Explore Tasks
Maintain a data and AI risk register and record risk treatment and acceptance
A risk committee receives a maintained data and AI risk register with treatments, acceptance decisions and incident lessons, prepared by the Data Governance Manager, AI Governance Lead or Privacy Manager, with acceptance decided by the accountable Head or Director; the Data Protection Officer advises and does not own.
4jobs do it
38jobs help with it
32skills it needs
- Data Governance Manager
- Privacy Manager
- Marketing Operations Manager
- AI Governance Lead
- Lead AI & ML Engineer
- Data Protection Officer
- Head of Data & Analytics
- Head of Data Architecture
- Head of Data Governance
- Head of Privacy
- Director of Data & Analytics
- Chief Data & AI Officer
- Head of Engineering
- Engineering Director
- Chief Technology Officer
- Head of Finance
- Head of Procurement
- Finance Director
- Procurement Director
- Chief Financial Officer
- Chief Procurement Officer
- Head of HR
- HR Director
- Chief People Officer
- Head of Marketing
- Marketing Director
- Chief Marketing Officer
- AI Product Manager
- Head of Product
- Product Director
- Chief Product Officer
- Head of Commercial
- Commercial Director
- Chief Commercial Officer
- Head of Operations
- Operations Director
- Chief Operating Officer
- Chief Transformation Officer
- Head of Insight
- Head of Marketing Operations
- Head of Revenue Operations
- Reward Manager
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Assessing data-service continuity and recoverability
- Classifying AI uses against regulatory obligations
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Analysing error patterns and failure modes in models and AI systems
- Monitoring model fitness and drift
- Evaluating responsible AI use risks
- Recognising personal data and privacy duties
- Assessing privacy risk through data protection impact assessments
- Recognising cybersecurity threats to data work
- Recognising and containing suspected personal data breaches
- Assessing data-driven fairness and bias
- Assessing supplier dependency, data-handling and exit risk
- Explaining control types, dependencies and compensating controls
- Rating risks against appetite and choosing proportionate treatment
- Exercising human oversight of automated and AI outputs
- Applying data classification and handling rules
- Running incident command: severity, timeline and status updates
- Recognising statutory incident reporting duties and deadlines
- Preserving incident evidence and timeline
Paths that teach the skills it needs
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
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
Responsible AI Governance and Assurance
Govern and assure AI across its lifecycle including third-party AI and redress.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonApplied AI at Work