Data Security for AI Impact Programme Overview
This programme equips teams to establish secure and efficient data workflows, understand how to implement AI solutions safely, and maximise the impact of AI tools and use cases while reducing risk. It is part of our Data and AI Academies.
Key Skills Gained:
AI risk and governance
AI implementation
Data protection for AI systems
AI incident response
Measuring AI ROI
Democratising AI
Business Outcomes:
Improve data quality and assurance: Enhance overall data governance and establish higher quality data for better data and AI insights.
Increase AI potential: Strengthen data workflows securely to enable improved AI scalability.
Reduce AI risk: Implement AI safely by assessing and evaluating security considerations, maintaining governance standards, and standardising end-user training.
Reduce regulatory risk: Boost organisation-wide ability to navigate, address, and comply with legislative requirements.
Apprenticeship Qualification Achieved: Level 4 Data Protection and Information Governance Practitioner
Duration: 13 month delivery, plus 4 month assessment
Data Security for AI Impact Indicative Curriculum Breakdown
Data readiness for AI impact
Month 1: Data assurance for effective AI
Establishing data quality frameworks
Mapping data ecosystems
Month 2: Data discovery & self-service platforms
Leveraging data catalogues
Enabling self-service access
Month 3: Securing data workflows for AI
Applying security principles
Implementing secure data handling and analysis
Month 4: Driving value within regulated industries
Identifying organisational needs
Creating operational opportunities
Responsible AI solutions and safe usage
Month 5: AI foundations and strategic application
Month 6: Evolving AI law and regulatory compliance
Interpreting AI legislation
Monitoring regulatory developments
Month 7: Sensitive data and classification for AI
Protecting sensitive data for AI
Applying AI data protection tools
Month 8: Secure and impactful AI usage
Identifying data-driven AI opportunities
Securing AI tool usage
AI security & scalability
Month 9: Operationalising AI as core infrastructure
Ensuring business continuity and resilience
Managing AI transformation
Month 10: AI risk and incident management
Mitigating AI risks
Managing AI incidents
Month 11: Enabling AI design and development
Fostering AI collaboration
Optimising infrastructure for AI
Month 12: Distributed AI impact and ROI
Measuring AI impact
Democratising secure AI access
Month 13: End point assessment (EPA) preparation
Gateway & independent preparation for end point assessment (EPA)
Months 14-17
Note: This is an example curriculum, and specific details may vary per cohort.
Data Security for AI Impact Indicative Delivery Model
Monthly delivery model, approx. 26 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.
Structured Learning (~40% - 10 hours/month):
Asynchronous learning (3.5 hours): Online, self-paced content that sets the foundation of skills for the module.
Live workshops (3.5 hours): Instructor-led, interactive learning that dives deeper and reinforces the asynchronous content.
Group coaching (1 hour): Peer support and feedback on project deliverables.
Coach support (2 hours): Includes tutoring, progress reviews, & other individual/group support.
Working in Existing Role (~60% - 16 hours/month):
Project and applied learning (16 hours): Structured and unstructured application of learning to apprentices’ roles.