Mastering ISO/IEC 27001 Implementation for AI-Driven Organizations
You're leading digital transformation in an organization where AI isn’t just a tool-it’s the engine. Yet with every algorithm deployed, every model trained, and every data pipeline scaled, there's an invisible risk compounding beneath the surface: information security gaps that traditional frameworks don't fully address. Regulators are watching. Boardrooms demand accountability. A single breach in your AI infrastructure could cripple investor confidence, trigger fines, and endanger your organization’s reputation overnight. You need more than compliance checklists-you need a proven, strategic blueprint tailored to the unique attack surface of intelligent systems. Mastering ISO/IEC 27001 Implementation for AI-Driven Organizations is not another generic compliance course. It’s a battle-tested roadmap designed specifically for security architects, risk leads, and transformation officers who must embed robust information security into AI models, MLOps pipelines, and autonomous decision systems-without slowing down innovation. In as little as 21 days, you’ll move from uncertainty to clarity, transforming your approach into one that delivers a certified, board-ready ISMS aligned with ISO/IEC 27001, fully contextualized for AI data flows, model integrity, third-party integrations, and dynamic threat landscapes. Ravi Mehta, Lead AI Security Officer at a global fintech firm, used this framework to secure approval for a $4.2M compliance upgrade. His implementation was fast-tracked by regulators because it demonstrated precise alignment between ISO 27001 controls and AI-specific risk vectors. He now leads enterprise security across two continents. You don’t need more theory. You need a system that works under pressure, proven across real AI environments, and structured so you can implement it step by step-without guesswork. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, and Always Accessible
This course is self-paced, with immediate online access upon enrollment. You control your timeline, study during peak focus hours, and progress according to your real-world workload-no fixed schedules, no mandatory sessions, and no artificial time pressure. Most learners complete the core implementation pathway in 18 to 25 hours, with tangible results visible within the first 72 hours of starting Module 1. The average time to draft your first AI-specific Statement of Applicability is under 5 days. Lifetime Access & Ongoing Updates
You receive lifetime access to all course materials, including every future update at no additional cost. As ISO standards evolve, AI regulations shift, and new control interpretations emerge, your learning evolves with them. All content is mobile-friendly and accessible 24/7 from any device-whether you're reviewing a risk treatment plan on your phone during a commute or refining a policy document from a tablet at home. Instructor Support & Expert Guidance
You are not alone. This course includes direct access to a dedicated support channel staffed by lead ISO 27001 auditors and AI security specialists. Ask questions, submit draft control mappings, and receive structured feedback designed to accelerate your implementation and reduce rework. Certification with Global Recognition
Upon completion, you earn a Certificate of Completion issued by The Art of Service-a globally recognized credential trusted by over 90,000 professionals in 157 countries. This certification validates your mastery of ISO/IEC 27001 within AI environments and strengthens your position as a credible leader in digital governance. Transparent Pricing, No Hidden Fees
The pricing model is straightforward with no recurring charges, hidden costs, or upsells. What you see is exactly what you get: one-time access to a complete, high-precision implementation system. - Secure payment processing via Visa
- Secure payment processing via Mastercard
- Secure payment processing via PayPal
Zero-Risk Enrollment: Satisfied or Refunded
We offer a full money-back guarantee if you complete the first three modules and find the course does not meet your expectations. This isn't just confidence in our material-it's a complete risk reversal. You’ll receive a confirmation email immediately after enrollment. Your access details and login credentials will be sent separately once your enrollment is fully processed and verified-ensuring accuracy and security. “Will This Work For Me?” – Addressing Your Biggest Doubt
Even if you’re not a formal auditor. Even if your AI stack is hybrid or partially outsourced. Even if your organization has never passed a compliance audit before-this course works. It works because it was built by practitioners who've led ISO 27001 certifications for AI startups, enterprise AI divisions, and regulated government AI pilots. It works because every template, every checklist, and every workflow was stress-tested in environments just like yours. If you’re a CISO balancing innovation and risk, a data governance lead navigating regulatory scrutiny, or an AI engineering manager required to meet compliance mandates-this course gives you the clarity, documentation, and confidence to act decisively. You're not buying content. You're investing in a proven implementation architecture with built-in risk mitigation, audit readiness, and executive alignment-backed by a satisfaction guarantee.
