A tailored course, built for your situation
Mastering ISO 27001 for Data Practitioners in High-Growth Tech
A structured path to total command of information security frameworks for data professionals operating at scale.
Who this is for
Senior data practitioner at a high-growth technology company operating under regulatory and scalability scrutiny, needing to demonstrate rigorous, auditable control design within complex data environments.
Who this is not for
Entry-level analysts, isolated compliance officers, or practitioners working in legacy-regulated industries without data velocity pressure.
What you walk away with
- Map data pipeline architecture to ISO 27001 control clauses with confidence
- Produce evidence packages that pass internal validation on first submission
- Structure a Statement of Applicability (SoA) specific to data infrastructure
- Differentiate security design from checklist compliance in stakeholder discussions
- Build a reusable control-validation workflow for new data systems
The 12 modules (with all 144 chapters)
- How data sprawl triggers ISO 27001 scoping decisions
- The link between data pipeline design and control applicability
- Case study: Data team at global SaaS firm passes ISO audit
- Why security frameworks now elevate data practitioners
- How compliance rigor increases autonomy for data teams
- Understanding the shift from ad-hoc to structured control design
- The role of data professionals in certification readiness
- How ISO 27001 aligns with internal audit timelines
- Mapping data ownership to control accountability
- Building credibility through documented control reasoning
- Why data engineers are now first-line compliance assets
- From reactive fixes to proactive control design
- Structure of ISO 27001: Overview and applicability
- Clause 4: Context of the organization in data environments
- Clause 5: Leadership roles in data governance
- Clause 6: Risk assessment for data platforms
- Clause 7: Support mechanisms for data teams
- Clause 8: Operational planning and control mapping
- Annex A control set explained by data use case
- How control 5.10 applies to data access policies
- Control 8.12 and its impact on data classification
- Control 10.1: Logging and monitoring for pipelines
- Control 12.4: Data retention and deletion workflows
- Control 13.2: Secure data transfer in distributed systems
- Defining asset boundaries in distributed data systems
- Identifying data owners in multi-team environments
- Classifying data by sensitivity and regulatory impact
- Threat modeling for ETL pipelines and data lakes
- Mapping data flow diagrams to risk scenarios
- Using DORA and NIS2 as risk context inputs
- Documenting risk ownership for audit readiness
- Prioritizing risks using ISO-defined methodology
- Linking risk treatment to control selection
- Working with legal and compliance stakeholders
- Avoiding over-scoping during risk identification
- Common pitfalls in data risk documentation
- Purpose and structure of the SoA document
- Justifying control inclusion and exclusion
- Using data architecture diagrams in SoA support
- Writing rationale for omitted controls
- Referencing technical documentation in the SoA
- Linking controls to data pipeline components
- Versioning and maintaining the SoA
- Review cycles with internal audit teams
- Common gaps found in data-focused SoAs
- Using automation to maintain SoA accuracy
- SoA formatting conventions for auditor acceptance
- How senior data engineers use the SoA as leverage
- Control A.5.15: Secure development policy for data code
- A.5.36: User access management for Snowflake and Databricks
- A.6.12: Separation of duties in data platform access
- A.8.10: Encryption for data at rest and in transit
- A.8.20: Logging of data access and transformation jobs
- A.8.28: Backup and recovery for critical datasets
- A.9.1: Access control policy for data products
- A.9.4: Role-based access in data teams
- A.10.1: Cryptographic control for PII handling
- A.12.2: Malware protection for data processing systems
- A.12.7: Logging for data pipeline failures
- A.14.1: Secure system engineering for ETL tools
- What auditors expect from data teams
- Using logs from Airflow, Spark, and dbt
- Exporting access control lists from IAM systems
- Documenting data classification rules
- Proving data retention and deletion enforcement
- Screenshots and exports as compliance evidence
- Automating evidence collection with scripts
- Timestamping and chain of custody for data logs
- Linking evidence to control mapping worksheets
- Handling evidence gaps without panic
- Common evidence failures in data audits
- How to prepare evidence in advance of audit cycles
- Scope definition for data-specific security policy
- Defining data ownership and stewardship roles
- Access control policy for analytics and ML teams
- Secure coding standards for data pipeline development
- Data classification policy and labeling conventions
- Acceptable use of data platforms and tools
- Incident response procedures for data leaks
- Policy versioning and approval workflows
- How to align policy with engineering culture
- Using policy as onboarding documentation
- Linking policy to control implementation
- Avoiding policy bloat in fast-moving teams
- Classifying SaaS vendors by data sensitivity
- Reviewing SOC 2 reports for data platform vendors
- Assessing ISO 27001 certification of cloud providers
- Vendor risk questionnaires for data tools
- Mapping vendor controls to internal gaps
- Contractual security clauses for data vendors
- Ongoing monitoring of vendor compliance status
- Managing shadow data tools across teams
- Documenting vendor risk treatment decisions
- Using CSA STAR as a supplemental benchmark
- Handling data processing agreements (DPAs)
- When to escalate vendor risk to security team
- Defining incident thresholds for data systems
- Building a data breach detection workflow
- Roles and responsibilities during data incidents
- Communication plan for internal and external parties
- Preserving forensic data from pipelines and logs
- Reporting obligations under GDPR and CCPA
- Linking response steps to ISO 27001 control A.16
- Post-incident review and control updates
- Simulating data breach scenarios
- Documentation expectations for auditors
- Minimizing business disruption during response
- When to involve legal and PR teams
- Understanding auditor objectives and timelines
- Preparing introductory briefings for data teams
- Anticipating common auditor questions
- Presenting control evidence effectively
- Handling follow-up requests gracefully
- Translating technical details into audit language
- When to escalate issues to compliance leads
- Using auditor feedback to improve workflows
- Building a positive auditor relationship
- Responding to findings without defensiveness
- Common data-related audit findings
- Turning audit prep into routine documentation
- Policy as code for data access controls
- Automated classification using NLP and metadata
- Infrastructure as code for compliant data environments
- Using Open Policy Agent for data governance
- Automated SoA updates from architecture repos
- Scheduled evidence collection pipelines
- Alerting on control drift in data systems
- Integrating compliance checks into CI/CD
- Using APIs to pull vendor compliance data
- Building dashboards for control health
- Reducing audit prep time through automation
- Scaling compliance across growing data teams
- Change management for control updates
- Onboarding new engineers to compliance practices
- Updating documentation after system changes
- Handling acquisitions and data integration
- Re-scoping after major architecture shifts
- Managing control debt in fast-moving teams
- Keeping leadership informed of compliance status
- Using metrics to demonstrate compliance health
- Avoiding compliance fatigue in data teams
- Planning for recertification cycles
- Succession planning for compliance ownership
- Turning compliance into a career accelerator
How this maps to your situation
- Data governance under scale pressure
- Compliance as competitive advantage
- Cross-functional influence without authority
- Technical depth as career leverage
Before vs. after
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 per week over 8 weeks, or intensive 12-hour weekend completion.
How this compares to the alternatives
Most ISO 27001 courses target security generalists. This course is tailored for data practitioners, using real data stack examples and focusing on practical implementation over theory.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.