Data Governance and Protection Strategy Training Course

Participants can identify significant data assets and determine their business and regulatory criticality.

• Data asset inventories capture systems, datasets, reports, documents, and interfaces
• Critical data elements support financial reporting, risk decisions, and regulatory submissions
• Business impact analysis ranks data according to service and reporting dependencies
• Materiality thresholds identify data defects requiring management escalation
• Data domains organise customer, product, transaction, finance, and supplier information
• System-of-record decisions prevent conflicting master data and duplicate ownership
• Dependency maps show upstream sources, transformations, reports, and consuming processes

Participants can create a data classification scheme and matching handling requirements.

• Classification criteria combine confidentiality, integrity, availability, and legal sensitivity
• Four-tier classification models distinguish public, internal, confidential, and restricted data
• Personal data categories identify direct identifiers, indirect identifiers, and special categories
• Handling rules specify storage, transmission, printing, sharing, and disposal requirements
• Labelling conventions make sensitivity visible in documents, datasets, and repositories
• Data classification exceptions require documented approval, expiry dates, and compensating controls
• Sampling techniques validate classification accuracy across representative data populations

Participants can specify metadata and lineage records that support control, traceability and reporting.

• Business glossaries establish approved definitions for financial and risk metrics
• Metadata standards record owners, sources, transformations, classifications, and retention periods
• Data catalogues provide searchable records of approved datasets and reports
• End-to-end lineage traces data from source capture through report publication
• Technical lineage distinguishes extraction, transformation, loading, aggregation, and reconciliation stages
• Key report fields mapped to source systems and accountable data owners
• Change records preserve version history for definitions, rules, and transformations

Participants can define data quality dimensions, rules, thresholds and remediation processes.

• Data quality dimensions measure accuracy, completeness, timeliness, validity, and uniqueness
• Critical data element rules specify permitted values, formats, tolerances, and dependencies
• Reconciliation controls compare source totals, ledger balances, and regulatory report outputs
• Profiling identifies null values, duplicates, outliers, invalid codes, and inconsistent formats
• Quality scorecards report rule results, trends, breaches, and remediation status
• Root cause analysis separates process defects, system defects, and human errors
• Issue workflows assign owners, target dates, validation evidence, and closure criteria

Participants can assess data risks and document proportionate treatment actions in a risk register.

• Risk statements describe cause, event, impact, and affected data assets
• Inherent risk scoring assesses likelihood and impact before control consideration
• Control effectiveness ratings distinguish design adequacy from operating effectiveness
• Residual risk ratings inform treatment priorities and management acceptance decisions
• Risk registers record ownership, actions, due dates, dependencies, and status
• Scenario analysis estimates consequences of corrupted reporting or exposed customer data
• Key risk indicators track deteriorating quality, access, retention, and incident trends

Participants can establish governance controls for lawful, transparent and accountable personal data processing.

• Lawful basis assessments document justification for each personal data processing purpose
• Purpose limitation prevents secondary uses without documented legal and governance review
• Data minimisation tests challenge unnecessary collection, fields, recipients, and retention
• Privacy notices align stated purposes with actual processing activities and disclosures
• Records of processing activities capture purposes, categories, transfers, and safeguards
• Data subject rights workflows address access, correction, deletion, objection, and portability
• Privacy accountability evidence links policies, training, assessments, controls, and oversight

Participants can conduct a privacy impact assessment and determine appropriate risk controls.

• Privacy impact assessment triggers identify high-risk processing and material changes
• Processing descriptions document data flows, purposes, populations, recipients, and technologies
• Necessity and proportionality tests assess whether processing meets stated objectives
• Privacy threats include unauthorised disclosure, reidentification, misuse, and exclusion
• Risk evaluation considers affected individuals, scale, sensitivity, and foreseeable harms
• Mitigation plans specify controls, accountable owners, deadlines, and residual risks
• Approval gates require privacy review before implementation or material process changes

Participants can translate data protection requirements into preventive, detective and corrective controls.

• CIA triad connects confidentiality, integrity, and availability to control objectives
• Control design addresses threats, vulnerabilities, attack paths, and business consequences
• Preventive controls restrict unauthorised access, alteration, extraction, and disclosure
• Detective controls identify anomalous access, failed transfers, and integrity exceptions
• Corrective controls restore approved data, services, permissions, and configuration baselines
• Control matrices link risks, objectives, activities, evidence, frequency, and ownership
• ISO 27001 control selection documents applicability, exclusions, and implementation rationale

Participants can design role-based access controls and review access risk systematically.

• Role-based access control assigns permissions through approved business roles
• Least privilege limits users to data and functions genuinely required
• Segregation of duties prevents incompatible creation, approval, release, and reconciliation activities
• Joiner-mover-leaver procedures maintain timely access provisioning, amendment, and removal
• Privileged access management protects administrator accounts, elevated permissions, and session records
• Periodic access reviews validate current need, approval evidence, and role suitability
• Access recertification exceptions require documented risk acceptance and compensating controls

Participants can specify appropriate cryptographic and transfer controls for sensitive data.

