Learn
Thirteen skills that AI security postings ask for, ordered by how many of the 48 postings name them. Each has free resources by level, and where researched, a sourced brief with concepts, exercises and practice questions.
Optional paid courses and certificationsLLM security: prompt injection, jailbreaks, output handling
Understanding how attackers manipulate LLM inputs and outputs, and how to design applications that resist prompt injection, jailbreaks, and unsafe output handling.
Python and coding for AI security work
Enough Python to build a small LLM or agent app, call model APIs, write tests against them and automate attacks or checks; 67 percent of the 48 postings ask for it.
Agent and agentic system security: tool use, permissions, memory, multi-agent
Securing AI systems that plan, call tools, hold memory, and act semi-autonomously, often through multiple cooperating agents.
Securing model and agent workloads on cloud platforms
Applying cloud security controls, identity boundaries and monitoring to model and agent workloads on AWS, Azure and GCP.
AI red teaming and adversarial testing
Probing AI systems, especially LLMs, for exploitable weaknesses using adversary tactics and structured attack testing.
AI system threat modelling and secure architecture
Mapping how an AI or agentic system can be attacked and designing the architecture and controls to reduce that risk.
Application security for LLM-backed software
Applying secure design, review and testing practices to applications that embed LLMs, agents or RAG pipelines.
AI governance, risk and compliance: NIST AI RMF, ISO/IEC 42001, EU AI Act, CSA AICM
Applying formal risk management, management-system, and regulatory frameworks to how an organisation builds, buys, and operates AI.
RAG pipeline security: retrieval, vector stores, data poisoning
Securing the retrieval side of an LLM application, including the vector database, the documents fed into it, and the ways an attacker can poison or manipulate what gets retrieved.
Model Context Protocol security: servers, tool poisoning, auth
Securing the client-server protocol that lets AI agents discover and call external tools, including malicious or compromised tool servers.
Advisory and customer-facing delivery skills
Explaining AI risk and controls to clients and leadership, and delivering advisory work they can act on.
Identity and least privilege for agents and tools
Scoping identities, tokens and permissions so agents and the tools they call can only do what they need to.
Detection, logging and response for AI systems
Knowing what an attack on an LLM or agent looks like in logs, which signals to collect, and how to respond when a model or agent misbehaves.