Data Integrity in ISO 27001

Description

What does the Data Integrity in ISO 27001 course cover?

Data Integrity in ISO 27001 is covered here in 10 modules: Defining Data Integrity Objectives within ISMS Frameworks, Data Classification and Handling Procedures, Access Control and Identity Governance and 7 more. The outline lists 80 specific topics, opening with selecting data classification criteria based on regulatory impact, business criticality, and data lifecycle stage and closing with updating response playbooks based on lessons.

How do you approach Data Integrity in ISO 27001 step by step?

The work is sequenced in 10 stages. It starts with Defining Data Integrity Objectives within ISMS Frameworks, moves through Data Classification and Handling Procedures and Access Control and Identity Governance, and ends at Incident Response and Data Forensics. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Integrity in ISO 27001 course?

Module 1 is Defining Data Integrity Objectives within ISMS Frameworks. It works through selecting data classification criteria based on regulatory impact, business criticality, and data lifecycle stage, mapping data integrity requirements from ISO 27001 Annex A controls (e.g., A.8.10, A.12.4) to specific business processes, establishing ownership models for data sets across departments to ensure accountability and 5 more.

How is the Data Integrity in ISO 27001 course delivered?

The Data Integrity in ISO 27001 course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data Integrity in ISO 27001 course cost?

The Data Integrity in ISO 27001 course is $349 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the equivalent of a multi-workshop technical advisory engagement, covering the design, implementation, and operational enforcement of data integrity controls across ISMS processes, identity governance, system configurations, third-party interactions, and incident response workflows.

Module 1: Defining Data Integrity Objectives within ISMS Frameworks

Selecting data classification criteria based on regulatory impact, business criticality, and data lifecycle stage
Mapping data integrity requirements from ISO 27001 Annex A controls (e.g., A.8.10, A.12.4) to specific business processes
Establishing ownership models for data sets across departments to ensure accountability
Integrating data integrity into risk assessment methodologies used during ISMS implementation
Aligning data integrity policies with existing information security policies without creating redundancy
Documenting integrity baselines for structured vs. unstructured data in policy artifacts
Defining acceptable thresholds for data drift or inconsistency in operational systems
Conducting gap analysis between current data handling practices and ISO 27001 control expectations

Module 2: Data Classification and Handling Procedures

Implementing automated tagging for sensitive data using DLP tools integrated with classification schemas
Configuring access control policies based on data classification levels in IAM systems
Designing handling rules for data in transit, at rest, and in use according to classification
Enforcing encryption standards for high-integrity data categories across storage platforms
Developing procedures for secure printing, downloading, and sharing of classified data
Creating data labeling workflows that support both human readability and machine parsing
Updating classification rules in response to changes in regulatory scope (e.g., GDPR, HIPAA)
Validating classification accuracy through periodic data sampling and audit

Module 3: Access Control and Identity Governance

Implementing role-based access control (RBAC) aligned with data integrity sensitivity tiers
Enforcing least privilege access through regular access review cycles and attestation
Integrating identity lifecycle management with HR systems to automate provisioning/deprovisioning
Configuring privileged access management (PAM) for administrative operations on critical data stores
Applying time-bound access grants for contractors working with high-integrity systems
Logging and monitoring access to data integrity-critical systems using SIEM integration
Defining segregation of duties (SoD) rules to prevent unauthorized data modification
Responding to access anomalies detected through behavioral analytics in identity systems

Module 4: Secure Configuration and System Hardening

Applying CIS benchmarks to harden database servers hosting integrity-sensitive data
Disabling unnecessary services and ports on systems that process or store critical data
Implementing configuration baselines for cloud databases (e.g., AWS RDS, Azure SQL) to prevent misconfigurations
Using infrastructure-as-code (IaC) templates to enforce consistent, auditable system configurations
Validating configuration compliance through automated scanning tools (e.g., Ansible, Chef InSpec)
Managing cryptographic key storage and access in alignment with system security policies
Configuring database audit logging to capture all DDL and DML operations on core tables
Enforcing secure boot and firmware integrity checks on physical servers handling regulated data

Module 5: Change Management and Configuration Control

Requiring formal change requests for any modification to systems storing or processing high-integrity data
Implementing peer review and approval workflows for database schema changes
Using version-controlled repositories for all configuration and database migration scripts
Conducting impact assessments on data integrity before approving infrastructure changes
Enforcing maintenance windows for changes to minimize disruption to data processes
Rolling back unauthorized or failed changes using automated recovery procedures
Integrating change management systems (e.g., ServiceNow) with monitoring tools to detect out-of-band changes
Documenting all changes with rationale, testing results, and backout plans in audit logs

Module 6: Logging, Monitoring, and Anomaly Detection

Centralizing logs from databases, applications, and access systems into a secured SIEM platform
Defining log retention periods based on regulatory requirements and forensic needs
Creating correlation rules to detect suspicious data modification patterns (e.g., bulk deletions)
Ensuring log integrity through write-once storage and cryptographic hashing mechanisms
Monitoring for unauthorized exports or transfers of sensitive data using DLP alerts
Validating that logging is enabled and functioning across all critical data systems
Responding to alerts indicating tampering with audit trails or log disabling attempts
Conducting periodic log reviews as part of internal audit and compliance testing

Module 7: Backup, Recovery, and Data Resilience

Defining recovery point objectives (RPO) and recovery time objectives (RTO) for critical data sets
Encrypting backup data both in transit and at rest using FIPS-validated modules
Validating backup integrity through periodic restore testing in isolated environments
Storing backups in geographically separate locations to mitigate site-level risks
Implementing immutable or write-once storage for backups to prevent tampering
Documenting and testing data recovery procedures as part of business continuity planning
Ensuring backup systems are included in access control and monitoring frameworks
Reviewing backup logs for signs of unauthorized access or deletion attempts

Module 8: Third-Party and Supply Chain Data Governance

Requiring third-party vendors to demonstrate compliance with data integrity controls via audits or certifications
Including data integrity clauses in contracts and SLAs with cloud service providers
Conducting technical assessments of vendor systems that process or store organizational data
Implementing API-level controls to validate data integrity during exchanges with partners
Monitoring data flows to and from third parties using network traffic analysis tools
Requiring encryption and access logging for all data shared externally
Establishing breach notification timelines and data remediation responsibilities in vendor agreements
Conducting periodic reassessments of third-party control effectiveness

Module 9: Audit, Continuous Monitoring, and Improvement

Planning internal audit schedules focused on data integrity control effectiveness
Executing technical audits of database configurations, access logs, and change records
Using automated compliance tools to assess adherence to ISO 27001 control objectives
Documenting non-conformities and tracking remediation through issue management systems
Integrating audit findings into management review meetings for ISMS improvement
Updating data integrity policies based on audit results and evolving threats
Measuring control performance using KPIs such as mean time to detect tampering or log coverage
Aligning internal audit scope with external certification audit requirements for ISO 27001

Module 10: Incident Response and Data Forensics

Defining incident response procedures specific to data corruption or unauthorized modification
Preserving forensic evidence from databases, logs, and file systems after suspected tampering
Engaging qualified forensic analysts to validate data integrity during investigations
Using cryptographic hashes to verify data authenticity before and after incidents
Coordinating communication with legal, compliance, and regulatory bodies during data incidents
Restoring data from verified backups while maintaining chain of custody
Conducting root cause analysis to prevent recurrence of data integrity breaches
Updating response playbooks based on lessons learned from real incidents