AI Risk Assessment and Governance for Public Agencies Training Course

Course Introduction

Artificial intelligence is increasingly being adopted by public agencies to improve service delivery, automate administrative activities, analyze information, support decision-making, and strengthen institutional productivity. However, the use of AI also introduces complex risks involving privacy, cybersecurity, bias, transparency, accountability, data quality, operational resilience, and public trust. This course equips public agency leaders and professionals with practical methods for identifying, assessing, governing, and mitigating these risks.

AI risk management requires a structured approach that considers the entire lifecycle of an AI system, from initial concept and procurement through development, testing, deployment, monitoring, modification, and retirement. Participants will learn how to establish practical risk assessment processes that help agencies understand where AI could create harm, how significant those risks may be, and what controls should be introduced before systems are implemented or expanded.

The training examines the distinctive risks associated with public sector AI. Government agencies often operate in environments where decisions can affect access to public services, benefits, opportunities, enforcement, employment, and other important aspects of citizens' lives. Participants will therefore explore how to assess potential impacts on individuals and communities while ensuring that AI-supported processes remain fair, transparent, accountable, secure, and consistent with institutional mandates.

Participants will learn how to create AI inventories, risk registers, impact assessments, control frameworks, governance structures, approval procedures, monitoring mechanisms, and incident response processes. The course provides practical approaches for evaluating AI systems and use cases according to their intended purpose, level of impact, data requirements, technical characteristics, human oversight needs, and potential consequences of failure.

The course also addresses emerging governance challenges involving generative AI, AI agents, multimodal systems, third-party platforms, automated decision support, synthetic content, deepfakes, model changes, vendor dependency, and rapidly evolving regulatory expectations. Participants will explore how traditional risk management approaches can be adapted to address technologies whose capabilities and risks may change rapidly after deployment.

By the end of the course, participants will be able to establish more systematic and proportionate approaches to AI risk management within public agencies. They will understand how to identify risks early, evaluate their severity, implement appropriate safeguards, assign accountability, monitor AI performance, and respond to incidents. The course ultimately supports agencies in adopting AI confidently while protecting citizens, institutional integrity, operational resilience, and public trust.

Duration

5 days

Who Should Attend

• Senior public agency executives responsible for governance, institutional performance, digital transformation, and strategic risk management.

• Government managers overseeing AI projects, technology adoption, operational modernization, and organizational transformation initiatives.

• Risk management professionals responsible for identifying and mitigating technology, operational, financial, and strategic risks.

• Legal and compliance officers advising public agencies on AI governance, regulatory obligations, accountability, and institutional policies.

• ICT and cybersecurity professionals responsible for securing AI systems, information assets, infrastructure, and technology-enabled workflows.

• Data protection and information governance officers managing privacy, confidentiality, data quality, and responsible information use.

• Internal auditors and assurance professionals assessing AI controls, governance arrangements, compliance, and institutional risk exposure.

• Procurement and contract management officers evaluating AI vendors, technology suppliers, service agreements, and third-party risks.

• Policy and governance officers developing AI policies, standards, procedures, frameworks, and institutional accountability mechanisms.

• Ethics and integrity professionals addressing fairness, transparency, human oversight, discrimination, and responsible AI use.

• Monitoring and evaluation professionals assessing AI performance, outcomes, unintended effects, and continuous improvement requirements.

• Digital transformation and innovation teams responsible for introducing AI technologies into public agency operations.

• Local authority and public service leaders implementing AI-supported services, administration, and citizen engagement initiatives.

• Program and project managers managing AI deployments, pilots, process redesign, implementation risks, and stakeholder requirements.

• Consultants and advisors supporting public agencies with AI strategy, risk assessment, governance, compliance, and digital transformation.

Course Objectives

• Explain the principles, processes, and governance structures required to manage artificial intelligence risks within public agencies.

• Identify AI risks involving privacy, cybersecurity, bias, reliability, transparency, accountability, data quality, and operational resilience.

• Conduct structured risk assessments that evaluate the likelihood, severity, exposure, and potential consequences of AI-related failures.

• Develop AI risk registers and control frameworks that document risks, mitigation measures, responsible owners, monitoring indicators, and escalation procedures.

• Apply risk-based approaches to classify AI systems and determine appropriate levels of governance, testing, human oversight, and approval.

• Assess the potential impact of AI applications on citizens, employees, public services, institutional decisions, and vulnerable or affected communities.

• Establish practical governance controls for AI procurement, deployment, monitoring, modification, auditing, incident response, and system retirement.

• Evaluate third-party AI providers according to security, privacy, transparency, performance, contractual, operational resilience, and governance requirements.

• Design effective human oversight mechanisms that preserve accountable decision-making when AI contributes to consequential public agency processes.

• Develop an actionable AI risk governance strategy that enables responsible innovation while protecting citizens, institutional integrity, compliance, and public trust.

Comprehensive Course Outline

Module 1: Foundations of AI Risk in Public Agencies

• Understanding artificial intelligence risk and why it requires specialized governance approaches within public institutions.

