I have about seven years of experience as a Network and Security Engineer, working with Fortinet products such as FortiGate, FortiAnalyzer, and FortiAuthenticator, along with Cisco, VPNs, high availability, Linux, VMware, Hyper-V, and enterprise infrastructure. I'd like to specialize in applying AI to cybersecurity, especially SOC operations, network defense, security automation, LLMs, and AI agents. I'm not trying to become a full-time data scientist. If you were starting from my position, what learning roadmap would you follow? Which learning platforms are worth paying for, what monthly budget is realistic, and which certifications offer the best return? I'd also appreciate suggestions for practical projects that could strengthen my résumé, along with advice on what learning approaches to avoid.
1 Answer
Your existing networking and security background is a strong foundation, but make sure it includes hands-on troubleshooting rather than only managing products through their interfaces. TCP/IP fundamentals, packet captures with tcpdump and Wireshark, Linux, authentication, logging, and incident investigation will be especially valuable when working with AI in a SOC or network-security setting. Cybersecurity is broad, and networking and security may be separate teams in many organizations, so focus on building the overlap instead of worrying about the exact job title. From there, learn Python and APIs, automation, SIEM data pipelines, prompt design, model limitations, and how to evaluate AI-generated results safely. Small projects—such as enriching alerts with threat-intelligence data, building a log-triage assistant, or creating a lab that tests an AI agent against simulated incidents—will usually demonstrate more practical ability than collecting many unrelated certifications.
That distinction is important: plenty of organizations split networking and security into separate teams, even though strong security work still depends on understanding how traffic and systems behave.