Module 1: Foundations of AI-Driven Information Security - Understanding the core principles of ISO/IEC 27001 in the context of AI workloads
- Defining information security for machine learning pipelines and autonomous systems
- Key differences between traditional IT security and AI-specific risk domains
- Mapping AI data lifecycle stages to information security requirements
- Identifying critical assets in AI-driven organizations: models, training data, APIs
- The role of confidentiality, integrity, and availability in AI inference operations
- Threat modeling for generative AI and large language model deployments
- Establishing the business case for an AI-integrated ISMS
- Recognizing regulatory triggers: GDPR, NIST AI RMF, and sector-specific mandates
- Aligning information security objectives with AI ethics and governance policies
Module 2: Context Establishment and Leadership Engagement - Defining organizational context for AI systems under clause 4.1
- Identifying internal and external stakeholders influencing AI security posture
- Performing PESTEL analysis for AI deployment environments
- Scoping the ISMS to include AI development, testing, and deployment zones
- Engaging executive leadership through risk-aware decision frameworks
- Developing AI-specific information security policies endorsed by board leadership
- Establishing governance structures for AI risk oversight
- Defining roles and responsibilities for AI security across data science and engineering teams
- Setting measurable objectives for AI-integrated information security programs
- Creating a risk-aware culture in agile and DevOps environments
Module 3: Risk Assessment Methodology for AI Systems - Selecting the appropriate risk assessment approach for AI environments
- Adapting ISO 27005 principles to AI model lifecycle risks
- Identifying AI-specific threats: data poisoning, model inversion, prompt injection
- Assessing vulnerabilities in training datasets and model architecture
- Evaluating exposure of AI APIs and inference endpoints
- Quantifying impact of AI model degradation or adversarial attacks
- Using likelihood and consequence matrices tailored to AI use cases
- Documenting AI risk scenarios in formal risk register format
- Integrating third-party AI vendor risks into organizational risk profile
- Applying attack tree analysis to autonomous decision-making systems
Module 4: Statement of Applicability for AI Environments - Justifying inclusion and exclusion of Annex A controls in AI contexts
- Customizing control objectives for AI development and deployment
- Mapping AI-specific risks to relevant ISO/IEC 27001 controls
- Documenting rationale for not applying certain physical or legacy IT controls
- Creating an AI-optimized Statement of Applicability template
- Aligning control selection with MLOps and CI/CD practices
- Incorporating AI supply chain security considerations
- Ensuring consistency with model monitoring and retraining schedules
- Reviewing and updating SoA after major AI system changes
- Using SoA as a communication tool with auditors and technical teams
Module 5: Risk Treatment Planning and AI Control Deployment - Developing a risk treatment plan specific to AI model risks
- Selecting risk response strategies: avoid, modify, share, retain
- Assigning ownership for AI control implementation across technical teams
- Integrating security controls into automated model training pipelines
- Implementing access restrictions for model weights and training logs
- Securing model repositories and registry services
- Enforcing version control and change management for AI artifacts
- Applying encryption to sensitive training data and inference payloads
- Configuring API gateways with authentication and rate limiting for AI services
- Automating control enforcement using infrastructure-as-code templates
Module 6: AI-Specific Annex A Control Implementation - Applying A.5.7 to AI intellectual property protection
- Implementing A.5.23 for AI project confidentiality during development
- Using A.6.9 to segregate duties in model training and deployment roles
- Securing A.7.4 for AI contractor access to model environments
- Applying A.8.2 to protect training datasets from unauthorized access
- Using A.8.3 for encryption of model parameters in transit and at rest
- Enforcing A.8.36 for integrity checks on pre-trained models
- Applying A.8.10 to monitor for anomalies in AI inference behavior
- Using A.8.28 for secure disposal of obsolete training datasets
- Implementing A.8.35 for model provenance and lineage tracking
- Applying A.9.2 to enforce role-based access to model endpoints
- Using A.9.4 for federated identity with external AI platforms
- Securing A.10.2 for cryptographic key management in AI inference
- Applying A.12.4 to log model prediction requests and responses
- Using A.12.6 to audit model drift and retraining triggers
- Implementing A.12.7 for secure logging in distributed AI systems
- Applying A.13.1 to protect AI data in cross-border inference
- Using A.13.3 for secure interfaces between AI models and downstream systems
- Applying A.14.2 to secure development environments for AI code
- Using A.15.2 to manage third-party AI vendor contracts and SLAs
- Implementing A.16.1 for incident response specific to model tampering
- Applying A.17.2 to ensure continuity of AI-powered business functions
- Using A.18.1 for comprehensive documentation of AI model behavior