• Examining AI risks across generative AI, predictive systems, machine learning, intelligent automation, and AI-enabled decision support.

• Exploring the relationship between AI risk, institutional objectives, public interest, service quality, accountability, and organizational resilience.

• Assessing common AI risk categories including strategic, operational, ethical, legal, technological, financial, cybersecurity, and reputational risks.

Module 2: AI Risk Identification and Use Case Assessment

• Identifying potential risks during AI use case discovery, design, procurement, implementation, deployment, and operational use.

• Mapping AI-enabled processes to determine where errors, failures, bias, unauthorized actions, or harmful outcomes could occur.

• Assessing affected stakeholders, decision points, data flows, dependencies, human roles, system outputs, and potential consequences.

• Developing structured risk statements that clearly connect AI capabilities with potential causes, impacts, affected parties, and control requirements.

Module 3: AI Risk Classification and Impact Assessment

• Developing risk classification models that distinguish low-impact AI applications from high-impact or consequential public agency systems.

• Conducting AI impact assessments to evaluate potential effects on rights, services, privacy, fairness, safety, security, and institutional operations.

• Applying likelihood and consequence criteria to determine risk severity and establish appropriate mitigation priorities.

• Establishing escalation thresholds for AI applications requiring enhanced executive oversight, legal review, technical testing, or independent assurance.

Module 4: Data, Privacy, and Information Governance Risks

• Assessing data quality, completeness, accuracy, relevance, provenance, representativeness, and suitability for AI-enabled public agency applications.

• Identifying privacy risks associated with collecting, processing, storing, sharing, and exposing personal or sensitive information through AI systems.

• Establishing information classification and handling controls for confidential, restricted, personal, operational, and strategically sensitive agency information.

• Developing data governance practices that support responsible AI while maintaining security, confidentiality, integrity, traceability, and appropriate access.

Module 5: Cybersecurity and Technical AI Risk

• Identifying cybersecurity threats involving AI systems, including prompt injection, data leakage, unauthorized access, malicious inputs, and model manipulation.

• Assessing technical risks related to model reliability, system integration, infrastructure dependencies, software vulnerabilities, and unexpected system behavior.

• Establishing security controls covering access management, authentication, monitoring, logging, testing, vulnerability management, and incident response.

• Developing resilience strategies for AI systems whose failure could disrupt critical government operations, services, or decision-support processes.

Module 6: Ethical Risk, Bias, Fairness, and Human Oversight

• Understanding algorithmic bias, discriminatory outcomes, unequal impacts, and ethical risks that may arise from AI-supported public agency processes.

• Applying practical methods for detecting, documenting, mitigating, and monitoring bias throughout the AI system lifecycle.

• Designing human oversight mechanisms that ensure officials can review, challenge, override, and correct AI-generated recommendations or decisions.

• Establishing ethical governance practices that protect fairness, transparency, dignity, accessibility, accountability, and public confidence.

Module 7: AI Governance Frameworks, Policies, and Controls

• Designing institutional AI governance frameworks that establish clear responsibilities, approval procedures, oversight structures, and accountability mechanisms.

• Developing AI policies covering acceptable use, risk management, employee responsibilities, information handling, system approval, and monitoring requirements.

• Creating AI inventories, risk registers, control matrices, documentation standards, review schedules, and governance reporting mechanisms.

• Establishing proportionate governance processes that support responsible experimentation without creating unnecessary administrative barriers to beneficial innovation.

Module 8: Procurement, Third-Party Risk, and Vendor Governance

• Integrating AI risk requirements into procurement specifications, technical evaluations, contracts, service agreements, and supplier management procedures.

• Assessing AI vendors based on data practices, security controls, model transparency, performance, auditability, resilience, and governance capabilities.

• Managing risks involving proprietary models, vendor dependency, subcontractors, service changes, data access, outages, and limited technical transparency.

• Establishing contractual controls for incident reporting, audit rights, data protection, system changes, performance standards, and responsible AI requirements.

Module 9: Monitoring, Auditing, Incident Management, and Emerging Risks

• Establishing continuous AI monitoring processes for accuracy, reliability, fairness, security, performance, drift, and unintended consequences after deployment.

• Designing audit and assurance mechanisms for reviewing AI systems, governance controls, risk treatment, documentation, and compliance over time.

• Developing incident response procedures for identifying, reporting, investigating, containing, correcting, and learning from AI-related failures.

• Examining emerging risks involving AI agents, multimodal systems, deepfakes, synthetic information, autonomous workflows, and rapidly changing AI capabilities.

Module 10: Implementing an AI Risk Governance Program

• Developing an institutional roadmap for establishing, implementing, monitoring, and continuously improving public agency AI risk governance.

• Assigning clear responsibilities across executives, risk officers, technical teams, legal functions, procurement units, data specialists, and operational users.

• Establishing key performance and risk indicators for measuring governance effectiveness, control performance, incident trends, and residual risk.

• Creating an executive action plan that integrates risk assessment, governance, workforce capability, technology controls, stakeholder engagement, and continuous improvement.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.