• Encryption at rest protects data stored in databases, files, backups, and devices
• Transport Layer Security protects data transmitted through applications, portals, and interfaces
• Key management defines generation, storage, rotation, revocation, and recovery procedures
• Tokenisation substitutes sensitive values while preserving controlled processing capability
• Data masking protects non-production environments used for testing and training
• Secure file transfer requirements address authentication, encryption, integrity, and receipt confirmation
• Cryptographic control evidence includes configurations, key inventories, logs, and approvals

Participants can establish retention schedules and disposal controls that meet business and legal needs.

• Retention schedules align record categories with legal, regulatory, and business requirements
• Event-based retention starts from account closure, contract expiry, or transaction completion
• Legal hold procedures suspend deletion during litigation, investigation, or regulatory review
• Archive controls preserve readability, integrity, access restrictions, and retrieval evidence
• Defensible disposal documents authorised deletion, destruction method, scope, and completion
• Backup retention rules address recovery needs without creating unmanaged long-term copies
• Records owners review retention periods after regulatory change or process redesign

Participants can assess and control data risks arising from suppliers, processors and cloud providers.

• Vendor due diligence assesses security, privacy, resilience, governance, and subcontracting arrangements
• Data processing agreements define instructions, confidentiality, breach notification, and audit rights
• Supplier data inventories identify shared datasets, purposes, locations, and receiving systems
• Cross-border transfer assessments document destination risks, safeguards, and contractual protections
• Service-level agreements specify availability, recovery, incident response, and reporting commitments
• Third-party control testing reviews independent assurance reports, evidence, and remediation actions
• Exit plans address data return, secure deletion, transition support, and retained copies

Participants can apply governance and protection requirements to cloud data services and analytics platforms.

• Shared responsibility models separate provider responsibilities from customer control obligations
• Cloud data inventories record accounts, regions, services, owners, and classifications
• Configuration baselines control storage permissions, logging, encryption, and public exposure
• Data residency requirements assess locations, replication, support access, and legal constraints
• Analytics governance controls model inputs, approved uses, outputs, and retention
• Application programming interface controls govern authentication, authorisation, throttling, and audit logging
• Cloud monitoring identifies exposed storage, excessive permissions, and unusual data transfers

Participants can coordinate a data incident response process from detection through lessons learned.

• Incident classification distinguishes confidentiality, integrity, availability, and privacy events
• Response playbooks assign containment, investigation, communication, recovery, and evidence responsibilities
• Triage procedures establish affected data, systems, individuals, jurisdictions, and severity
• Forensic preservation protects logs, records, images, and chain-of-custody documentation
• Breach assessment determines notification obligations, deadlines, audiences, and approval routes
• Stakeholder communications balance transparency, factual accuracy, confidentiality, and legal requirements
• Post-incident reviews identify control failures, root causes, actions, and accountable owners

Participants can create management information that demonstrates governance performance and emerging data risk.

• Key performance indicators measure ownership coverage, quality performance, and remediation timeliness
• Key risk indicators identify unresolved critical issues, access exceptions, and overdue reviews
• Dashboard design separates operational detail from executive decision information
• Thresholds and tolerances trigger escalation before breaches become material incidents
• Control attestations capture owner confirmation, exceptions, evidence, and corrective actions
• Trend analysis distinguishes isolated failures from persistent systemic weaknesses
• Management reports link data risks to financial, regulatory, customer, and operational consequences

Participants can prepare evidence and test data governance controls for internal and external assurance.

• Control objectives define intended outcomes before selecting testing procedures
• Walkthroughs trace a transaction, dataset, or report through relevant controls
• Design effectiveness testing assesses whether controls address identified data risks
• Operating effectiveness testing samples evidence across defined review periods
• Population completeness validates that testing samples represent relevant control activity
• Audit evidence includes approvals, system logs, reports, tickets, and reconciliations
• Findings ratings distinguish observations, control deficiencies, and material control failures

Participants can build a phased implementation plan for governance, protection and assurance improvements.

• Current-state assessments compare existing practices against target governance requirements
• Maturity models assess policy, ownership, metadata, quality, protection, and assurance capabilities
• Gap analyses prioritise weaknesses by risk exposure, feasibility, cost, and business dependency
• Target operating models define governance forums, roles, service levels, and escalation
• Roadmaps sequence quick wins, foundational controls, platform changes, and assurance activities
• Resource plans identify sponsor, programme lead, owners, stewards, and control specialists
• Change management plans address communications, training, adoption measures, and resistance

Participants can build, review and present a complete data governance and protection strategy for a defined business case.

• Business case defines priority data domains, strategic objectives, and material risk exposures
• Governance operating model assigns accountable executives, owners, stewards, and escalation forums
• Data inventory identifies critical elements, classifications, lineage, retention, and dependencies
• Risk register evaluates privacy, quality, access, supplier, and resilience exposures
• Control matrix specifies preventive, detective, corrective, and assurance control requirements
• Implementation roadmap sequences priorities, resources, milestones, measures, and governance decisions
• Executive presentation defends recommendations, residual risks, investment needs, and expected outcomes