A tailored course, built for your situation
Mastering ISO 27001 for Applied AI Leaders
Build audit-ready security governance into AI systems from design through deployment
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI governance work is too often seen as a technical checklist, not a strategic asset
The situation this course is for
Even the most robust AI security practices can go unnoticed if they’re not framed in language that resonates with executives and auditors. Practitioners like Miku build deep compliance into models, but without a recognized framework, that effort remains invisible to leadership.
Who this is for
Senior AI leader in enterprise tech driving secure, compliant AI adoption; already implementing governance-by-design but needs higher-visibility validation
Who this is not for
Individuals focused on academic AI, non-enterprise AI startups, or those not involved in systematizing compliance for production AI platforms
What you walk away with
- Present AI security work in ISO 27001-aligned terms that resonate in executive conversations
- Surface evidence of compliance maturity during funding or audit cycles
- Turn internal control practices into reusable, auditable documentation
- Anticipate and pre-empt regulator follow-ups with structured control mapping
- Position your AI governance approach as a benchmark across the organization
The 12 modules (with all 144 chapters)
Module 1. Why ISO 27001 is becoming the baseline for AI infrastructure funding
Understand how private capital markets are using ISO 27001 as a signal of technical and governance maturity in AI ventures. This module connects security practices to investor confidence and funding flow.
12 chapters in this module
- How hyperscaler financing decisions now factor in compliance maturity
- ISO 27001 as a due diligence signal in credit risk assessment
- Mapping AI system boundaries to information security contexts
- Building investor-facing narratives from technical controls
- Case study: AI startup funding round delayed on audit gaps
- When private credit teams request SOC 2 or ISO 27001 evidence
- Aligning model lifecycle phases with information security domains
- Translating AI risk registers into ISO clause language
- Common gaps in AI teams' first ISO 27001 readiness assessments
- Integrating compliance into AI project kickoff templates
- Building traceability from model design to control ownership
- Documenting security architecture for external review
Module 2. Information security context for AI systems
Define scope and context for applying ISO 27001 to AI workloads, including data flows, model retraining cycles, and access patterns.
12 chapters in this module
- Identifying information assets in AI pipelines
- Classifying training data by sensitivity and retention needs
- Mapping inference requests to access control policies
- Defining custodianship across model development teams
- Securing model weights and configuration artifacts
- Handling third-party data in fine-tuning workflows
- Logging and monitoring for AI-specific access events
- Integrating data lineage into security documentation
- Boundary definition for AI microservices
- Documenting data movement across regions
- Managing temporary storage in inference pipelines
- Threat modeling for AI system dependencies
Module 3. Leadership and organizational context under ISO 27001
Align executive accountability with AI governance, ensuring security is owned and resourced at the leadership level.
12 chapters in this module
- Assigning information security roles in AI teams
- How C-suite ownership strengthens audit posture
- Integrating AI security into enterprise risk reporting
- Developing security policies tailored to model lifecycle
- Creating role-based access frameworks for data scientists
- Documenting leadership commitment to compliance
- Security training plans for AI engineering staff
- Integrating incident response into model operations
- Maintaining oversight during rapid iteration cycles
- Establishing metrics for AI security performance
- Reviewing compliance posture at sprint planning
- Setting expectations for ethical AI use in policy
Module 4. Risk assessment and treatment planning for AI
Apply ISO 27001 risk methodology to AI-specific threats like model drift, prompt injection, and data poisoning.
12 chapters in this module
- Identifying assets unique to AI systems
- Threat actors targeting machine learning pipelines
- Likelihood of model inversion attacks
- Impact of adversarial inputs on production models
- Data integrity risks in continuous retraining
- Evaluating exposure from third-party AI tools
- Mapping threats to ISO 27001 control clauses
- Developing risk treatment plans for high-severity gaps
- Integrating findings into model validation gates
- Creating risk heat maps for executive review
- Maintaining risk registers across model versions
- Updating assessments after incident detection
Module 5. Embedding security in AI system development
Apply ISO 27001 controls to AI design, training, testing, and deployment workflows.
12 chapters in this module
- Secure coding standards for model training scripts
- Version control for datasets and model checkpoints
- Access governance for model deployment pipelines
- Code reviews focused on security and compliance
- Managing secrets in model serving environments
- Enforcing secure configurations across clusters
- Automating compliance checks in CI/CD gates
- Auditing changes to model architecture
- Documenting model provenance for audit
- Validating model drift detection mechanisms
- Securing model explanation systems
- Integrating security feedback into retraining cycles
Module 6. Access control for AI workloads
Implement least privilege and role-based access across AI platforms, data stores, and model endpoints.
