AI Cybersecurity Solutions: Overview of Applied AI Security

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.