Data Risk Management and Control Assessment Training Course

Course Introduction

The Data Risk Management And Control Assessment Training Course provides a structured approach to identifying, evaluating, managing, monitoring, and reducing risks associated with organizational data. Participants learn how data risks can arise throughout the data lifecycle and how effective risk assessment practices can help organizations understand vulnerabilities affecting data quality, security, privacy, availability, integrity, compliance, operations, and business decision-making.

The course examines the relationship between data risk management, data governance, internal controls, cybersecurity, privacy, compliance, enterprise risk management, and business continuity. Participants learn how to establish data risk assessment frameworks that identify critical information assets, assess threats and vulnerabilities, evaluate potential consequences, determine risk levels, and define appropriate controls for reducing exposure to unacceptable data-related risks.

Participants will develop practical skills for conducting data risk assessments across business processes, applications, databases, cloud environments, data warehouses, data lakes, integration platforms, reporting systems, analytics environments, and third-party data ecosystems. The training covers risk identification, risk classification, inherent and residual risk assessment, control mapping, control effectiveness evaluation, risk treatment, risk ownership, escalation, and continuous monitoring.

The course provides detailed guidance on assessing the effectiveness of existing data controls and determining whether they adequately address identified risks. Participants learn how to review preventive, detective, corrective, compensating, manual, and automated controls, while examining control design, implementation, operating effectiveness, evidence, exceptions, dependencies, and residual exposure. Emphasis is placed on connecting risk assessment results to practical control improvement and remediation decisions.

Modern data environments introduce increasingly complex risk factors involving cloud adoption, artificial intelligence, machine learning, data sharing, APIs, distributed architectures, self-service analytics, third-party providers, and cross-border data flows. The training therefore addresses emerging data risks associated with automated decision-making, data lineage, model inputs, sensitive information, access privileges, data integrity, system dependencies, shadow data environments, and rapidly changing technology ecosystems.

Emerging approaches such as continuous data risk monitoring, automated control assessment, AI-assisted risk analysis, data observability, predictive risk indicators, intelligent control testing, and risk-based governance are incorporated into the course. Participants learn how to build sustainable data risk management and control assessment programs that provide management with clearer visibility into data exposure, control effectiveness, remediation priorities, and the organization's ability to protect and responsibly use its information assets.

Duration

10 days

Who Should Attend

• Data Risk Managers and Data Risk Analysts

• Data Governance Managers and Data Governance Officers

• Chief Data Officers and Data Management Leaders

• Risk Management and Enterprise Risk Professionals

• Governance, Risk, and Compliance Specialists

• Internal Auditors and Control Assessment Professionals

• Data Quality Managers and Data Quality Analysts

• Information Security and Cybersecurity Professionals

• Privacy and Data Protection Professionals

• IT Governance and Technology Risk Professionals

• Data Owners and Data Stewards

• Data Architects and Enterprise Architects

• Business Process and Control Owners

• Compliance and Regulatory Reporting Professionals

• Data Engineers and Technology Operations Professionals

• Professionals responsible for identifying, assessing, monitoring, or controlling data-related risks

Course Objectives

• Understand the principles of data risk management and how structured risk assessment supports the protection, reliability, availability, security, and responsible use of organizational data.

• Identify data-related risks across business processes, applications, systems, databases, integrations, cloud platforms, analytics environments, and third-party data ecosystems.

• Apply structured methodologies for assessing inherent risk, control effectiveness, residual risk, risk likelihood, potential impact, and overall data risk exposure.

• Develop data risk assessment criteria that consider business criticality, regulatory requirements, financial consequences, operational disruption, customer impact, security exposure, and reputational considerations.

• Design risk and control matrices that connect data assets, business processes, identified risks, control objectives, control activities, owners, evidence, and assessment results.

• Evaluate the design and operating effectiveness of preventive, detective, corrective, compensating, manual, and automated controls that address identified data risks.

• Apply risk-based approaches to prioritize data risks and control weaknesses according to their potential impact, likelihood, urgency, recurrence, and organizational significance.

• Conduct control assessments using appropriate evidence collection, walkthroughs, interviews, inspection, sampling, analytical procedures, observation, reperformance, and automated testing techniques.

• Identify control deficiencies and determine whether weaknesses result from inadequate control design, ineffective implementation, inconsistent execution, insufficient monitoring, or changing risk conditions.