Module 7: AI Model Security and Adversarial Defense - Implementing controls against data poisoning attacks during training
- Detecting model inversion and membership inference threats
- Securing model weights from unauthorized extraction
- Hardening inference servers against prompt injection exploits
- Validating input sanitization for natural language processing models
- Implementing adversarial training as a preventive control
- Monitoring for model drift indicating potential compromise
- Using explainability tools to verify model decision consistency
- Securing model update mechanisms against tampering
- Documenting model safety testing procedures in compliance evidence
Module 8: Third-Party and Supply Chain Risk in AI - Assessing risks of pre-trained models from public repositories
- Validating security posture of external AI API providers
- Conducting due diligence on open-source AI framework dependencies
- Managing risks of AI-as-a-service platforms (e.g. cloud inference APIs)
- Establishing contractual obligations for AI vendor security compliance
- Monitoring third-party AI services for breach notifications
- Implementing controls for models fine-tuned on customer data
- Securing data flows between internal systems and external AI engines
- Ensuring compliance cascading in AI supply chain agreements
- Using vendor risk scoring tailored to AI security maturity
Module 9: Monitoring, Review, and Internal Audit - Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Understanding the core principles of ISO/IEC 27001 in the context of AI workloads
- Defining information security for machine learning pipelines and autonomous systems
- Key differences between traditional IT security and AI-specific risk domains
- Mapping AI data lifecycle stages to information security requirements
- Identifying critical assets in AI-driven organizations: models, training data, APIs
- The role of confidentiality, integrity, and availability in AI inference operations
- Threat modeling for generative AI and large language model deployments
- Establishing the business case for an AI-integrated ISMS
- Recognizing regulatory triggers: GDPR, NIST AI RMF, and sector-specific mandates
- Aligning information security objectives with AI ethics and governance policies
Module 2: Context Establishment and Leadership Engagement - Defining organizational context for AI systems under clause 4.1
- Identifying internal and external stakeholders influencing AI security posture
- Performing PESTEL analysis for AI deployment environments
- Scoping the ISMS to include AI development, testing, and deployment zones
- Engaging executive leadership through risk-aware decision frameworks
- Developing AI-specific information security policies endorsed by board leadership
- Establishing governance structures for AI risk oversight
- Defining roles and responsibilities for AI security across data science and engineering teams
- Setting measurable objectives for AI-integrated information security programs
- Creating a risk-aware culture in agile and DevOps environments
Module 3: Risk Assessment Methodology for AI Systems - Selecting the appropriate risk assessment approach for AI environments
- Adapting ISO 27005 principles to AI model lifecycle risks
- Identifying AI-specific threats: data poisoning, model inversion, prompt injection
- Assessing vulnerabilities in training datasets and model architecture
- Evaluating exposure of AI APIs and inference endpoints
- Quantifying impact of AI model degradation or adversarial attacks
- Using likelihood and consequence matrices tailored to AI use cases
- Documenting AI risk scenarios in formal risk register format
- Integrating third-party AI vendor risks into organizational risk profile
- Applying attack tree analysis to autonomous decision-making systems
Module 4: Statement of Applicability for AI Environments - Justifying inclusion and exclusion of Annex A controls in AI contexts
- Customizing control objectives for AI development and deployment
- Mapping AI-specific risks to relevant ISO/IEC 27001 controls
- Documenting rationale for not applying certain physical or legacy IT controls
- Creating an AI-optimized Statement of Applicability template
- Aligning control selection with MLOps and CI/CD practices
- Incorporating AI supply chain security considerations
- Ensuring consistency with model monitoring and retraining schedules
- Reviewing and updating SoA after major AI system changes
- Using SoA as a communication tool with auditors and technical teams
Module 5: Risk Treatment Planning and AI Control Deployment - Developing a risk treatment plan specific to AI model risks
- Selecting risk response strategies: avoid, modify, share, retain
- Assigning ownership for AI control implementation across technical teams
- Integrating security controls into automated model training pipelines
- Implementing access restrictions for model weights and training logs
- Securing model repositories and registry services
- Enforcing version control and change management for AI artifacts
- Applying encryption to sensitive training data and inference payloads
- Configuring API gateways with authentication and rate limiting for AI services
- Automating control enforcement using infrastructure-as-code templates
Module 6: AI-Specific Annex A Control Implementation - Applying A.5.7 to AI intellectual property protection
- Implementing A.5.23 for AI project confidentiality during development
