Hands-on training in AI security, agentic systems, and LLM governance to become a Principal AI Security Engineer.
Sub Category
- Network & Security
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Objectives
- Explain how AI security differs from traditional application security and identify risks unique to LLMs, RAG systems, tool-calling applications, and memory
- Design secure agentic AI architectures using layered controls, trust boundaries, least privilege, human approvals, and policy enforcement.
- Identify, execute, and defend against prompt injection, jailbreaks, document poisoning, memory poisoning, parameter injection, and unauthorized agent actions.
- Build secure AI applications that include chat interfaces, RAG pipelines, tool calling, persistent memory, and autonomous agents.
- Implement prompt validation, input sanitization, risk scoring, output filtering, context isolation, trusted-source validation, and AI guardrails.
- Create an integrated AI Security Gateway that protects prompts, retrieved content, tools, memory, model responses, and agent workflows.
- Establish enterprise AI governance using inventories, ownership models, lifecycle controls, risk assessments, approval gates, policies, and evidence management.
- Build governance dashboards for AI usage, cost, risk, model evaluation, drift, prompt quality, sensitive-data exposure, incidents, and human oversight.
- Map operational AI controls to frameworks and standards including NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Communicate AI security risks, control gaps, remediation priorities, and governance recommendations to executives, engineers, risk teams, auditors and others
Pre Requisites
- No previous AI security or governance experience is required; the course begins with foundational concepts.
- Basic familiarity with computers, software applications, and internet technologies will be helpful.
- Beginner-level Python knowledge is recommended, but the practical exercises are explained step by step.
- A computer capable of running Python, Visual Studio Code, and the course project files is required.
- Students should be comfortable installing software and running commands in a terminal or command prompt.
- Basic awareness of APIs, databases, cloud applications, or cybersecurity concepts is useful but not mandatory.
- Docker is introduced for packaging the final secure AI application; prior Docker experience is not required.
- An interest in AI security, generative AI, risk management, compliance, or enterprise governance is the most important prerequisite.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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