Course Introduction
Data Platform Security and Access Management Training Course provides a comprehensive foundation for protecting modern data platforms against unauthorized access, misuse, data exposure, and operational security risks. As organizations increasingly rely on databases, data warehouses, data lakes, lakehouses, streaming platforms, and cloud-based data services, effective security and access management have become essential components of reliable data operations. This course examines the principles, architectures, technologies, controls, and operational practices required to secure data platforms throughout their lifecycle.
The course explores data platform security across infrastructure, databases, storage systems, processing environments, applications, networks, APIs, and cloud services. Participants examine how authentication, authorization, identity management, access policies, privileged access controls, encryption, secrets management, and network security work together to establish layered protection. Particular attention is given to designing access models that provide users and applications with appropriate permissions while reducing excessive privileges, unauthorized data access, credential exposure, and security vulnerabilities.
Participants will learn how to design and implement role-based access control, attribute-based access control, least-privilege models, identity federation, service identities, privileged access management, and access certification processes. The course also addresses database permissions, data warehouse controls, lake and lakehouse security, object-level and column-level access, row-level security, dynamic data masking, and policy-based access enforcement. These concepts enable organizations to manage access consistently across complex data environments while supporting operational requirements and regulatory obligations.
Data platform security also requires continuous monitoring, detection, and response. The course therefore examines security logging, audit trails, access monitoring, behavioral analysis, anomaly detection, security information and event management, and investigation workflows. Participants learn how to identify suspicious access patterns, investigate inappropriate permissions, detect credential misuse, and establish controls for responding to security incidents involving data platforms. The relationship between security monitoring, data governance, compliance, privacy, and operational risk is also explored.
Modern data platforms increasingly operate across hybrid and multi-cloud environments, creating additional challenges around identity federation, distributed authorization, data movement, third-party access, APIs, machine identities, and cross-platform permissions. The course addresses these challenges while introducing security considerations for data pipelines, analytics environments, AI and machine learning platforms, data sharing environments, and emerging data architectures. Participants examine how security controls can be integrated into data engineering and platform operations rather than being treated as separate activities.
Advanced topics include zero-trust approaches to data platforms, automated access provisioning and deprovisioning, policy-as-code, identity-aware data security, continuous access evaluation, secrets automation, security posture management, and AI-assisted security monitoring. Through a combination of conceptual frameworks, operational practices, governance principles, and emerging technologies, the course equips participants to develop structured security and access management capabilities that protect data assets while supporting legitimate business and analytical use.
Duration: 10 days
Who Should Attend
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Data Platform Engineers
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Data Engineers
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Data Architects
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Database Administrators
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Cloud Engineers
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Cloud Security Professionals
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Cybersecurity Professionals
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Information Security Managers
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Identity And Access Management Professionals
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Data Governance Professionals
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Data Protection And Privacy Professionals
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DevOps And Platform Engineering Professionals
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Security Operations Professionals
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Enterprise Architects
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IT Managers And Technology Leaders
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Compliance And Risk Professionals
Course Objectives
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Explain the core principles, architectures, and control frameworks used to secure modern data platforms across on-premises, cloud, hybrid, and distributed environments.
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Identify major data platform security risks involving unauthorized access, privilege abuse, credential compromise, data exposure, insecure configurations, and weak identity controls.
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Design authentication and authorization architectures that provide secure and controlled access to databases, warehouses, lakes, lakehouses, applications, APIs, and data services.
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Apply role-based access control, attribute-based access control, least-privilege principles, and policy-driven authorization to different data platform environments.
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Develop effective identity lifecycle processes covering user provisioning, access changes, privileged access, service identities, access reviews, and timely deprovisioning.
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Implement security controls for databases and data platforms, including row-level security, column-level security, data masking, object permissions, and administrative controls.
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Apply encryption, key management, secrets management, credential protection, and secure communications practices to protect data and platform components.
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Establish network and infrastructure security controls that restrict unauthorized connectivity to data services and reduce the attack surface of data platforms.
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Develop security monitoring, logging, auditing, alerting, and investigation capabilities for detecting suspicious or unauthorized data platform activities.
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Integrate data platform security with governance, privacy, compliance, risk management, incident response, and operational assurance processes.
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Evaluate security requirements for cloud, hybrid, multi-cloud, data pipeline, API, analytics, AI, and machine learning environments.
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Develop continuous security improvement practices using automation, policy-as-code, zero-trust principles, security posture management, and emerging data security technologies.
Comprehensive Course Outline
Module 1: Foundations Of Data Platform Security
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Data platform security principles, objectives, responsibilities, threat surfaces, and layered defense strategies.
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Security architecture across databases, warehouses, lakes, lakehouses, pipelines, applications, APIs, and cloud services.
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Common data platform threats including unauthorized access, credential compromise, privilege abuse, data leakage, and insecure configurations.
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Security-by-design principles and integrating protection requirements throughout the data platform lifecycle.
Module 2: Data Platform Identity And Authentication
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Identity concepts for users, administrators, applications, services, workloads, and machine identities.
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Passwordless authentication, multi-factor authentication, certificates, tokens, and modern authentication mechanisms.
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Identity federation, single sign-on, directory integration, and centralized authentication architectures.
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Authentication security for cloud-native, distributed, hybrid, and multi-platform data environments.
Module 3: Authorization And Access Control Models
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Role-Based Access Control, Attribute-Based Access Control, rule-based authorization, and policy-driven access models.
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Least privilege, need-to-know access, separation of duties, and controlled privilege escalation.
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Object-level, schema-level, database-level, table-level, column-level, and row-level access controls.