- Using A.6.9 to segregate duties in model training and deployment roles
- Securing A.7.4 for AI contractor access to model environments
- Applying A.8.2 to protect training datasets from unauthorized access
- Using A.8.3 for encryption of model parameters in transit and at rest
- Enforcing A.8.36 for integrity checks on pre-trained models
- Applying A.8.10 to monitor for anomalies in AI inference behavior
- Using A.8.28 for secure disposal of obsolete training datasets
- Implementing A.8.35 for model provenance and lineage tracking
- Applying A.9.2 to enforce role-based access to model endpoints
- Using A.9.4 for federated identity with external AI platforms
- Securing A.10.2 for cryptographic key management in AI inference
- Applying A.12.4 to log model prediction requests and responses
- Using A.12.6 to audit model drift and retraining triggers
- Implementing A.12.7 for secure logging in distributed AI systems
- Applying A.13.1 to protect AI data in cross-border inference
- Using A.13.3 for secure interfaces between AI models and downstream systems
- Applying A.14.2 to secure development environments for AI code
- Using A.15.2 to manage third-party AI vendor contracts and SLAs
- Implementing A.16.1 for incident response specific to model tampering
- Applying A.17.2 to ensure continuity of AI-powered business functions
- Using A.18.1 for comprehensive documentation of AI model behavior
Module 7: AI Model Security and Adversarial Defense - Implementing controls against data poisoning attacks during training
- Detecting model inversion and membership inference threats
- Securing model weights from unauthorized extraction
- Hardening inference servers against prompt injection exploits
- Validating input sanitization for natural language processing models
- Implementing adversarial training as a preventive control
- Monitoring for model drift indicating potential compromise
- Using explainability tools to verify model decision consistency
- Securing model update mechanisms against tampering
- Documenting model safety testing procedures in compliance evidence
Module 8: Third-Party and Supply Chain Risk in AI - Assessing risks of pre-trained models from public repositories
- Validating security posture of external AI API providers
- Conducting due diligence on open-source AI framework dependencies
- Managing risks of AI-as-a-service platforms (e.g. cloud inference APIs)
- Establishing contractual obligations for AI vendor security compliance
- Monitoring third-party AI services for breach notifications
- Implementing controls for models fine-tuned on customer data
- Securing data flows between internal systems and external AI engines
- Ensuring compliance cascading in AI supply chain agreements
- Using vendor risk scoring tailored to AI security maturity
Module 9: Monitoring, Review, and Internal Audit - Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Selecting the appropriate risk assessment approach for AI environments
- Adapting ISO 27005 principles to AI model lifecycle risks
- Identifying AI-specific threats: data poisoning, model inversion, prompt injection
- Assessing vulnerabilities in training datasets and model architecture
- Evaluating exposure of AI APIs and inference endpoints
- Quantifying impact of AI model degradation or adversarial attacks
- Using likelihood and consequence matrices tailored to AI use cases
- Documenting AI risk scenarios in formal risk register format
- Integrating third-party AI vendor risks into organizational risk profile
- Applying attack tree analysis to autonomous decision-making systems
Module 4: Statement of Applicability for AI Environments - Justifying inclusion and exclusion of Annex A controls in AI contexts
- Customizing control objectives for AI development and deployment
- Mapping AI-specific risks to relevant ISO/IEC 27001 controls
- Documenting rationale for not applying certain physical or legacy IT controls
- Creating an AI-optimized Statement of Applicability template
- Aligning control selection with MLOps and CI/CD practices
- Incorporating AI supply chain security considerations
- Ensuring consistency with model monitoring and retraining schedules
- Reviewing and updating SoA after major AI system changes
- Using SoA as a communication tool with auditors and technical teams
Module 5: Risk Treatment Planning and AI Control Deployment - Developing a risk treatment plan specific to AI model risks
- Selecting risk response strategies: avoid, modify, share, retain
- Assigning ownership for AI control implementation across technical teams
- Integrating security controls into automated model training pipelines
- Implementing access restrictions for model weights and training logs
- Securing model repositories and registry services
- Enforcing version control and change management for AI artifacts
- Applying encryption to sensitive training data and inference payloads
- Configuring API gateways with authentication and rate limiting for AI services
- Automating control enforcement using infrastructure-as-code templates
Module 6: AI-Specific Annex A Control Implementation - Applying A.5.7 to AI intellectual property protection
- Implementing A.5.23 for AI project confidentiality during development
- Using A.6.9 to segregate duties in model training and deployment roles
- Securing A.7.4 for AI contractor access to model environments
- Applying A.8.2 to protect training datasets from unauthorized access
