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Guest Post

How network engineers can prepare for the future with AI

The rapid rise of AI has left some professionals feeling unprepared. GenAI is beneficial to networks, but engineers must have the proper tools to adapt to this new change.

What if AI is an ally and not a threat?

While AI is not new, generative AI (GenAI) tools emerged like a whirlwind with the introduction of numerous global products. Competition spurred innovation and unlocked the potential to create products using APIs. AI's versatility makes it useful in many fields, but not everyone embraces it. For some, AI threatens to replace parts of the workforce or make them obsolete if they don't adapt to this new technology.

Network engineers are adaptable. From the CLI to APIs and now to AI, engineers know how and when to adopt technologies to keep their networks up to date. To prepare networks for the future with AI, network engineers must understand AI's potential and meet some prerequisites. These steps prepare the engineers and enable networks to handle massive AI workloads.

A new ally

Security operations center (SOC) teams sometimes rely on manual processes and analysis for threat hunting. Integrating AI and automation into these processes limits time-consuming tasks and simplifies operations. Teams can then focus on critical tasks that might evade AI tools. GenAI can further provide suggestions to the team before they make decisions. Integrating AI into threat detection can be a good experience for teams hesitant to work with AI as a tool.

AI can also augment network engineering tasks. AI performs some tasks quickly via automated processes overseen by professionals. The remaining tasks require human touch and expertise for completion. Engineers should consider using AI to accomplish the following goals:

But AI isn't perfect, and it's not a replacement for workforces. As professionals work with AI and provide feedback, AI companies will improve their applications, and AI will evolve. Working with AI now benefits network engineers in the future, as they gain understanding of AI as it evolves, not after.

Prerequisites for AI

Adaptability is key to moving forward in the AI era. AI's versatility enables teams to work closer together than ever before. Working with different teams requires network engineers to understand many different applications and programs.

Network engineers should consider fluency in the following areas if they intend to move to AI:

  • Rest APIs. Knowing the basics is important. Most services on the internet use APIs for software communication. Network engineers and developers use them to manage network devices and sometimes build innovative services around them.
  • Python. Automation is a steppingstone to AI, and Python is the de facto language of automation. Fluency in Python in the AI era is a must, enabling engineers to easily work on many tasks.
  • Data formats. Servers and clients use YAML, XML and JSON for data exchange. Knowing the basics of these formats is a must for anyone wishing to work with network and development teams.
  • Cloud platforms. Multi-cloud environments enable enterprise customers to choose a platform based on their needs. AWS, Azure and Google Cloud are the major platforms in cloud services, and AI powers some of these services now.
  • AI tools. Use IBM Watsonx, Tabnine and Amazon CodeWhisperer for code autocompletion and Darktrace to detect and respond to threats in real time.
  • AI models. Meta's Llama, Google's Gemini and OpenAI's GPT are some of the most commonly known and used AI models. Learning and understanding how they work helps engineers explore the potential of AI in their networks and software development. Harnessing this potential will lead to more innovative services.


Certifications are a great way to prove AI knowledge as it integrates into more applications and services. The following entry-level certifications are a good starting point for those interested in AI:

  • Cisco Certified DevNet Associate. Implementing network automation is a first step to integrating AI. This certification focuses on network automation and teaches skills in network engineering, software development and application deployment. These skills are invaluable when it comes to cultivating AI skills.
  • Cisco Certified CyberOps Associate. This certification proves technical skills that SOCs need on their teams. Knowing how to work with AI tools and models helps improve workflows with threat detection and response.
  • Nvidia-Certified Associate Generative AI LLMs (NCA-GENL). GenAI and large language model development, integration and maintenance are more important than ever as AI becomes more widely available. NCA-GENL validates these foundational concepts.
  • AWS Certified Cloud Practitioner. This certification is a good investment for beginners looking to become fluent in cloud services. It's useful for understanding the basics of AWS cloud services. Upon completion, practitioners can move to higher levels of certifications and expand their AI knowledge.

The era of AI is upon us. As AI becomes increasingly unavoidable, network engineers must adapt their networks or risk their technology becoming obsolete.

Verlaine Muhungu is a self-taught freelance network technician. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.

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