• Develop risk treatment and remediation strategies that define accountable owners, corrective actions, deadlines, resources, dependencies, residual risk acceptance, and validation requirements.

• Establish data risk indicators, control performance measures, dashboards, and reporting mechanisms that provide management with ongoing visibility into changing risk exposure and control effectiveness.

• Implement continuous improvement practices that integrate data risk management with governance, cybersecurity, privacy, data quality, assurance, compliance, and emerging technology risk management.

Comprehensive Course Outline

Module 1: Foundations Of Data Risk Management

• Understanding data risk and its relationship to data governance, information security, privacy, compliance, operations, and organizational performance.

• Exploring common categories of data risk including quality, security, privacy, availability, integrity, access, compliance, operational, and third-party risks.

• Examining how data risks emerge throughout collection, storage, processing, transformation, sharing, reporting, retention, and disposal.

• Establishing foundational principles for developing consistent, risk-based, measurable, and sustainable data risk management practices.

Module 2: Data Risk Identification And Classification

• Identifying data risks across critical business processes, information assets, applications, platforms, interfaces, reports, and analytical environments.

• Developing data risk taxonomies that support consistent classification, aggregation, reporting, ownership, and management across organizational domains.

• Assessing risks associated with sensitive, critical, regulated, confidential, high-value, and strategically important data assets.

• Establishing systematic risk identification methods that combine stakeholder input, documentation review, technical analysis, incidents, audits, and monitoring evidence.

Module 3: Data Asset And Business Impact Assessment

• Identifying critical data assets and evaluating their importance to business operations, regulatory reporting, analytics, customers, and strategic decision-making.

• Assessing business impact resulting from data loss, corruption, unauthorized access, unavailability, inaccurate reporting, or inappropriate data use.

• Evaluating dependencies between critical data assets, applications, business processes, systems, users, reports, and downstream consumers.

• Translating technical data risks into measurable business, operational, financial, regulatory, customer, and strategic consequences.

Module 4: Data Risk Assessment Methodologies

• Applying structured approaches for evaluating likelihood, impact, exposure, velocity, persistence, and other factors influencing data risk severity.

• Developing qualitative, quantitative, and hybrid risk assessment models appropriate for different organizational data environments.

• Establishing consistent scoring criteria that support transparent comparison, prioritization, escalation, and management decision-making.

• Managing uncertainty, incomplete evidence, conflicting stakeholder perspectives, changing conditions, and emerging risks during assessments.

Module 5: Inherent And Residual Data Risk

• Understanding the distinction between inherent risk before controls and residual risk remaining after controls are considered.

• Evaluating how control effectiveness, control coverage, dependencies, exceptions, and implementation gaps influence residual data risk.

• Developing methodologies for documenting inherent risk, control-adjusted risk, accepted risk, transferred risk, and target risk levels.

• Establishing processes for monitoring changes in residual risk following remediation, system changes, incidents, regulatory developments, or emerging threats.

Module 6: Data Risk And Control Mapping

• Developing risk and control matrices that connect identified data risks with appropriate control objectives and control activities.

• Mapping preventive, detective, corrective, compensating, manual, and automated controls to specific data risk scenarios.

• Identifying control gaps where significant data risks lack adequate preventive, detective, monitoring, or remediation mechanisms.

• Establishing traceability between risks, controls, owners, evidence, testing procedures, deficiencies, remediation actions, and assurance outcomes.

Module 7: Control Assessment And Effectiveness Testing

• Evaluating whether existing controls are appropriately designed to address the risks for which they were established.

• Assessing whether controls have been implemented consistently within relevant processes, applications, systems, and organizational environments.

• Testing operating effectiveness through inspection, inquiry, observation, reperformance, sampling, analytical review, and automated procedures.

• Documenting control assessment results, exceptions, evidence, conclusions, reviewer observations, and residual risk implications.

Module 8: Data Quality, Integrity And Availability Risks

• Assessing risks associated with inaccurate, incomplete, inconsistent, duplicated, invalid, outdated, or improperly transformed organizational data.

• Evaluating controls that protect data integrity during collection, integration, migration, transformation, storage, reporting, and downstream consumption.

• Assessing availability risks affecting critical data, systems, interfaces, analytical platforms, and business processes.

• Establishing reconciliation, validation, backup, recovery, monitoring, and quality controls that reduce data integrity and availability exposure.

