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)

 

 

 

Course Overview:

TN-575: Open Source Network Security Monitoring teaches students how to deploy, build, and run an NSM operation using open source software and vendor-neutral tools. No network is bullet proof and when attackers access your network, this course will show you how to build a security net to detect, contain, and control the attacker. Sensitive data can be monitored and deep packet and deep attachment analysis can be achieved. As organizations stand up a Security Operations Center (SOC) the enterprise NSM is the key ingredient to that SOC. This course not only teaches how to implement an NSM technologically, but how to effectively monitor an enterprise operationally. You will learn how to architect an NSM solution: where to deploy your NSM platforms and how to size them, stand-alone or distributed, and integration into packet analysis, interpret evidence, and integrate threat intelligence from external sources to identify sophisticated attackers. A properly implemented NSM is integral to incident response and provides the responders timely information to react to the incident. TN-575: Open Source Network Security Monitoring is a lab intensive environment with a cyber range that gives each student in-depth knowledge and practical experience monitoring live systems to include: Cisco, Windows, Linux, IoT, and Firewalls.

Attendees to TN-575: Open Source Network Security Monitoring class will receive TechNow approved course materials and expert instruction.

This Course is taught utilizing Security Onion or RockNSM as specified by the customer.

Dates/Locations:

No Events

Duration: 5 Days

Course Objective:

The focus of this course is to present a suite of Open Source security products integrated into a highly functional and scalable Network Security Monitoring solution.

Prerequisites:

Students should have a basic understanding of networks, TCP/IP and standard protocols such as DNS, HTTP, etc. Some Linux knowledge/experience is recommended, but not required

Course Outline:

  • Network Security Monitoring (NSM) Methodology
  • High Bandwidth Packet Capture Challenges
  • Installation of Security Onion
    • Use Cases (analysis, lab, stand-alone, distributed)
    • Resource Requirements
  • Configuration
    • Setup Phase I – Network Configuration
    • Setup Phase 2 – Service Configuration
    • Evaluation Mode vs. Configuration Mode
    • Verifying Services
  • Security Onion Architecture
    • Configuration Files and Folders
    • Network Interfaces
    • Docker Environment
    • Security Onion Containers
  • Overview of Security Onion Analyst Tools
    • Kibana
    • CapME
    • CyberChef
    • Squert
    • Sguil
    • NetworkMiner
  • Quick Review of Wireshark and Packet Analysis
    • Display and Capture Filters
    • Analyze and Statistics Menu Options
    • Analysis for Signatures
  • Analyzing Alerts
    • Replaying Traffic
    • 3 Primary Interfaces:
      • Squert
      • Sguil
      • Kibana
    • Pivoting Between Interfaces
    • Pivoting to Full Packet Capture
  • Snort and Surricata
    • Rule Syntax and Construction
    • Implementing Custom Rules
    • Implementing Whitelists and Blacklists
  • Hunting
    • Using Kibana to Slice and Dice Logs
    • Hunting Workflow with Kibana
  • Bro
    • Introduction and Overview
      • Architecture, Commands
    • Understanding and Examining Bro Logs
      • Using AWK, sort, uniq, and bro-cut
    • Working with traces/PCAPs
    • Bro Scripts Overview
      • Loading and Using Scripts
    • Bro Frameworks Overview
      • Bro File Analysis Framework FAF
    • Using Bro scripts to carve out more than files
  • RockNSM ( * If Applicable)
    •  Kafka
      • Installation and Configuration
      • Kafka Messaging
      • Brokers
      • Integration with Bro and FSF
    • File Scanning Framework FSF
      • Custom YARA Signatures
      • JSON Trees
      • Sub-Object Recursion
      • Bro and Suricata Integration
  • Elastic Stack
    • Adding new data sources in Logstash
    • Enriching data with Logstash
    • Automating with Elastalert
    • Building new Kibana dashboards
  • Production Deployment
    • Advanced Setup
    • Master vs Sensor
    • Node Types – Master, Forward, Heavy, Storage
    • Command Line Setup with sosetup.conf
    • Architectural Recommendations
    • Sensor Placement
    • Hardening
    • Administration
    • Maintenance
  • Tuning
    • Using PulledPork to Disable Rules
    • BPF’s to Filter Traffic
    • Spinning up Additional Snort / Suricata / Bro Workers to Handle Higher Traffic Loads

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Course Overview:

The focus of Red Hat OpenStack Administration I: Core Operations for Cloud Operators (CL110) will be managing OpenStack using both the web-based dashboard and the command-line interface, in addition to managing instances and installing a proof-of-concept environment using Red Hat OpenStack Platform (RHOSP) director. Essential skills covered in the course include configuring Red Hat OpenStack Platform (using the director UI); managing users, projects, flavors, roles, images, networking, and block storage; setting quotas; and configuring images at instantiation.

Attendees to CL-115 Red Hat OpenStack Administration I: Core Operations for Cloud Operators will receive TechNow approved course materials and expert instruction.

Dates/Locations:

No Events

Duration: 5 Days

Prerequisites:

This course is designed for Linux system administrators, cloud administrators, and cloud operators interested in, or responsible for, maintaining a private or hybrid cloud.

Prerequisites for this course is Red Hat Certified System Administrator (RHCSA) or demonstrate equivalent experience

Course Outline:

  • Launch an instance
  • Manage projects, quotas, and users
  • Manage networks, subnets, routers, and floating IP adresses
  • Create and manage block and object storage in the OpenStack framework
  • Customize instances with cloud-init
  • Deploy scalable stacks
  • Deploy RedHat OpenStack Platform using RHOSP director

 

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