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Description
AI security is no longer optional. Modern LLMs, RAG pipelines, agents, vector databases, and AI powered tools introduce entirely new attack surfaces that traditional cybersecurity does not cover. Organizations face prompt injection, data leakage, model exploitation, unsafe tool calls, drift, misconfiguration, and unreliable governance.This course gives you a complete, practical, architecture driven guide to securing real GenAI systems end to end. No fluff, no theory for theory’s sake. Only actionable engineering practices, proven controls, and real world templates.
What this course delivers
A full AI security blueprint , including:
AI Security Reference Architecture for model, prompt, data, tools, and monitoring layers
The complete GenAI threat landscape and how attacks actually work
AI firewalls, runtime guardrails, policy engines, and safe tool execution
AI SDLC workflows: dataset security, red teaming, evals, versioning
RAG data governance: ACLs, filtering, encryption, secure embeddings
Access control and identity for AI endpoints and tool integrations
AI SPM: asset inventory, drift detection, policy violations, risk scoring
Observability and evaluation pipelines for behavior, quality, and safety
What you gain
You get practical, ready to use artifacts , including:
Reference architectures
Threat modeling worksheets
Security and governance templates
RAG and AI SDLC checklists
Firewall evaluation matrix
End to end security control stack
A 30, 60, 90 day implementation roadmap
Why this course stands out
Focused entirely on real engineering and real security controls
Covers the full AI stack , not just prompts or firewalls
Gives you tools used by enterprises adopting GenAI today
Helps you build expertise that is rare, in demand, and highly valued
If you want a structured, practical, and complete guide to securing LLMs and RAG systems, this course gives you everything you need to design defenses, implement controls, and operate AI safely in production. This is the roadmap professionals use when they need to secure real AI systems the right way.
- AI Security Reference Architecture for model, prompt, data, tools, and monitoring layers
- The complete GenAI threat landscape and how attacks actually work
- AI firewalls, runtime guardrails, policy engines, and safe tool execution
- AI SDLC workflows: dataset security, red teaming, evals, versioning
- RAG data governance: ACLs, filtering, encryption, secure embeddings
- Access control and identity for AI endpoints and tool integrations
- AI SPM: asset inventory, drift detection, policy violations, risk scoring
- Observability and evaluation pipelines for behavior, quality, and safety
- Reference architectures
- Threat modeling worksheets
- Security and governance templates
- RAG and AI SDLC checklists
- Firewall evaluation matrix
- End to end security control stack
- A 30, 60, 90 day implementation roadmap
- Focused entirely on real engineering and real security controls
- Covers the full AI stack , not just prompts or firewalls
- Gives you tools used by enterprises adopting GenAI today
- Helps you build expertise that is rare, in demand, and highly valued
Who this course is for:
- Software developers building or integrating AI features
- ML and AI engineers working with LLMs or RAG pipelines
- Architects designing secure AI driven systems
- Data engineers and data scientists handling AI datasets
- Security engineers and DevSecOps teams supporting AI workloads
- Technical leads and managers responsible for AI adoption and risk management
