AI+ Network TM

Network Engineers & Architects: Automate, secure, and optimize networks using AI

Cybersecurity Professionals: Detect and mitigate threats with intelligent algorithms

IT Professionals: Gain cross-domain expertise in networking, AI, and DevOps

Telecom & Cloud Specialists: Build AI-driven infrastructure for scalable digital ecosystems

Students & Technologists: Prepare for next-gen roles in intelligent network management

AI-Driven Network Automation:

Automate configurations, routing, and monitoring using intelligent AI-based logic.

AI-Powered Network Security:

Implement real-time threat detection and response using predictive analytics.

Network Optimization with AI:

Use machine learning to improve bandwidth usage, reduce latency, and scale with demand.

Incident Response and Cybersecurity Intelligence:

Apply AI tools for breach detection, incident forensics, and mitigation strategies.

- Basic understanding of networking fundamentals.

- Familiarity with programming languages such as Python.

- Fundamental knowledge of AI and machine learning concepts.

- Experience with network management tools and technologies is a plus.

- There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTS Authorized Training Partners (ATPs).

Module 1: Networking Foundations

• 1.1 Basic Networking Concepts: A refresher on the fundamental principles of networking, including the OSI model, TCP/IP, IP addressing, and subnetting.
• 1.2 Networking Protocols and Standards: A deep dive into essential protocols such as HTTP, DNS, DHCP, and standards like Ethernet and Wi-Fi.
• 1.3 Network Infrastructure and Design: An overview of network components (routers, switches, firewalls) and the principles of designing scalable and reliable network architectures.
• 1.4 Introduction to Network Security: A foundational look at network security, including firewalls, intrusion detection systems (IDS), and access control lists (ACLs).

Module 2: Advanced Networking Technologies

• 2.1 Network Virtualization and Cloud Networking: Explore how technologies like Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are changing network management, and how networking is handled in public cloud environments.
• 2.2 Emerging Network Architectures: A forward-looking discussion on new network architectures like intent-based networking (IBN) and their role in enabling automation and AI integration.
• 2.3 Advanced Routing and Switching: A deeper look at complex routing protocols like BGP and OSPF, and advanced switching concepts.
• 2.4 Network Storage and Data Centers: An overview of how networks connect and manage data centers and storage solutions.

Module 3: AI in Networking

• 3.1 Introduction to AI and Machine Learning: An accessible introduction to the core concepts of AI and Machine Learning (ML), including supervised and unsupervised learning, and how they apply to networking.
• 3.2 AI-Driven Network Optimization: Learn how AI algorithms analyze network data to identify bottlenecks, optimize routing paths, and dynamically allocate resources to improve performance and efficiency.
• 3.3 AI for Network Security and Threat Detection: Understand how AI is used to enhance network security by detecting anomalies, identifying malware, and responding to threats in real time.
• 3.4 AI-Enhanced Network Management: Explore how AI can automate routine network management tasks, such as configuration, monitoring, and troubleshooting, leading to a more proactive and efficient operational model.

Module 4: Network Automation and Orchestration

• 4.1 Fundamentals of Network Automation: A practical guide to automating repetitive network tasks using tools and scripting languages like Python and Ansible.
• 4.2 AI-Driven Network Orchestration: Learn the difference between automation and orchestration, and how AI can be used to coordinate and manage complex, multi-domain network workflows.
• 4.3 Policy-Driven Network Management: Understand how to define high-level business policies and use AI to automatically translate them into low-level network configurations.
• 4.4 Case Studies in Network Automation: Analyze real-world examples of how organizations have used automation to improve efficiency and reduce human error.

Module 5: AI-Enhanced Network Security

• 5.1 Advanced Threat Detection with AI: A deep dive into using AI for anomaly detection and behavioral analysis to identify sophisticated threats that evade traditional security systems.
• 5.2 Secure Network Design and Architecture: Learn how to design a network that is inherently more secure by integrating AI-driven security tools at every layer.
• 5.3 AI for Cybersecurity Intelligence: Understand how AI can be used to analyze large volumes of threat data, generate actionable insights, and predict future attack vectors.
• 5.4 Ethical Considerations in AI-Driven Security: A critical discussion on the ethical challenges of using AI in security, including data privacy, bias, and the potential for false positives.

Module 6: Practical Labs and Hands-On Projects

• 6.1 Network Simulation and Emulation: Hands-on practice with tools like GNS3 or Cisco CML to build and test network topologies in a virtual environment.
• 6.2 AI-Driven Network Automation Projects: A guided project to use Python and AI libraries to automate a network configuration or management task.
• 6.3 AI for Network Security Projects: A practical project to build a simple anomaly detection model to analyze network traffic and identify suspicious behavior.
• 6.4 Capstone Project (Using Google Colab and Azure cloud): A culminating project where you will design and implement a complete AI-driven networking solution, using a cloud platform like Azure and a tool like Google Colab to train your AI models.

Module 7: Emerging Trends and Future Directions

• 7.1 Future of AI in Networking: A forward-looking discussion on how AI will continue to shape the networking landscape, from self-healing networks to predictive maintenance.
• 7.2 AI-Powered IoT Networks: Explore the unique challenges and opportunities of integrating AI with Internet of Things (IoT) networks, with a focus on data management and security.
• 7.3 Blockchain and AI in Networking: An overview of how blockchain and AI can be combined to create a more secure, transparent, and decentralized network.
• 7.4 Continuous Learning and Career Development: A guide to staying current with the rapid pace of innovation in AI and networking, including resources for further education and professional growth.

Optional Module: AI Agents for Network Management

• 1. Understanding AI Agents: An introduction to the concept of AI agents as autonomous systems that can perform complex, multi-step tasks to achieve a goal.
• 2. Case Studies: Explore real-world examples of AI agents being used for tasks like automating network troubleshooting, optimizing resource allocation, and ensuring cloud security.
• 3. Hands-On Practice with AI Agents: A workshop-style session to experiment with building and deploying simple AI agents for specific network management workflows.