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SEC5763 Mastering ISO 27001 for Data and AI Architects

$199.00
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A tailored course, built for your situation

Mastering ISO 27001 for Data and AI Architects

Build defensible, auditable information security frameworks that stand up to scrutiny the first time, no rework, no last-minute fixes.

$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.
Audit delays due to inconsistent control documentation

The situation this course is for

Teams spend excessive time reconciling control evidence because mappings lack clarity, traceability, or consistency, especially when AI systems interact with regulated data. This leads to last-minute revisions, stakeholder friction, and fragile narratives under review.

Who this is for

Senior data and AI practitioners in regulated consulting environments who own or contribute to compliance-critical architecture and documentation.

Who this is not for

Entry-level analysts, developers without governance ownership, or professionals outside data/AI/security compliance tracks.

What you walk away with

  • Produce ISO 27001-compliant control mappings that pass internal review the first time
  • Design repeatable templates for SoA and evidence collection aligned to AI and data workflows
  • Reduce audit preparation cycles by standardizing documentation across projects
  • Gain confidence in articulating control rationale with regulator-grade precision
  • Automate consistency checks for control implementation across environments

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 27001's Core Structure and Annex A Controls
Lay the foundation by exploring the intent, scope, and implementation logic of ISO 27001’s control set, with a focus on relevance to data and AI systems.
12 chapters in this module
  1. Introduction to ISO 27001 and its role in modern data environments
  2. Structure of the standard: Clauses 4, 10 explained
  3. Purpose and scope of Annex A controls
  4. Mapping Annex A to data lifecycle stages
  5. How AI systems expand the traditional attack surface
  6. Control selection criteria for hybrid cloud deployments
  7. Integrating ISO 27001 with NIST CSF and SOC 2
  8. Common misconceptions about scope and applicability
  9. Documenting 'Statement of Applicability' rationale
  10. Role of risk assessment in control tailoring
  11. Understanding control objectives vs. implementation depth
  12. Practitioner checklist for initial gap assessment
Module 2. Defining Scope and Context for Data and AI Projects
Learn how to accurately define the boundaries of an ISMS when working with multi-source datasets and AI-driven analytics platforms.
12 chapters in this module
  1. Identifying information assets in distributed environments
  2. Scoping considerations for multi-tenant cloud architectures
  3. Delineating responsibilities between client and provider
  4. Handling third-party data processors in AI workflows
  5. Assessing data classification levels across sources
  6. Documenting asset ownership and stewardship
  7. Boundary definition for serverless and containerized workloads
  8. Integrating data lineage into scope documentation
  9. Common pitfalls in over-scoping or under-scoping
  10. Template for scope justification narratives
  11. Stakeholder alignment on scope clarity
  12. Review cycle integration for scope updates
Module 3. Conducting Risk Assessments for AI and Data Systems
Apply ISO 27001-aligned risk methodology to identify, analyze, and prioritize threats specific to data pipelines and AI models.
12 chapters in this module
  1. Risk assessment methodology per ISO 27001 Clause 6.1.2
  2. Threat modeling for data ingestion and transformation
  3. Vulnerability identification in machine learning pipelines
  4. Likelihood and impact scoring frameworks
  5. Mapping risks to Annex A controls
  6. Documenting risk treatment decisions
  7. Handling residual risk acceptance
  8. Risk register structure and maintenance
  9. Incorporating GDPR and data privacy risks
  10. Scenario: AI drift detection as a security risk
  11. Third-party model risk considerations
  12. Automation opportunities for risk tracking
Module 4. Developing a Statement of Applicability (SoA)
Craft a defensible, evidence-ready SoA tailored to data and AI implementations, with clear rationale for each control decision.
12 chapters in this module
  1. Purpose and structure of the SoA document
  2. Mandatory fields required for auditor review
  3. Justifying inclusion of each Annex A control
  4. Documenting control exclusions with valid rationale
  5. Linking SoA entries to risk assessment findings
  6. Version control and change tracking for SoA updates
  7. SoA templates for cloud-native deployments
  8. Common mistakes in SoA drafting
  9. Integrating SoA with control implementation tracking
  10. Using SoA to guide evidence collection
  11. Cross-referencing SoA with compliance frameworks
  12. Review checklist for SoA completeness
Module 5. Designing Access Control Policies for Data Workflows
Build role-based access control frameworks that align with data sensitivity and AI model governance needs.
12 chapters in this module
  1. Principles of least privilege in data environments
  2. User access review cycles and documentation
  3. Segregation of duties for data engineering roles
  4. Authentication mechanisms for API and service accounts
  5. Managing access in multi-cloud environments
  6. Temporary privilege escalation controls
  7. Access logging and monitoring requirements
  8. Handling access revocation for offboarding
  9. Policy templates for data analysts and scientists
  10. Integrating with identity providers like Azure AD
  11. Automated access certification workflows
  12. Audit trail expectations for access changes
Module 6. Securing Data in Transit and at Rest for AI Pipelines
Implement encryption and data protection strategies across the data lifecycle, from ingestion to inference.
12 chapters in this module
  1. Encryption standards for structured and unstructured data
  2. Key management best practices for cloud environments
  3. TLS enforcement across data transfer points
  4. Data masking and anonymization techniques
  5. Secure storage configurations in S3, ADLS, etc.
  6. Handling model weights and training data security
  7. Endpoint protection for data science workstations
  8. Secure model deployment pipelines
  9. Data retention and destruction policies
  10. Encryption validation testing procedures
  11. Compliance requirements for cross-border data flows
  12. Documenting encryption architecture decisions
Module 7. Establishing Incident Response for Data and AI Systems
Develop incident response plans tailored to data breaches, model poisoning, and unauthorized access attempts.
12 chapters in this module
  1. Incident classification framework for data events
  2. Roles and responsibilities in breach scenarios
  3. Detection mechanisms for AI model anomalies
  4. Logging and monitoring requirements for forensics
  5. Notification procedures for data subjects and regulators
  6. Coordination with legal and compliance teams
  7. Post-incident evidence preservation
  8. Root cause analysis methodology
  9. Incident response plan template customization
  10. Tabletop exercise design for team readiness
  11. Integration with SOCs and external partners
  12. Continuous improvement of response playbooks
Module 8. Managing Third-Party and Vendor Risk in AI Deployments
Evaluate and monitor third-party vendors, APIs, and open-source tools used in data and AI systems.
12 chapters in this module
  1. Vendor onboarding risk assessment process
  2. Due diligence for AI platform providers
  3. Reviewing SOC 2 and ISO 27001 reports from vendors
  4. Contractual security requirements for data handling
  5. Ongoing monitoring of vendor compliance status
  6. Managing open-source model and library risks
  7. Vendor offboarding and data deletion verification
  8. Assessing supply chain risks in model deployment
  9. Using SIG and CAIQ questionnaires effectively
  10. Documenting third-party risk acceptance
  11. Multi-cloud vendor coordination challenges
  12. Vendor audit rights and evidence collection
Module 9. Building Audit-Ready Documentation Packages
Create structured, consistent documentation that withstands internal and external scrutiny without rework.
12 chapters in this module
  1. Core documents required for ISO 27001 audits
  2. Document hierarchy and version control
  3. Evidence collection planning by control
  4. Standardizing narrative descriptions across teams
  5. Template library for policies, procedures, and logs
  6. Linking evidence to SoA entries
  7. Preparing for auditor interviews
  8. Common auditor questions and responses
  9. Remote audit readiness and access provisioning
  10. Handling requests for additional evidence
  11. Final pre-audit quality check process
  12. Post-audit action item tracking
Module 10. Implementing Continuous Monitoring and Improvement
Design ongoing controls validation and metrics tracking to ensure sustained compliance and detect drift.
12 chapters in this module
  1. Key performance indicators for security controls
  2. Automated control validation scripts
  3. Log analysis for anomaly detection
  4. Security dashboard design for operational teams
  5. Change management integration with ISMS
  6. Periodic review schedules for policies and controls
  7. Internal audit process design
  8. Lessons learned from past incidents and reviews
  9. Updating risk assessments based on new threats
  10. Feedback loops with data and AI engineering teams
  11. Metrics for tracking improvement over time
  12. Integration with DevSecOps pipelines
Module 11. Integrating ISO 27001 with AI Governance Frameworks
Align information security controls with emerging AI governance and model oversight requirements.
12 chapters in this module
  1. Mapping ISO 27001 to AI risk domains
  2. Securing model training data and artifacts
  3. Access controls for model deployment environments
  4. Model versioning and integrity verification
  5. Monitoring for adversarial attacks and drift
  6. Documentation requirements for AI audits
  7. Ethical review integration with security processes
  8. Handling model explainability data securely
  9. Vendor risk for AI-as-a-service platforms
  10. Data provenance in AI decision-making
  11. Incident response for AI-specific failures
  12. Cross-functional governance committee engagement
Module 12. Sustaining Certification Across Audit Cycles
Maintain ISO 27001 certification through consistent documentation, stakeholder engagement, and improvement.
12 chapters in this module
  1. Annual audit preparation timeline
  2. Internal audit coordination process
  3. Managing auditor access and requests
  4. Corrective action tracking system
  5. Updating policies based on audit findings
  6. Stakeholder communication strategy
  7. Training refresh cycles for new hires
  8. Handling organizational changes and restructuring
  9. Re-certification audit expectations
  10. Cost-benefit analysis of extended certification
  11. Knowledge transfer across team members
  12. Preparing for surprise audits and regulatory inquiries