12 chapters in this module
- Designing roles for AI engineering workflows
- Managing access to sensitive training data
- Controlling model deployment permissions
- Securing access to inference APIs
- Auditing access to model performance logs
- Handling access in multi-tenant AI platforms
- Integrating with enterprise identity providers
- Managing service accounts for batch jobs
- Temporary access for debugging and monitoring
- Revoking access after project completion
- Monitoring for anomalous access patterns
- Documenting access decisions for auditors
Module 7. Cryptographic controls in AI systems
Apply encryption, hashing, and key management to protect AI models, data, and outputs.
12 chapters in this module
- Encrypting data at rest in training pipelines
- Securing model weights during transit
- Protecting inference payloads in flight
- Key management for distributed AI environments
- Using hashing to detect model tampering
- Securing model cards and metadata
- Auditing cryptographic policy compliance
- Handling key rotation in model serving
- Integrating HSMs into model deployment
- Documenting crypto usage for auditors
- Managing certificates for AI endpoints
- Validating cryptographic libraries in third-party tools
Module 8. Physical and environmental security for AI infrastructure
Extend ISO 27001 physical controls to cloud and hybrid AI deployments.
12 chapters in this module
- Assessing physical risks in cloud provider environments
- Managing access to co-location facilities
- Securing hardware used for training
- Environmental monitoring for compute clusters
- Protecting backup media for model artifacts
- Inventory management for GPUs and accelerators
- Securing remote access to physical devices
- Documenting provider security commitments
- Validating physical access logs for audits
- Managing decommissioning of AI hardware
- Tracking equipment across lifecycle stages
- Integrating physical security with logical controls
Module 9. Operations security for AI systems
Establish secure operational practices for monitoring, logging, and incident response in AI environments.
12 chapters in this module
- Logging model inputs and outputs securely
- Monitoring for model performance degradation
- Detecting prompt injection attempts
- Incident response for data poisoning
- Backup strategies for model checkpoints
- Recovery testing for AI workloads
- Change management for model updates
- Securing CI/CD pipelines
- Managing vulnerabilities in AI libraries
- Patch management for inference servers
- Auditing operational changes
- Documenting runbook compliance
Module 10. Communications security for AI APIs and pipelines
Secure data in transit across AI components and with external systems.
12 chapters in this module
- Enforcing TLS for model inference endpoints
- Validating client certificates for API access
- Securing data exchange between microservices
- Managing API keys and tokens
- Detecting and blocking malicious API traffic
- Rate limiting for inference protection
- Encrypting model metadata in transit
- Auditing communication patterns
- Securing inter-data-center traffic
- Handling cross-origin requests securely
- Integrating DDoS protection for AI services
- Documenting network security policies
Module 11. Monitoring, review, and continuous improvement
Institutionalize ongoing compliance through internal audit, management review, and continual improvement.
12 chapters in this module
- Scheduling internal ISO 27001 audits
- Tracking control effectiveness over time
- Reporting compliance metrics to leadership
- Updating risk assessments after incidents
- Improving controls based on audit findings
- Benchmarking against industry peers
- Conducting management review meetings
- Documenting continual improvement actions
- Updating policies after framework changes
- Aligning with new regulatory expectations
- Training teams on updated controls
- Securing audit trails for compliance verification
Module 12. Preparing for external audit and certification
Compile evidence, coordinate with auditors, and position your AI program for successful ISO 27001 certification.
12 chapters in this module
- Selecting accredited certification bodies
- Preparing documentation for Stage 1 audit
- Conducting internal mock audits
- Gathering evidence of control implementation
- Coordinating auditor access to systems
- Responding to non-conformities
- Finalizing Statement of Applicability
- Preparing leadership for audit interviews
- Demonstrating continuous compliance
- Maintaining certification after audit
- Communicating certification achievement
- Using certification in vendor evaluations
How this maps to your situation
- AI system design and deployment
- Compliance and audit readiness
- Executive communication and visibility
- Cross-functional governance coordination
Before vs. after
Before
AI governance efforts remain embedded in technical workflows, undetected by leadership or auditors
After
Security and compliance practices are structured, visible, and recognized as strategic assets during funding, audit, and executive reviews
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: 90 minutes of focused reading, structured to fit in a single Sunday morning
If nothing changes
Without a recognized compliance framework, even robust AI governance may be overlooked in funding decisions, audit findings, or strategic planning , leaving your team's work under-recognized and vulnerable to external criticism.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for applied AI leaders , connecting ISO 27001 controls to real model lifecycle decisions, deployment patterns, and executive expectations.
Frequently asked
Is this relevant if we're not pursuing formal ISO 27001 certification?
Yes. The framework helps you structure and communicate compliance rigor, whether or not you pursue certification. Many teams use it to strengthen internal audit readiness and funding narratives.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help in conversations with investors or private credit teams?
Yes. The course shows how to frame AI security work in terms investors and lenders recognize, directly addressing trends like those highlighted in the BlackRock analysis.
$199 one-time. 90 minutes of focused reading, structured to fit in a single Sunday morning.
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours