Generative AI Governance, Risk Management and Responsible AI Deployment in Banking Training Workshop

Course overview

The Generative AI Governance, Risk Management and Responsible AI Deployment in Banking Training Workshop equips banking executives, risk professionals, compliance officers, technology leaders and financial regulators with practical frameworks for governing the adoption, deployment and ongoing monitoring of generative artificial intelligence across financial services.

The programme examines the use of large language models (LLMs), retrieval-augmented generation (RAG), AI copilots, intelligent document processing, conversational banking assistants and AI agents in banking operations. It addresses the governance, operational, legal, cybersecurity, data protection and reputational risks associated with these technologies, including hallucinations, prompt injection, sensitive-data leakage, algorithmic bias, model misuse and dependence on external AI providers.

Course objectives

• Establish governance frameworks, policies and accountability structures for generative AI adoption in banking.
• Develop an enterprise generative AI governance and monitoring framework aligned with institutional risk appetite, applicable regulations and responsible AI principles.
• Design practical controls for prompt security, retrieval-augmented generation, human oversight, output validation, auditability and incident management.
• Evaluate AI applications, foundation models, external vendors and enterprise deployments using structured risk assessment and assurance methods.
• Assess generative AI risks involving model reliability, data confidentiality, algorithmic bias, cybersecurity and regulatory compliance.

Who should attend

Course content

• Module 1. Generative AI Fundamentals and Banking Applications Evolution of generative AI, foundation models, large language models, multimodal AI, tokenisation and embeddings, model inference, AI copilots, conversational interfaces, AI agents, enterprise
• Module 2. Enterprise Generative AI Governance Frameworks AI governance principles, enterprise AI strategies, institutional AI policies, board and senior management accountability, AI governance committees, roles and responsibilities, AI inventories, use-
• Module 3. Regulatory Requirements and Responsible AI in Banking Financial-sector regulatory expectations, responsible AI principles, applicable data protection and consumer protection laws, confidentiality obligations, financial services conduct requirem
• Module 4. Generative AI Risk Identification and Assessment AI risk taxonomies, inherent and residual risk, use-case materiality, customer impact, operational criticality, risk scoring, risk ownership, risk acceptance, risk treatment, model limitations, h
• Module 5. Large Language Model Selection and Enterprise Deployment Governance Commercial, open-weight and institution-hosted models, model selection criteria, model capability evaluation, cost and latency considerations, data handling policies, licensing
• Module 6. Data Governance, Privacy and Confidentiality Classification of banking information, customer data protection, confidential financial records, personally identifiable information, data minimisation, lawful processing, consent where applicable, r
• Module 7. Prompt Engineering, Prompt Security and Output Governance Prompt design for banking workflows, system instructions, prompt templates, role-based instructions, structured outputs, grounded responses, prompt injection, indirect prompt injection,
• Module 8. Retrieval-Augmented Generation and Banking Knowledge Systems Enterprise knowledge bases, document ingestion, OCR and document parsing, chunking strategies, embeddings, vector databases, semantic search, hybrid retrieval, metadata filtering, per
• Module 9. AI Agents, Workflow Automation and Autonomous Actions Agentic AI concepts, tool calling, workflow orchestration, multi-step reasoning, AI agents for customer support and internal operations, integration with banking applications, transaction-re
• Module 10. Cybersecurity and Adversarial Threats in Generative AI AI threat modelling, prompt injection, data poisoning, model extraction, insecure output handling, sensitive-data disclosure, malicious document ingestion, supply-chain vulnerabilities, co
• Module 11. Model Evaluation, Testing and Independent AI Assurance Model performance assessment, factual accuracy, groundedness, relevance, completeness, consistency, bias evaluation, robustness, harmful output detection, retrieval quality, hallucination
• Module 12. Governance of Banking Use Cases and Customer-Facing AI AI assistants for customer service, internal policy search, compliance support, financial document summarisation, relationship management, fraud investigation support, credit-related infor
• Module 13. Third-Party AI Risk, Cloud Governance and Vendor Management External AI providers, cloud-hosted models, model APIs, software-as-a-service platforms, vendor due diligence, data-use restrictions, contractual safeguards, audit rights, service-lev
• Module 14. AI Monitoring, Incident Management and Continuous Governance Production monitoring, usage analytics, model performance indicators, output quality monitoring, user feedback, access logs, policy violations, data leakage detection, incident class
• Module 15. Integrated Capstone: Enterprise Generative AI Governance and Deployment Design of a controlled generative AI assistant for banking operations, definition of business objectives, use-case classification, selection of an appropriate model, devel

General notes

• Participants learn how to establish institutional AI policies, classify use cases by risk, evaluate AI vendors and models, define acceptable-use boundaries, implement human oversight and monitor AI performance. The workshop also explores enterprise AI architecture, model access controls, audit trails, output evaluation, incident management and the integration of AI governance into existing enterprise risk management and compliance frameworks.
• Through practical exercises and banking case studies, participants develop AI governance policies, use-case assessment frameworks, risk registers, testing protocols and monitoring procedures. The programme emphasises responsible innovation, regulatory accountability, customer protection and the controlled deployment of generative AI in environments handling confidential financial information.
• Bank executives, chief risk officers, chief information officers, chief technology officers, chief compliance officers, AI governance officers, digital transformation managers, banking technology managers, model risk management specialists, operational risk officers, IT auditors, cybersecurity professionals, data protection officers, internal auditors, regulatory compliance teams, legal advisers, central bank supervisors, financial regulators and enterprise AI implementation teams.
• Practical Exercises and Case Studies Develop an enterprise generative AI policy for a simulated commercial bank. Create an AI use-case inventory and classify applications according to risk and business criticality. Conduct a risk assessment for an AI assistant accessing internal banking policies and procedures. Evaluate external AI models for security, confidentiality, reliability, cost and deployment suitability. Design a retrieval-augmented generation workflow with source citations and document access controls. Test an AI assistant for hallucinations, unsupported financial claims and inaccurate regulatory interpretations. Simulate prompt injection and sensitive-data extraction attempts against a banking AI application. Develop an evaluation scorecard for accuracy, groundedness, relevance, fairness and output consistency. Review an AI vendor agreement for data usage, retention, confidentiality, audit rights and incident notification. Define human approval and escalation controls for AI-assisted compliance and customer-facing workflows. Design an AI monitoring dashboard covering usage, quality, security incidents and policy violations. Complete a production readiness assessment and prepare an AI governance report for senior management.
• Practical Outputs and Deliverables Enterprise generative AI governance framework, institutional AI policy and acceptable-use standards, AI use-case inventory and risk classification matrix, generative AI risk assessment template, foundation model selection and vendor evaluation scorecard, AI data governance and confidentiality checklist, prompt security assessment framework, RAG architecture and access-control design, AI output evaluation scorecard, generative AI red-team testing checklist, human oversight and approval framework, third-party AI risk assessment template, AI incident response and escalation procedure, production readiness checklist, AI monitoring dashboard specification, model lifecycle governance and change management framework, AI assurance evidence register, enterprise generative AI deployment roadmap, capstone AI governance and production readiness report.

Frequently asked questions

Schedule & fees