Privacy Manager
Accountable for managing a privacy team and for the privacy programme that covers data, analytics and AI processing.
3tasks in this job
52skills it uses
7tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 2 of 3 tasks
- 2Building data protection by design into data products and pipelinesNeeded in 2 of 3 tasks
ToolsServiceNowVaronisMicrosoft PowerPointMicrosoft Power BIOneTrustDPOrganizerTrustArc
All 3 tasks, where people get stuck and all 52 skills
Tasks
- Triage a suspected data security or personal data breach and preserve evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
Where people get stuck
- Writing evidence-led findings and auditable recommendations with their limits
- Presenting and defending evidence-based recommendations to decision-makers
- Testing the completeness and accuracy of reports and data that controls rely on
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
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing supplier dependency, data-handling and exit risk
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Designing testable data controls
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- 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
- Explaining technical work to accountable non-specialists
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- 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
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
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
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
65skills it uses
9tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 6 of 7 tasks
- 2Building data protection by design into data products and pipelinesNeeded in 5 of 7 tasks
- 3Writing evidence-led findings and auditable recommendations with their limitsNeeded in 1 of 7 tasks
- 4Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 7 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsOneTrustServiceNowMicrosoft PowerPointMicrosoft Power BIVaronisMicrosoft Excel+ 3 more
All 7 tasks, where people get stuck and all 65 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
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Fulfil a data subject rights request with a lawful, redacted response
- Map personal data processing and maintain the record of processing activities
Where people get stuck
All skills
- Assessing privacy risk through data protection impact assessments
- Building data protection by design into data products and pipelines
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- 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-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
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Designing testable data controls
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- 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
- Explaining technical work to accountable non-specialists
- Finding relevant data sources
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- 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
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
All tools
- OneTrust
- ServiceNow
- Microsoft PowerPoint
- Microsoft Power BI
- Varonis
- Microsoft Excel
- DPOrganizer
- TrustArc
- BigID
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
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
6tasks in this job
65skills it uses
8tools named
The skills this job leans on most
- 1Assessing privacy risk through data protection impact assessmentsNeeded in 5 of 6 tasks
- 2Building data protection by design into data products and pipelinesNeeded in 4 of 6 tasks
- 3Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 6 tasks
- 4Writing evidence-led findings and auditable recommendations with their limitsNeeded in 1 of 6 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsOneTrustServiceNowMicrosoft Power BIMicrosoft PowerPointVaronisTrustArc+ 2 more
All 6 tasks, where people get stuck and all 65 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Triage a suspected data security or personal data breach and preserve evidence
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Fulfil a data subject rights request with a lawful, redacted response
Where people get stuck
All skills
- Assessing privacy risk through data protection impact assessments
- Building data protection by design into data products and pipelines
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- 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-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
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Designing testable data controls
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- 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
- Explaining technical work to accountable non-specialists
- Finding relevant data sources
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- 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
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
All tools
- OneTrust
- ServiceNow
- Microsoft Power BI
- Microsoft PowerPoint
- Varonis
- TrustArc
- DPOrganizer
- BigID
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
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
About the role
Accountable for managing a privacy team and for the privacy programme that covers data, analytics and AI processing. Plans the team's work, sets standards for assessments and reviews findings before they reach business owners. Does not direct the Data Protection Officer's advice, which stays independent and goes straight to the highest management level.
Also called Data Protection Manager