- Using A.8.3 for encryption of model parameters in transit and at rest
- Enforcing A.8.36 for integrity checks on pre-trained models
- Applying A.8.10 to monitor for anomalies in AI inference behavior
- Using A.8.28 for secure disposal of obsolete training datasets
- Implementing A.8.35 for model provenance and lineage tracking
- Applying A.9.2 to enforce role-based access to model endpoints
- Using A.9.4 for federated identity with external AI platforms
- Securing A.10.2 for cryptographic key management in AI inference
- Applying A.12.4 to log model prediction requests and responses
- Using A.12.6 to audit model drift and retraining triggers
- Implementing A.12.7 for secure logging in distributed AI systems
- Applying A.13.1 to protect AI data in cross-border inference
- Using A.13.3 for secure interfaces between AI models and downstream systems
- Applying A.14.2 to secure development environments for AI code
- Using A.15.2 to manage third-party AI vendor contracts and SLAs
- Implementing A.16.1 for incident response specific to model tampering
- Applying A.17.2 to ensure continuity of AI-powered business functions
- Using A.18.1 for comprehensive documentation of AI model behavior
Module 7: AI Model Security and Adversarial Defense - Implementing controls against data poisoning attacks during training
- Detecting model inversion and membership inference threats
- Securing model weights from unauthorized extraction
- Hardening inference servers against prompt injection exploits
- Validating input sanitization for natural language processing models
- Implementing adversarial training as a preventive control
- Monitoring for model drift indicating potential compromise
- Using explainability tools to verify model decision consistency
- Securing model update mechanisms against tampering
- Documenting model safety testing procedures in compliance evidence
Module 8: Third-Party and Supply Chain Risk in AI - Assessing risks of pre-trained models from public repositories
- Validating security posture of external AI API providers
- Conducting due diligence on open-source AI framework dependencies
- Managing risks of AI-as-a-service platforms (e.g. cloud inference APIs)
- Establishing contractual obligations for AI vendor security compliance
- Monitoring third-party AI services for breach notifications
- Implementing controls for models fine-tuned on customer data
- Securing data flows between internal systems and external AI engines
- Ensuring compliance cascading in AI supply chain agreements
- Using vendor risk scoring tailored to AI security maturity
Module 9: Monitoring, Review, and Internal Audit - Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Developing a risk treatment plan specific to AI model risks
- Selecting risk response strategies: avoid, modify, share, retain
- Assigning ownership for AI control implementation across technical teams
- Integrating security controls into automated model training pipelines
- Implementing access restrictions for model weights and training logs
- Securing model repositories and registry services
- Enforcing version control and change management for AI artifacts
- Applying encryption to sensitive training data and inference payloads
- Configuring API gateways with authentication and rate limiting for AI services
- Automating control enforcement using infrastructure-as-code templates
Module 6: AI-Specific Annex A Control Implementation - Applying A.5.7 to AI intellectual property protection
- Implementing A.5.23 for AI project confidentiality during development
- Using A.6.9 to segregate duties in model training and deployment roles
- Securing A.7.4 for AI contractor access to model environments
- Applying A.8.2 to protect training datasets from unauthorized access
- Using A.8.3 for encryption of model parameters in transit and at rest
- Enforcing A.8.36 for integrity checks on pre-trained models
- Applying A.8.10 to monitor for anomalies in AI inference behavior
- Using A.8.28 for secure disposal of obsolete training datasets
- Implementing A.8.35 for model provenance and lineage tracking
- Applying A.9.2 to enforce role-based access to model endpoints
- Using A.9.4 for federated identity with external AI platforms
- Securing A.10.2 for cryptographic key management in AI inference
- Applying A.12.4 to log model prediction requests and responses
- Using A.12.6 to audit model drift and retraining triggers
- Implementing A.12.7 for secure logging in distributed AI systems
- Applying A.13.1 to protect AI data in cross-border inference
- Using A.13.3 for secure interfaces between AI models and downstream systems
- Applying A.14.2 to secure development environments for AI code
- Using A.15.2 to manage third-party AI vendor contracts and SLAs
- Implementing A.16.1 for incident response specific to model tampering
- Applying A.17.2 to ensure continuity of AI-powered business functions
- Using A.18.1 for comprehensive documentation of AI model behavior
Module 7: AI Model Security and Adversarial Defense - Implementing controls against data poisoning attacks during training
- Detecting model inversion and membership inference threats
- Securing model weights from unauthorized extraction
- Hardening inference servers against prompt injection exploits
- Validating input sanitization for natural language processing models
- Implementing adversarial training as a preventive control
- Monitoring for model drift indicating potential compromise
- Using explainability tools to verify model decision consistency
- Securing model update mechanisms against tampering
- Documenting model safety testing procedures in compliance evidence
Module 8: Third-Party and Supply Chain Risk in AI - Assessing risks of pre-trained models from public repositories
- Validating security posture of external AI API providers
- Conducting due diligence on open-source AI framework dependencies
- Managing risks of AI-as-a-service platforms (e.g. cloud inference APIs)
- Establishing contractual obligations for AI vendor security compliance
- Monitoring third-party AI services for breach notifications
- Implementing controls for models fine-tuned on customer data
- Securing data flows between internal systems and external AI engines
- Ensuring compliance cascading in AI supply chain agreements
- Using vendor risk scoring tailored to AI security maturity
Module 9: Monitoring, Review, and Internal Audit - Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Implementing controls against data poisoning attacks during training
- Detecting model inversion and membership inference threats
- Securing model weights from unauthorized extraction
- Hardening inference servers against prompt injection exploits
- Validating input sanitization for natural language processing models
- Implementing adversarial training as a preventive control
- Monitoring for model drift indicating potential compromise
- Using explainability tools to verify model decision consistency
- Securing model update mechanisms against tampering
- Documenting model safety testing procedures in compliance evidence
Module 8: Third-Party and Supply Chain Risk in AI - Assessing risks of pre-trained models from public repositories
- Validating security posture of external AI API providers
- Conducting due diligence on open-source AI framework dependencies
- Managing risks of AI-as-a-service platforms (e.g. cloud inference APIs)
- Establishing contractual obligations for AI vendor security compliance
- Monitoring third-party AI services for breach notifications
- Implementing controls for models fine-tuned on customer data
- Securing data flows between internal systems and external AI engines
- Ensuring compliance cascading in AI supply chain agreements
- Using vendor risk scoring tailored to AI security maturity
Module 9: Monitoring, Review, and Internal Audit - Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Scheduling AI-specific internal audits within the ISMS cycle
- Developing checklists for auditing model development and deployment
- Reviewing access logs for unusual model training or deployment activity
- Auditing adherence to AI model version control policies
- Verifying enforcement of data usage restrictions in training
- Assessing effectiveness of AI incident response playbooks
- Conducting management review meetings with AI risk metrics
- Presenting AI control performance to executive leadership
- Updating risk assessments after model retraining or redeployment
- Using automated policy compliance scanning in CI/CD pipelines
Module 10: Certification Readiness and External Audit Preparation - Preparing for stage 1 audit with AI-specific documentation
- Compiling evidence for AI model-related controls
- Rehearsing auditor interviews with AI engineering teams
- Addressing auditor questions about dynamic AI environments
- Explaining control applicability to non-static model behavior
- Demonstrating continuous control effectiveness in AI systems
- Preparing responses to potential non-conformities in AI scope
- Mapping audit findings to corrective action plans with technical owners
- Finalizing AI-integrated Statement of Applicability for submission
- Obtaining certification with confidence through structured readiness
Module 11: Continuous Improvement and AI System Evolution - Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities
Module 12: Real-World Implementation Projects and Templates - Hands-on exercise: Scoping an ISMS for a computer vision deployment
- Project: Drafting an AI-specific information security policy
- Exercise: Conducting a risk assessment for a chatbot system
- Project: Creating a Statement of Applicability for LLM usage
- Exercise: Documenting model access controls for audit evidence
- Project: Building an AI vendor risk assessment template
- Exercise: Mapping attack scenarios to Annex A controls
- Project: Designing an incident response playbook for model compromise
- Exercise: Preparing management review materials with AI metrics
- Project: Finalizing certification readiness checklist for AI scope
- Access to downloadable templates: AI risk register, SoA, policy drafts
- Customizable checklists for internal AI audits
- Model security configuration benchmarks
- AI compliance evidence tracker spreadsheet
- Executive briefing deck template for ISMS status
- Applying PDCA cycle to AI model lifecycle management
- Updating ISMS controls after AI architecture changes
- Integrating new AI regulations into existing compliance framework
- Scaling ISMS across multiple AI projects and business units
- Measuring control effectiveness through AI security KPIs
- Using feedback loops from AI incident data to improve controls
- Revising risk assessments after introduction of new AI capabilities
- Ensuring ISMS remains relevant with advances in generative AI
- Automating compliance evidence collection from MLOps tools
- Training new AI team members on information security responsibilities