A tailored course, built for your situation
Mastering ISO 27001 for Principal Data Scientists
Build unshakeable command of the ISO 27001 framework for data-centric compliance execution
$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.
Most data scientists rely on compliance teams to interpret ISO 27001, but that creates delays and dilution when audits demand technical clarity.
The situation this course is for
When ISO 27001 requirements hit data systems, generic interpretations fall apart. Without deep framework command, data leaders defer to generalists who don’t grasp model risk, pipeline integrity, or AI governance boundaries, leaving critical controls under-justified or over-engineered.
Who this is for
Principal Data Scientist operating at the intersection of advanced analytics, AI systems, and enterprise compliance , expected to deliver technical rigor under regulatory scrutiny.
Who this is not for
Entry-level data analysts, compliance generalists without technical depth, or managers seeking high-level overviews without implementation detail.
What you walk away with
- Map ISO 27001 controls to data architecture components with precision
- Justify control design using clause-level reasoning and real data system examples
- Produce audit-ready documentation that reflects actual implementation
- Anticipate auditor follow-ups with sourced, defensible rationale
- Lead cross-functional control alignment without dependency on external teams
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27001: Structure and Intent
Break down the standard’s architecture, focusing on clauses most relevant to data systems and AI infrastructure.
12 chapters in this module
- Purpose of ISO 27001
- Annex A vs Clause 4 structure
- Role of risk assessment in control selection
- How certification bodies interpret clause 6 1
- Data scientist’s role in ISMS design
- Linking AI governance to control objectives
- Common misconceptions in technical teams
- Why documentation must reflect technical reality
- Control selection vs implementation depth
- Scope definition for data platforms
- Boundary decisions for cloud environments
- Integrating with model risk frameworks
Module 2. Clause 4: Context of the Organization
Define organizational context with data-specific stakeholders, threat models, and compliance dependencies.
12 chapters in this module
- Identifying data custodians
- Mapping data flows for clause 4 1
- Stakeholder analysis for AI systems
- Regulatory interfaces with FCRA
- Defining data processing boundaries
- Third party data partners
- Internal consumers of data outputs
- Jurisdictional data handling rules
- AI model training data sources
- Data sovereignty constraints
- Cross border transfer risks
- Documentation for clause 4 review
Module 3. Clause 5: Leadership and Commitment
Align leadership obligations with data science governance accountability and executive sponsorship.
12 chapters in this module
- Leadership role in ISMS
- Commitment to data integrity
- Assigning control ownership
- Data scientist as control steward
- Reporting on control performance
- Executive visibility on data risks
- Tone from technical leadership
- Incorporating ethical AI principles
- Budgeting for control maintenance
- Training plans for data teams
- Success metrics for compliance
- Linking to corporate governance
Module 4. Clause 6: Planning for Risk Treatment
Build risk treatment plans using data-specific threats and AI model lifecycle considerations.
12 chapters in this module
- Risk assessment methodology
- Data classification schema
- Model data leakage scenarios
- Adversarial attack vectors
- Bias as a control failure
- Third party model risks
- Supply chain data integrity
- Data poisoning detection
- Output manipulation risks
- Control selection logic
- Risk acceptance thresholds
- Documentation standards
Module 5. Clause 7: Support and Resources
Ensure control sustainability through training, documentation, and internal communication practices.
12 chapters in this module
- Awareness for data teams
- Version control for data policies
- Document storage architecture
- Access control for compliance docs
- Training data scientists on controls
- Internal audit coordination
- Retention policies for model logs
- Metadata tagging standards
- Change management for data systems
- Communication plans
- Resource needs for compliance
- Tools for documentation
Module 6. Clause 8: Operational Control Implementation
Implement controls with data pipeline integrity, model monitoring, and secure development practices.
12 chapters in this module
- Secure AI development
- Model training environment controls
- Data masking in testing
- Access reviews for data sets
- Automated control checks
- Logging for model decisions
- Anomaly detection rules
- Data retention automation
- Model version traceability
- Pipeline change approvals
- Incident response for data
- Third party monitoring
Module 7. Annex A 5: Information Security Policies
Develop and maintain data-specific security policies aligned with ISO 27001 control objectives.
12 chapters in this module
- Policy drafting standards
- AI model governance policy
- Data retention policy
- Acceptable use for datasets
- Policy review cycles
- Stakeholder approval process
- Policy distribution methods
- Version control for policies
- Policy exception handling
- Training on policy updates
- Enforcement mechanisms
- Audit readiness for policies
Module 8. Annex A 6: Organization of Information Security
Structure roles, responsibilities, and onboarding for data and AI security compliance.
12 chapters in this module
- Control ownership model
- Data steward roles
- Model risk management
- Onboarding for data scientists
- Offboarding checks
- Third party oversight
- Vendor data risk assessment
- Joint control reviews
- Escalation paths
- Cross team coordination
- Accountability tracking
- Reporting structure
Module 9. Annex A 7: Human Resource Security
Secure data access through personnel controls tailored to analytics and machine learning teams.
12 chapters in this module
- Pre employment screening
- Role based access for data
- Security training content
- Confidentiality agreements
- Post employment access review
- Behavior monitoring
- Whistleblower mechanisms
- Data misuse detection
- AI ethics training
- Incident reporting
- Termination procedures
- Remote work controls
Module 10. Annex A 8: Asset Management
Classify and manage data assets, models, and infrastructure components under ISO 27001.
12 chapters in this module
- Data inventory creation
- Model registry standards
- Data classification levels
- Ownership assignment
- Storage location tracking
- Data lifecycle stages
- Disposal procedures
- Model retirement process
- Metadata requirements
- Audit trail for access
- Third party asset tracking
- Reclassification workflows
Module 11. Annex A 9: Access Control
Design granular access controls for data platforms and model endpoints.
12 chapters in this module
- Role based access design
- Attribute based access control
- Model endpoint permissions
- Data lake access policies
- Privileged access for data scientists
- Access review frequency
- Segregation of duties
- Just in time access
- Emergency access procedures
- Logging access changes
- Automated access recertification
- Audit trail requirements
Module 12. Annex A 10: Cryptography
Apply encryption standards to data at rest, in transit, and within AI systems.
12 chapters in this module
- Encryption for data lakes
- Model parameter protection
- Key management practices
- Homomorphic encryption use
- Secure model transmission
- Tokenization strategies
- Data masking rules
- Encryption in transit
- Key rotation schedule
- Certificate management
- Quantum risk preparation
- Compliance with NIST standards
How this maps to your situation
- When scoping an ISO 27001 project for a data platform
- Before audit documentation is submitted
- After a control fails in review
- When onboarding a new AI product into compliance scope
Before vs. after
Before
Relying on compliance teams to translate ISO 27001 for data systems, leading to misalignment and audit friction.
After
Directly owning control mapping and justification with confidence in technical accuracy and regulatory intent.
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 3 hours per module, designed for steady progress alongside current workload.
If nothing changes
Continuing to depend on intermediaries increases review cycles, weakens control narratives, and limits influence in cross-functional governance discussions.
How this compares to the alternatives
Unlike generic ISO 27001 courses, this program is built specifically for senior data scientists who must implement controls within AI and complex data environments , not for compliance generalists or auditors.
Frequently asked
Is this course technical enough for a Principal Data Scientist?
Yes. Every module includes data architecture decisions, model governance examples, and implementation-level detail tailored to senior technical roles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover ISO 27001:the current cycle updates?
Yes. All content reflects the ISO 27001:the current cycle revision with data-specific interpretations.
$199 one-time. Approximately 3 hours per module, designed for steady progress alongside current workload..
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