Explore Tasks
Evaluate an AI use case for suitability, value and risk before adoption
A Function lead receives a go, conditional-go or no-go on an AI use case with value estimate, failure modes and data protection advice, using the AI Governance Lead's recorded risk classification and required controls (NEW-S1-06); the DPO advises and the business owner decides adoption.
- AI Engineer
- Privacy Analyst
- Lead Data Scientist
- Lead AI & ML Engineer
- Data Governance Lead
- Data Protection Officer
- Privacy Manager
- Head of Data & Analytics
- Finance Business Partner
- Head of Finance
- Finance Director
- Talent Acquisition Manager
- Head of HR
- HR Director
- Chief People Officer
- Marketing Director
- Chief Marketing Officer
- AI Product Manager
- Group Product Manager
- Head of Product
- Chief Product Officer
- Customer Service Manager
- Head of Insight
- Head of Marketing Operations
- AI Governance Lead
- 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
- Locating and acting on personal data for individual rights requests
- Writing data quality rules
- Enforcing and auditing data access
- Applying data classification and handling rules
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Designing testable data controls
- Controlling disclosure risk in shared data and outputs
- Resolving consent and suppression status across systems
- Mapping personal data processing and flows
- Interpreting policy scope and delegated authority limits
- Building data protection by design into data products and pipelines
- Designing AI-assisted workflows with bounded tools and human authority
- Bounding and securing AI systems' tool and data access
- Designing and running evaluations of AI systems
- Presenting and defending evidence-based recommendations to decision-makers
- Decomposing one's own tasks for AI assistance
- Protecting sensitive data in AI-assisted work
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
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- 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