How this maps to your situation

  • Data engineering teams delivering governed datasets for analytics
  • AI architects building secure, auditable machine learning systems
  • Compliance leads needing consistent evidence across client engagements
  • Consulting practitioners preparing for ISO 27001 audits at client sites

Before vs. after

Before
Spending weeks pulling together inconsistent policy documents and control mappings under time pressure before audits.
After
Delivering clean, coherent, and auditor-approved evidence packages on demand , first time, every time.

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: Approximately 12 hours total, designed for completion in short, focused sessions over a few weeks.

If nothing changes
Without a standardized, quality-first approach to ISO 27001 implementation, teams risk repeated audit findings, increased remediation costs, and reputational exposure , especially when handling sensitive data in AI-driven environments.

How this compares to the alternatives

Unlike generic ISO 27001 training, this course is tailored to data and AI practitioners in consulting roles , focusing on actionable documentation, real-world evidence packaging, and control mapping that survives scrutiny without rework.

Frequently asked

Is this course suitable for someone without prior ISO 27001 experience?
Yes. The course starts with foundational concepts and builds progressively, with clear examples tailored to data and AI roles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive practical templates I can use immediately?
Yes. Every module includes downloadable, customizable templates , including SoA drafts, risk registers, and evidence checklists.
$199 one-time. Approximately 12 hours total, designed for completion in short, focused sessions over a few weeks..

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
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