AI Security Engineer is becoming one of the most exciting and misunderstood roles in cybersecurity. It is not simply prompt hacking. It is not just AI governance. And it is certainly not about memorising a list of AI vulnerabilities.
An AI security engineer must understand how AI applications, RAG pipelines, models, agents, tools, identities, data and cloud infrastructure work and how attackers can manipulate them.
In this video, I explain what it genuinely takes to build a career in AI security engineering.
We will discuss: 🔹 What an AI security engineer actually does 🔹 AI engineering versus AI security engineering 🔹 Skills required across AppSec, cloud, IAM, containers and AI 🔹 Prompt injection, data poisoning and sensitive-data exposure 🔹 RAG, agent, tool and memory security 🔹 Excessive agency and insecure tool execution 🔹 A realistic day in the life of an AI security engineer 🔹 How developers, security professionals and beginners can pivot 🔹 Portfolio projects that demonstrate real ability 🔹 Certifications and frameworks worth exploring 🔹 Interview questions and hiring expectations 🔹 A practical six-month learning roadmap
Whether you work in cybersecurity, software development, cloud, machine learning, governance, risk or are just beginning your journey, this video will help you understand where you fit and what to learn next. Subscribe for more practical content on AI security, agentic security, MCP security, application security and secure AI engineering. #AISecurity #AISecurityEngineer #CybersecurityCareers #GenerativeAI #AgenticAI #LLMSecurity #PromptInjection #AIEngineering #AppSec #CloudSecurity #AIJobs #CareerRoadmap #Cybersecurity