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Designing scalable authorization models for complex organizational and multi-tenant data environments.
Module 4: Identity Lifecycle And Access Governance
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User onboarding, provisioning, role assignment, access modification, suspension, and deprovisioning processes.
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Access request workflows, approvals, entitlement management, and periodic access certification.
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Managing joiner, mover, and leaver processes across centralized and distributed data platforms.
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Automating identity lifecycle management and reducing orphaned accounts, excessive permissions, and stale access.
Module 5: Privileged Access And Administrative Security
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Privileged accounts, administrative roles, elevated permissions, and risks associated with excessive privileges.
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Privileged Access Management principles including controlled elevation, credential protection, and session monitoring.
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Database administrator, cloud administrator, platform administrator, and emergency access controls.
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Break-glass access, administrative accountability, privileged activity logging, and continuous review.
Module 6: Database And Data Store Security
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Database security architecture covering authentication, authorization, permissions, roles, schemas, and administrative controls.
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Row-level security, column-level security, dynamic data masking, views, stored procedures, and controlled data exposure.
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Security configuration for relational, NoSQL, analytical, distributed, and cloud-managed databases.
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Database vulnerability management, secure configuration baselines, patching, hardening, and security assessment.
Module 7: Data Encryption, Key Management And Secrets Security
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Encryption at rest, encryption in transit, encryption in use, and appropriate protection strategies for sensitive data.
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Cryptographic keys, key rotation, key storage, hardware security modules, and centralized key management.
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Secrets management for database credentials, API keys, tokens, certificates, connection strings, and service accounts.
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Preventing credential leakage through source code, configuration files, logs, pipelines, notebooks, and development environments.
Module 8: Data Platform Network And Infrastructure Security
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Network segmentation, private connectivity, firewalls, security groups, access control lists, and secure service endpoints.
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Protecting database and data platform interfaces from unauthorized network access and unnecessary exposure.
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Secure connectivity between users, applications, pipelines, cloud services, and distributed data components.
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Zero-trust network principles and identity-aware connectivity for modern data platform architectures.
Module 9: Data Access Monitoring, Logging And Auditing
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Data access logging, authentication events, authorization decisions, administrative actions, and security audit trails.
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Designing centralized logging architectures for databases, cloud services, data warehouses, pipelines, and applications.
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Monitoring unusual access behavior, privilege changes, failed authentication, excessive queries, and suspicious data activity.
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Security Information And Event Management integration, alerting, investigation workflows, and audit evidence management.
Module 10: Data Platform Security Incident Management
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Identifying and classifying security incidents involving unauthorized access, credential compromise, data exposure, and privilege abuse.
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Incident response processes for containment, investigation, eradication, recovery, and post-incident improvement.
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Forensic considerations for database activity, access logs, cloud events, pipeline executions, and compromised identities.
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Coordinating security, data, infrastructure, legal, compliance, privacy, and business teams during data security incidents.
Module 11: Cloud And Hybrid Data Platform Security
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Security architectures for cloud databases, warehouses, data lakes, lakehouses, object storage, and managed data services.
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Cloud identity and access management, resource policies, service identities, workload identities, and cross-account access.
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Hybrid and multi-cloud identity federation, centralized policy management, and distributed access control challenges.
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Cloud security posture management, misconfiguration detection, continuous control monitoring, and security assurance.
Module 12: Data Pipeline, API And Application Access Security
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Securing ETL, ELT, orchestration, streaming, ingestion, transformation, and data integration workflows.
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API authentication, authorization, tokens, service identities, rate controls, and secure data service interfaces.
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Protecting data pipelines from credential exposure, unauthorized execution, insecure dependencies, and malicious data flows.
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Integrating security controls into CI/CD, DevSecOps, infrastructure-as-code, and data engineering workflows.
Module 13: Data Governance, Privacy And Access Compliance
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Integrating security access controls with data classification, sensitivity labeling, ownership, stewardship, and governance frameworks.
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Protecting personal, confidential, regulated, financial, health, and other sensitive data through appropriate access restrictions.
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Access policies for data sharing, third-party users, external collaborators, analytical environments, and business applications.
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Supporting regulatory requirements through access reviews, audit trails, evidence collection, retention controls, and policy enforcement.
Module 14: Zero Trust And Continuous Data Access Security
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Zero-trust principles including continuous verification, least privilege, identity-aware access, and explicit trust decisions.
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Continuous access evaluation based on identity, device posture, location, risk signals, behavior, and data sensitivity.
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Adaptive access controls, conditional policies, session controls, and dynamic authorization mechanisms.
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Applying zero-trust architectures to distributed data platforms, APIs, cloud services, analytics, and machine identities.
Module 15: Automated Data Security And Intelligent Access Management
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Policy-as-code, automated provisioning, automated entitlement management, and continuous security control enforcement.
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Security automation for access reviews, privilege detection, credential rotation, configuration validation, and remediation.
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Behavioral analytics and machine learning for detecting anomalous access patterns and potential data security threats.
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Emerging approaches including AI-assisted security operations, automated policy optimization, and intelligent data access governance.
Module 16: Advanced Data Platform Security And Future Directions
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Security architecture for AI platforms, machine learning workloads, data products, real-time analytics, and advanced data ecosystems.
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Securing data sharing, clean rooms, federated environments, distributed analytics, and cross-organizational data collaboration.
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Future developments in confidential computing, privacy-enhancing technologies, identity-aware data security, and autonomous security operations.
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Developing a continuous data platform security strategy covering architecture, access governance, monitoring, resilience, compliance, and emerging threats.
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.
