Generative AI security is about protecting thinking systems, not just code. As AI models and agents make decisions, use tools, and access real data, the attack surface shifts from inputs and APIs to prompts, context, memory, and autonomy.
A career in GenAI security focuses on securing AI models, agent workflows, data pipelines, and outputs against risks like prompt injection, data leakage, over-privileged agents, RAG poisoning, and model misuse.
Common roles include AI Security Engineer, AI Security Researcher, AI Security Analyst, Governance & Risk Specialist, and AI Security Consultant. Across these roles, responsibilities typically involve threat modeling AI systems, designing secure architectures, preventing model abuse, monitoring AI behavior, and responding to AI-driven incidents.
To kickstart: • Build strong foundations in Python, application security, cloud, and AI basics • Learn AI-specific threats • Get hands-on by building and breaking LLM apps • Follow real-world standards like OWASP LLM Top 10 and MITRE ATLAS • GenAI security isn’t about being an AI expert first — it’s about security thinking applied to AI-powered systems.
How are you approaching GenAI security today — and what skills or challenges do you think matter most as AI systems become more agentic and autonomous?
Feedback, additions, or counterpoints are very welcome. Let’s learn from each other. 👇
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