Module 9: Data Security, Privacy And Access Risks

• Assessing risks associated with unauthorized access, excessive privileges, inappropriate data sharing, credential compromise, and weak access governance.

• Evaluating controls covering authentication, authorization, privileged access, segregation of duties, encryption, masking, monitoring, and secure data handling.

• Identifying privacy risks involving sensitive information, excessive collection, inappropriate use, retention, disclosure, and inadequate protection.

• Integrating data risk assessments with cybersecurity, privacy management, identity governance, information security, and regulatory compliance programs.

Module 10: Technology, Cloud And Third-Party Data Risks

• Assessing risks arising from cloud platforms, data lakes, data warehouses, APIs, integration services, distributed architectures, and technology dependencies.

• Evaluating third-party and vendor data risks involving data sharing, outsourced processing, contractual obligations, access, security, quality, and service dependencies.

• Assessing technology change risks associated with migrations, system upgrades, integrations, application changes, and new data platforms.

• Establishing risk assessment procedures for evaluating control responsibilities across internal teams, technology providers, cloud platforms, and external data processors.

Module 11: Data Risk Indicators And Continuous Monitoring

• Developing key risk indicators that provide early visibility into changes in data exposure, control performance, incidents, exceptions, and vulnerabilities.

• Establishing dashboards and monitoring mechanisms for tracking critical data risks across business domains, applications, systems, and data environments.

• Applying thresholds, alerts, trends, and exception monitoring to identify risks requiring investigation, escalation, or immediate management attention.

• Integrating risk indicators with data quality monitoring, cybersecurity systems, privacy controls, data observability, and enterprise risk reporting.

Module 12: Risk Treatment And Control Improvement

• Developing appropriate risk treatment strategies including mitigation, avoidance, transfer, acceptance, remediation, and enhanced monitoring.

• Prioritizing risk treatment actions according to severity, business impact, regulatory obligations, control weaknesses, resource requirements, and organizational priorities.

• Designing remediation plans that define actions, owners, timelines, dependencies, milestones, evidence requirements, and completion criteria.

• Validating whether completed remediation activities have reduced risk to an acceptable level and strengthened the underlying control environment.

Module 13: Risk Reporting, Escalation And Governance

• Designing data risk reports that communicate significant risks, control weaknesses, residual exposure, trends, remediation progress, and management actions.

• Establishing escalation criteria for high-risk data issues, overdue remediation, repeated control failures, emerging threats, and unacceptable residual exposure.

• Integrating data risk reporting with data governance councils, enterprise risk committees, audit functions, compliance programs, and executive management.

• Developing governance processes for risk acceptance, exception approval, accountability, review, challenge, escalation, and periodic reassessment.

Module 14: Scenario-Based Data Risk And Control Assessment

• Conducting practical assessments using realistic scenarios involving data breaches, quality failures, system migrations, unauthorized access, and reporting inaccuracies.

• Performing end-to-end risk assessments from asset identification and risk discovery through control evaluation, scoring, treatment, and reporting.

• Applying structured evidence collection and testing techniques to determine whether controls adequately address scenario-specific data risks.

• Developing management recommendations based on assessment findings, residual exposure, control effectiveness, remediation requirements, and business priorities.

Module 15: Modern Data And Emerging Technology Risks

• Assessing data risks associated with artificial intelligence, machine learning, generative AI, automated decision systems, and model-dependent data processes.

• Evaluating risks arising from data mesh, data products, self-service analytics, federated governance, decentralized ownership, and distributed data environments.

• Examining emerging risks involving synthetic data, data provenance, model inputs, automated data processing, intelligent systems, and third-party AI services.

• Developing adaptable control assessment approaches that remain effective as technologies, data architectures, business models, and regulatory expectations evolve.

Module 16: Continuous Improvement And Advanced Risk Management

• Establishing continuous data risk assessment practices that detect changing exposure, control deterioration, emerging threats, and new organizational dependencies.

• Exploring AI-assisted risk analysis, automated control assessment, predictive risk indicators, continuous assurance, and intelligent data risk monitoring.

• Integrating data risk management with DataOps, cybersecurity, privacy, data quality, business continuity, enterprise architecture, and governance programs.

• Building long-term data risk improvement roadmaps that strengthen resilience, accountability, control effectiveness, regulatory confidence, and responsible data use.

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.