[100% Off] Ai Cybersecurity Solutions: Overview Of Applied Ai Security
Learn to identify, analyze, and mitigate GenAI threats using modern security playbooks
What you’ll learn
- 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
Requirements
- 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
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:
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AI Security Reference Architecture for model, prompt, data, tools, and monitoring layers
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The complete GenAI threat landscape and how attacks actually work
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AI firewalls, runtime guardrails, policy engines, and safe tool execution
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AI SDLC workflows: dataset security, red teaming, evals, versioning
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RAG data governance: ACLs, filtering, encryption, secure embeddings
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Access control and identity for AI endpoints and tool integrations
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AI SPM: asset inventory, drift detection, policy violations, risk scoring
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Observability and evaluation pipelines for behavior, quality, and safety
What you gain
You get practical, ready to use artifacts, including:
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Reference architectures
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Threat modeling worksheets
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Security and governance templates
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RAG and AI SDLC checklists
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Firewall evaluation matrix
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End to end security control stack
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A 30, 60, 90 day implementation roadmap
Why this course stands out
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Focused entirely on real engineering and real security controls
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Covers the full AI stack, not just prompts or firewalls
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Gives you tools used by enterprises adopting GenAI today
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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.
