CL-355: Containers, Kubernetes, and Red Hat OpenShift Administration

 

Course Overview:

Through an introduction to Docker, Kubernetes, and Red Hat OpenShift Platform, this training course helps you understand one of the key tenets of the DevOps and DevSecOps Platform (DSOP) movement: continuous integration and continuous deployment. The CI/CD pipeline becomes well understood and implemented in an open architecture.  Containers have become a key technology for the configuration and deployment of applications and micro services. Kubernetes is a container orchestration platform that provides foundational services in Red Hat OpenShift Container Platform, which allows enterprises to manage container deployments and scale their applications using Kubernetes.

This training course provides an overview of the DoD Enterprise DevSecOps Platform (DSOP) Reference Design, its current state, and ties to DoD Cloud Platform One (P1). Workflows of the DoD Iron Bank container repository are introduced, along with an overview of the DoD Pipeline as represented in Big Bang.  Continuous authorization cATO via Party Bus within NIST RMF is presented. You will become aware of the Platform One (P1) integrations and relationship to Docker, Kubernetes, Istio (Red Hat OpenShift Service Mesh) and Red Hat OpenShift Platform.

In addition to gaining an understanding of these tools, you will build core administration skills through the installation, configuration, and management of an OpenShift cluster and containerized applications.

Course Objectives:

  • Learn about Containers, Docker, Kubernetes, and OpenShift architecture
  • Overview DoD Enterprise DevSecOps Platform (DSOP) Reference Design and DoD Cloud Platform One (P1)
  • Tie together awareness of various DoD Cloud offerings and their relationships
  • Create containerized services
  • Manage containers and container images
  • Deploy multi-container applications
  • Install an OpenShift cluster
  • Configure and manage masters and nodes
  • Secure OpenShift
  • Control access to resources on OpenShift
  • Monitor and collect metrics on OpenShift
  • Deploy applications on OpenShift using source-to-image (S2I)
  • Manage storage on OpenShift

Course Outline:

  • Getting started with container technology
  • Creating containerized services
  • Managing containers
  • Managing container images
  • Creating custom container images
  • Deploying containerized applications on OpenShift
  • Deploying multi-container applications
  • Troubleshooting containerized applications
  • Comprehensive Review of Introduction to Container, Kubernetes, and RedHat OpenShift
  • Introducing Red Hat OpenShift Container Platform
  • Installing OpenShift Container Platform
  • Describing and exploring OpenShift networking concepts
  • Executing commands
  • Controlling access to OpenShift resources
  • Allocating persistent storage
  • Managing application deployments
  • Installing and configuring the metrics subsystem
  • Managing and monitoring OpenShift Container Platform

Dates/Locations:

No Events

Duration: 5 Days

Prerequisites:

  • Ability to use a Linux® terminal session and issue operating system commands
  • Good foundation in Linux
  • Experience with web application architectures and their corresponding technologies

Target Audience:

  • Developers who wish to containerize software applications
  • Administrators who are new to container technology and container orchestration
  • Architects who are considering using container technologies in software architectures
  • System administrators
  • System architects
  • Architects and developers who want to install and configure OpenShift Container Platform
  • Those working in the field of DevSecOps supporting DoD Platform One (P1) and other implementations

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RH-345: Red Hat JBoss Application Administration I

 
 

Course Overview:

 

Install, configure, and manage Red Hat JBoss Enterprise Application Platform

Red Hat JBoss® Application Administration I teaches you the best practices for installing and configuring Red Hat JBoss Enterprise Application Platform 6. Through hands-on labs, learn the essential, real-world tasks that a system administrator needs to know to effectively deploy and manage applications on JBoss Enterprise Application Platform.

Attendees to RH-345: Red Hat JBoss Application Administration I, will receive TechNow approved course materials and expert instruction.

Dates/Locations:

Duration: 5 Days

Course Objectives:

  • Overview of JBoss Enterprise Application Platform
  • Configure JBoss Enterprise Application Platform in standalone mode
  • Configure JBoss Enterprise Application Platform in domain mode
  • Configure servers
  • Use the CLI tool
  • The datasource subsystem
  • The logging subsystem
  • The messaging subsystem
  • The security subsystem
  • JVM configuration
  • Migrating applications to JBoss Enterprise Application Platform 6
  • The web subsystem

Prerequisites:

  • Linux System Administration

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TN-413: Certified AI Program Manager (C|AIPM)

Certified AI Program Manager (CAIPM) is EC-Council’s professional certification for people responsible for owning AI decisions and driving execution: business, technology, data, and risk.

