AI Cybersecurity Solutions: Overview of Applied AI Security
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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.
Intro level understanding of how modern applications or cloud systems work
Optional familiarity with machine learning or LLM based tools
Some exposure to security fundamentals is useful but not mandatory
Comfort with technical documentation and architectural schematics
No background in AI security or specialized tooling required
Understand the full GenAI threat landscape and how modern attacks target LLMs and RAG systems
Apply the AI Security Reference Architecture to design secure AI applications
Perform threat modeling for GenAI systems and map risks to concrete mitigations
Implement AI firewalls, filtering rules, and runtime protection controls
Build a secure AI SDLC with dataset security, evals, and red-teaming practices
Configure identity, access, and permission models for AI tools and endpoints
Apply data governance techniques for RAG pipelines, embeddings, and connectors
Use SPM platforms to monitor drift, violations, and AI asset inventory
Deploy observability and evaluation tooling to track model behavior and quality
Assemble an end-to-end AI security control stack and build a 30/60/90 day roadmap
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
