISO 27005 compliance for AI systems
- Responding to auditor requests for risk assessment records
- Demonstrating traceability from risk to treatment actions
- Justifying risk acceptance decisions with documented rationale
- Handling requests for AI model risk artifacts
- Preparing AI risk statements for external reporting
- Organizing audit trails for model development and deployment
- Meeting GDPR, CCPA, and AI Act requirements through risk documentation
- Coordinating with internal audit teams on AI risk coverage
- Addressing findings from previous audits related to AI
- Creating a centralized repository for AI risk evidence
- Training staff on audit response protocols for AI systems
- Integrating risk documentation into compliance management tools
- Facilitating on-site auditor access to risk artifacts
Module 12: Integration with Security Governance and Compliance Programs - Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Embedding ISO 27005 practices into existing ISMS
- Aligning AI risk management with corporate governance standards
- Integrating risk outcomes into business continuity planning
- Linking AI risk decisions to incident response frameworks
- Coordinating with data protection impact assessments (DPIAs)
- Incorporating AI risk into vendor due diligence processes
- Supporting AI ethics review boards with risk analysis
- Feeding risk insights into strategic technology decisions
- Aligning with NIST AI Risk Management Framework (RMF)
- Connecting to SOC 2 trust principles for AI services
- Integrating with enterprise-wide risk management platforms
- Using AI risk data to inform cyber insurance applications
- Supporting CISO dashboards with AI-specific metrics
- Enabling risk-based authorization for AI development access
- Building cross-functional AI risk governance teams
Module 13: Practical Risk Assessment Project – End-to-End Application - Selecting a real-world AI use case for risk assessment
- Defining the scope and boundaries of the assessment
- Identifying and classifying AI assets and data flows
- Engaging stakeholders for input and validation
- Conducting a full threat and vulnerability analysis
- Estimating likelihood and impact for identified risks
- Creating a comprehensive risk register with AI context
- Prioritizing top-tier risks using business impact criteria
- Selecting and documenting appropriate risk treatments
- Developing action plans with owners and timelines
- Establishing metrics for treatment effectiveness
- Documenting assumptions and limitations in the process
- Producing a final executive summary report
- Presenting findings to a simulated governance committee
- Receiving structured feedback and refining deliverables
Module 14: Advanced Topics in AI Risk and Emerging Standards - Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios
Module 15: Career Advancement, Certification, and Next Steps - How to leverage your Certificate of Completion for career growth
- Adding ISO 27005 expertise to your LinkedIn profile and resume
- Using course projects as work samples in job applications
- Negotiating higher rates or promotions with new credentials
- Pursuing advanced certifications in AI governance and security
- Networking with other professionals certified by The Art of Service
- Accessing exclusive alumni resources and updates
- Joining AI risk working groups and industry forums
- Transitioning into roles like AI Risk Officer or ML Security Lead
- Providing consultancy services in AI risk assessments
- Designing internal training programs based on course content
- Contributing to open-source AI risk frameworks
- Mentoring junior team members in risk best practices
- Staying current with emerging AI threats and controls
- Planning your next professional development steps in risk leadership
- Risk management for foundation models and large language models
- Securing federated learning and distributed AI training
- Managing risks in AI-generated content and deepfakes
- Risk considerations for autonomous agents and AI orchestration
- Handling emergent behaviors in recursive AI systems
- Assessing AI influence on financial markets and critical infrastructure
- Regulatory horizon scanning: upcoming AI directives and laws
- Preparing for the EU AI Act compliance requirements
- Understanding OECD AI Principles in risk governance
- Integrating sustainability into AI risk decision-making
- Addressing energy consumption and environmental risks of AI
- Managing systemic risks in AI-driven decision ecosystems
- Assessing geopolitical risks in AI supply chains
- Considering long-term societal impacts of AI deployments
- Building resilience against AI model collapse scenarios