The Certified AI Program Manager (CAIPM) Course equips you with hands-on expertise across the full spectrum of AI tools, from conversational AI and image generation to code assistants and audio synthesis.

Participants will learn how to evaluate, deploy, and integrate AI tools into enterprise workflows, understanding not just how they work, but how to leverage them for maximum business impact. This course covers how to assess AI readiness across teams and processes, Prioritize AI use cases tied to business outcomes, Design adoption and rollout roadmaps , Coordinate delivery across cross-functional teams, implement governance, Responsible AI, and security controls , and how to track performance and ROI to prove value

By the end of the course, learners will be well-prepared to take the Certified AI Program Manager (CAIPM) exam and demonstrate the ability to own AI initiatives end to end , validate mastery of decision framing and trade-off analysis for AI initiatives and Apply governance, ethics, and risk management principles across the AI lifecycle.

Course Objectives:

•MLOps Principles: Model life cycle management for scalable, production-ready AI
•Use Case Evaluation: ROI-driven assessment and prioritization of AI initiatives
•AI Strategy Frameworks: Enterprise AI roadmapping, portfolio planning, and value prioritization
•AI Investment Justification: Quantifying AI value, ROI, and mission impact for funding decisions
•Change Management: Workforce enablement and stakeholder alignment
•KPI Development: AI metrics, success indicators, and executive dashboards
•AI Governance: Risk, ethics, compliance, and responsible AI principles
•Vendor Evaluation: AI platform and tool selection aligned with enterprise needs

Dates/Locations:

No Events

Prerequisites: Familiarity with generative AI concepts, prompt engineering fundamentals, and AI workflows will help you succeed. 

TN-412: Artificial Intelligence Essentials (AI|E)

 

 

TN-804: Corporate Risk Management

Course Overview:

TechNow understands the security threats that make your network & your company vulnerable.  We have developed a program to help you learn what those threats are, the kinds of defenses available, the language of Network Security, & how to organize yourself internally to deal with these Network Security issues.  TechNow's Corporate Risk Management course will give your team the fundamental skills & knowledge they need to develop, manage or implement your organization's Information Security Policy.

Attendees to TN-804: Corporate Risk Management will receive TechNow approved course materials and expert instruction.

Dates/Locations:

No Events

Duration: 4 Days

Course Objectives:

  • Day 1
    • Chapter 1: What is information Security
    • Chapter 2: Types of Attacks
    • Chapter 3: Information Security Services
    • Chapter 4: Legal Issues
    • Chapter 5: Policy
  • Day 2
    • Chapter 6: Managing Risk
    • Chapter 7: Information Security Process
    • Chapter 8: Information Security Best Practices
    • Chapter 9: Internet Architecture
    • Chapter 10: Virtual Private Networks
  • Day 3
    • Chapter 11: E-Commerce Security Needs
    • Chapter 12: Encryption
    • Chapter 13: Hacker Techniques
    • Chapter 14: Intrusion Detection
  • Day 4
    • Chapter 15: UNIX Security Issues
    • Chapter 16: Windows Security Issues
    • Open Discussion

Prerequisites:

  • Basic computer literacy.

 

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CT-415: CompTIA SecAI+

CompTIA SecAI+ is the first certification in CompTIA’s expansion series, designed to help you secure, govern and responsibly integrate artificial intelligence into your cybersecurity operations. You’ll build the skills to defend AI systems, meet global compliance expectations and use AI to enhance threat detection, automation and innovation—so you can strengthen your expertise and help keep your organization’s systems and data secure.

SecAI+ helps you build practical AI security and automation skills on top of your existing expertise, so you can secure AI deployments, use AI‑assisted security tools with confidence, and stay ready for the next step in your cybersecurity career.

Course Objectives:

  • Apply AI concepts to strengthen your organization’s cybersecurity posture
  • Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure
  • Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations
  • Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries
  • Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI
  • Integrate AI securely into DevSecOps pipelines and enterprise security strategies.

Dates/Locations:

No Events

Prerequisites: Recommended experience: 3–4 years in IT and 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended

SecAI+ (V1) exam objectives summary

     Basic AI concepts related to cybersecurity (17%)

  • Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
  • Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations. 
  • Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.

Securing AI systems (40%)

  • Implement security controls: Protect AI systems, data, and models using robust technical safeguards. 
  • Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures. 
  • Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. 

AI-assisted security (24%)

  • Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation. 
  • Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration. 
  • Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring. 

AI governance, risk, and compliance (19%)

  • Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption. 
  • Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle. 
  • Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.