Data Protection Officer
Accountable for independently advising on and monitoring data protection compliance, including for data, analytics and AI processing.
6tasks in this job
56skills it uses
9tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 6 of 6 tasks
- 2Building data protection by design into data products and pipelinesNeeded in 6 of 6 tasks
ToolsOneTrustMicrosoft PurviewDPOrganizerTrustArcBigIDMicrosoft ExcelSalesforceWorkdayMicrosoft SharePoint
All 6 tasks, where people get stuck and all 56 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Fulfil a data subject rights request with a lawful, redacted response
- Map personal data processing and maintain the record of processing activities
- Set and review the organisation's AI use policy and AI literacy requirements
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Recognising and containing suspected personal data breaches
All skills
- Assessing privacy risk through data protection impact assessments
- Building data protection by design into data products and pipelines
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Applying exemptions and third-party redaction to rights responses
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Classifying AI uses against regulatory obligations
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and running evaluations of AI systems
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing testable data controls
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Finding relevant data sources
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Record-keeping requirements for processing activities and AI systems
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDesigning Controlled Automations and AI-assisted Workflows
8tasks in this job
67skills it uses
11tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 7 of 8 tasks
- 2Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 6 of 8 tasks
- 3Controlling disclosure risk in shared data and outputsNeeded in 6 of 8 tasks
- 4Recognising and containing suspected personal data breachesNeeded in 6 of 8 tasks
- 5Mapping personal data processing and flowsNeeded in 6 of 8 tasks
ToolsOneTrustMicrosoft PurviewGoogle Tag ManagerServiceNowMicrosoft ExcelBigID+ 5 more
All 8 tasks, where people get stuck and all 67 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Triage a suspected data security or personal data breach and preserve evidence
- Test the design and operating effectiveness of a data-dependent control
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Fulfil a data subject rights request with a lawful, redacted response
- Map personal data processing and maintain the record of processing activities
- Set and review the organisation's AI use policy and AI literacy requirements
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Controlling disclosure risk in shared data and outputs
- Mapping personal data processing and flows
- Recognising and containing suspected personal data breaches
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Applying exemptions and third-party redaction to rights responses
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Classifying AI uses against regulatory obligations
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and running evaluations of AI systems
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing testable data controls
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Finding relevant data sources
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Record-keeping requirements for processing activities and AI systems
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- OneTrust
- Microsoft Purview
- Google Tag Manager
- ServiceNow
- Microsoft Excel
- BigID
- TrustArc
- Salesforce
- Workday
- DPOrganizer
- Microsoft SharePoint
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDesigning Controlled Automations and AI-assisted Workflows
9tasks in this job
69skills it uses
10tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 9 of 9 tasks
- 2Judging whether and how to use AI for a taskNeeded in 7 of 9 tasks
- 3Evaluating fitness and full cost of AI-assisted workNeeded in 7 of 9 tasks
- 4Evaluating responsible AI use risksNeeded in 7 of 9 tasks
- 5Building data protection by design into data products and pipelinesNeeded in 6 of 9 tasks
ToolsMicrosoft PurviewGoogle Tag ManagerMicrosoft PowerPointOneTrustServiceNowMicrosoft Power BI+ 4 more
All 9 tasks, where people get stuck and all 69 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Evaluate an AI use case for suitability, value and risk before adoption
- Triage a suspected data security or personal data breach and preserve evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Classify an AI system's risk and assure its documentation, evaluation and oversight evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Set and review the organisation's AI use policy and AI literacy requirements
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Assessing privacy risk through data protection impact assessments
- Building data protection by design into data products and pipelines
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Applying exemptions and third-party redaction to rights responses
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Classifying AI uses against regulatory obligations
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and running evaluations of AI systems
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing testable data controls
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Evaluating and improving chart accessibility
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Finding relevant data sources
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Record-keeping requirements for processing activities and AI systems
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Purview
- Google Tag Manager
- Microsoft PowerPoint
- OneTrust
- ServiceNow
- Microsoft Power BI
- DPOrganizer
- TrustArc
- Microsoft SharePoint
- Microsoft Excel
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonApplied AI at WorkDesigning Controlled Automations and AI-assisted Workflows
About the role
Accountable for independently advising on and monitoring data protection compliance, including for data, analytics and AI processing. Reports directly to the highest management level and takes no instructions on how to carry out these tasks. Does not decide how personal data is used, which sits with the business owners of that data.
Also called Data Privacy Officer, Privacy Officer, DPO