AI Security Fundamentals: Risks, Frameworks & Tools
About Course
AI Security Fundamentals: Risks, Frameworks & Tools
Master AI threat modeling, SDLC integration, and compliance for enterprise-grade systems
What you'll learn
- Identify modern GenAI risks and understand how attackers target LLM and RAG pipelines
- Apply a layered AI security design to strengthen every component of an AI application
- Create detailed AI threat models and link each threat to concrete control measures
- Configure AI firewalls and runtime guardrails to manage prompts, responses, and tool actions
- Embed security practices into AI development workflows, including dataset checks and eval automation
- Implement robust identity, authorization, and scoped access for AI endpoints and integrations
- Enforce data governance for RAG systems through access rules, tagging, and secure retrieval patterns
- Use SPM platforms to maintain visibility over models, datasets, connectors, and policy violations
- Build observability pipelines to track prompts, responses, decisions, and model quality metrics
- Assemble a unified AI security strategy and translate it into clear 30, 60, and 90 day actions
Requirements
- Some background in tech, engineering, or system development
- Optional exposure to machine learning concepts or LLM based tools
- Basic understanding of common security practices is a plus
- Ability to interpret high level architecture and process diagrams
- No previous experience with specialized AI security solutions required
Who this course is for
- Developers integrating AI capabilities into existing or new products
- Machine learning engineers maintaining model workflows and RAG systems
- System and cloud architects designing secure AI infrastructures
- Security analysts and DevSecOps teams responsible for safeguarding AI services
- Team leads and decision makers who oversee AI initiatives and compliance requirements
Description
Modern AI applications introduce security challenges that traditional defenses cannot address. LLM based systems, retrieval pipelines, agents, data connectors, and vector databases expose new attack paths that organizations must understand and control. This course gives you a complete, practical, and engineering focused approach to securing GenAI systems across their entire lifecycle.
You will learn how attackers exploit AI models, how sensitive data leaks through prompts and outputs, how RAG pipelines can be manipulated, and how misconfigured tools or connectors expose entire environments. The course shows you how to design secure AI architectures, apply the right controls at the right layers, and build a repeatable security process for any AI powered system.
What this course includes
A detailed AI Security Reference Architecture for models, prompts, data, tools, and monitoring
Full coverage of GenAI threats: injection attacks, data leakage, model misuse, unsafe tools
Practical guardrail design using AI firewalls, filtering, and permissioning
AI SDLC guidance for dataset integrity, evaluations, red teaming, and version control
Data governance for RAG systems: access control, filtering logic, encryption, secure embeddings
Identity and authorization models for AI endpoints and tool integrations
AI Security Posture Management workflows for monitoring risk and drift
Observability pipelines for logging prompts, responses, decisions, and quality metrics
What you get
Architecture blueprints
Threat modeling templates
Governance and policy frameworks
Security checklists for AI SDLC and RAG
Evaluation and firewall comparison matrices
A full AI security control stack
A clear 30, 60, 90 day adoption roadmap
Why this course is valuable
It is built for real engineering and real enterprise environments
It covers the full AI ecosystem instead of focusing on a single control
It provides the exact artifacts professionals need to secure AI systems
It prepares you for one of the most in demand skill sets in modern tech
If you need a practical, structured, and comprehensive guide to securing LLM and RAG applications, this course gives you the tools, knowledge, and processes required to protect AI systems with confidence and to operate them safely at scale.
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