Course Overview
Data Governance, Privacy & Responsible AI is a practical 6-hour instructor-led course designed to help professionals understand how organisations can manage data and Artificial Intelligence responsibly, securely and transparently. Participants will learn the foundations of data governance, including data ownership, quality, classification, access and lifecycle management, before exploring privacy principles, data protection by design and risk assessment. The course then examines Responsible AI topics including fairness, bias, transparency, human oversight, security, accountability and the responsible use of Generative AI.
Through practical exercises and a governance case study, learners will assess a business AI use case, identify data and AI risks, define appropriate controls and create a simple governance framework. The course provides practical regulatory awareness without attempting to provide legal advice.
Requirements
No previous experience with data governance, data protection or Responsible AI is required.
Participants should have basic workplace computer skills and an interest in how organisations collect, use, manage or protect data.
Familiarity with data analysis, Artificial Intelligence, Generative AI, or business processes can help but is not essential.
No programming, statistics or Machine Learning experience is required.
You may also complete the following course(s) before attending the Data Governance, Privacy & Responsible AI course but they are not mandatory:
Course Content
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Introduction to Data & AI Governance
- What Data Governance means
- Why organisations need governance
- Data Governance vs Data Management
- Understanding AI Governance
- Business, regulatory, ethical and reputational risks
- Governance across the data and AI lifecycle
- Roles of business, technology, legal, privacy and security teams
- Understanding accountability and organisational responsibility
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Data Ownership, Quality & Lifecycle Management
- Understanding data ownership and stewardship
- Identifying important organisational data
- Data classification
- Personal, confidential and sensitive information
- Understanding data quality
- Accuracy, completeness, consistency and timeliness
- Managing data throughout its lifecycle
- Collection, use, sharing, retention and deletion
- Understanding data lineage at a practical level
- Creating basic data ownership and governance responsibilities
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Data Privacy & Protection Principles
- Understanding personal data
- Introduction to UK data protection responsibilities
- Lawfulness, fairness and transparency
- Purpose limitation
- Data minimisation
- Accuracy
- Storage limitation
- Integrity and confidentiality
- Accountability
- Understanding individual data rights
- Privacy notices and transparency
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Privacy by Design & Data Protection Risk
- Understanding privacy by design and by default
- Identifying privacy risks early
- Mapping what data is collected and why
- Limiting unnecessary data collection
- Understanding Data Protection Impact Assessments (DPIAs)
- Recognising when higher-risk processing may require additional assessment
- Applying technical and organisational controls
- Privacy considerations when introducing new AI technologies
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Responsible AI Principles & Risks
- What Responsible AI means
- Understanding AI risk in a business context
- Fairness and discrimination
- Bias in data and AI outputs
- Transparency and explainability
- Accuracy and reliability
- Privacy and confidentiality
- Security and misuse
- Human autonomy and oversight
- Accountability for AI-assisted decisions
- Understanding potential harm to individuals and organisations
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Generative AI, Privacy & Workplace Risk
- Understanding risks associated with Generative AI
- Entering personal or confidential information into AI systems
- Understanding hallucinations and inaccurate outputs
- Bias and inappropriate content
- Intellectual property and copyright awareness
- Verifying AI-generated information
- Human review before important decisions
- Creating appropriate workplace AI usage rules
- Understanding vendor and third-party AI risk
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Human Oversight, Transparency & Accountability
- Understanding human-in-the-loop approaches
- Determining when human review is required
- Avoiding inappropriate fully automated decisions
- Defining who is responsible for an AI system
- Maintaining documentation and audit trails
- Making users aware when they interact with AI
- Escalation and human intervention
- Handling complaints and incorrect AI outcomes
- Establishing clear accountability across the AI lifecycle
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AI Risk Assessment & Governance Controls
- Creating an inventory of organisational AI systems
- Classifying AI use cases by potential impact and risk
- Identifying affected users and stakeholders
- Assessing likelihood and severity of potential harm
- Creating an AI risk register
- Choosing appropriate risk controls
- Assigning responsible owners
- Documenting accepted and residual risks
- Monitoring systems after deployment
- Reviewing AI systems when data, models or business use changes
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Current Regulatory & Governance Awareness
- UK GDPR and Data Protection Act principles
- Awareness of recent UK data protection developments
- Introduction to the EU AI Act
- Understanding risk-based AI regulation
- Transparency requirements for certain AI systems
- AI literacy and organisational responsibility
- Introduction to Responsible AI frameworks
- Understanding why requirements vary by jurisdiction and industry
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Practical Data & AI Governance Project
- Review a proposed business AI use case
- Identify the data being collected and processed
- Identify owners and stakeholders
- Classify sensitive and personal information
- Identify privacy and AI risks
- Assess fairness, transparency and human oversight
- Create a simple risk register
- Recommend governance and security controls
- Define human review and escalation responsibilities
- Create a short Responsible AI governance plan
- Decide whether the proposed AI use should proceed, be modified or require further review
Course Dates, Prices & Enrolment
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