[78% Off] Enterprise Ai Security Architecture: Protecting Ai Apps
Create a full-stack AI defense strategy across model, data, and infrastructure layers
What you’ll learn
- Analyze the unique attack surface of GenAI systems and see how LLMs and RAG apps are exploited
- Use a structured AI security architecture to plan protections across all layers of an AI solution
- Build complete threat models for AI workloads and connect identified risks with practical defenses
- Deploy AI gateways and guardrail engines to filter inputs
- outputs
- and tool executions
- Integrate security into every AI development stage
- including data sourcing
- evaluations
- and safety reviews
- Set up strong authentication
- scoped permissions
- and regulated tool access for AI components
- Govern sensitive data in RAG pipelines with structured policies
- metadata rules
- and controlled retrieval flows
- Operate AI SPM tools to track models
- datasets
- connectors
- and detect risk or drift over time
- Implement logging
- telemetry
- and evaluation pipelines to observe how AI behaves in production
- Construct a complete AI security control stack and define an actionable plan for short and long term adoption
Requirements
- General experience with IT
- software
- or engineering environments
- Helpful but optional familiarity with AI workflows or retrieval systems
- Basic awareness of cybersecurity ideas like access control or data protection
- Ability to follow technical explanations and architectural breakdowns
- No prior hands on work with AI security platforms or evaluations needed
Description
AI systems introduce risks that traditional security cannot handle. LLM powered applications, retrieval pipelines, agents, vector databases, and tool integrations open new vulnerabilities that organizations struggle to understand and control. This course gives you a complete, practical, end to end framework for securing real GenAI workloads in production environments.
You will learn how modern AI attacks actually work, how to map threats across every layer of an LLM or RAG system, and how to implement controls that prevent data leakage, prompt manipulation, unsafe tool execution, and misconfigured connectors. The course is fully aligned with the way enterprises deploy and operate AI today, combining architecture, security engineering, data governance, and monitoring into one unified approach.
What this course covers
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A full breakdown of the AI Security Reference Architecture
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Real world GenAI threats: prompt injection, data exposure, model exploitation
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AI firewalls, guardrails, filtering engines, and safe tool permission models
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AI SDLC practices: provenance, evaluations, red teaming, versioning
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Data governance for RAG pipelines: ACLs, filtering, encryption, secure embeddings
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Identity and access patterns for AI endpoints and tool integrations
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AI Security Posture Management: asset inventory, risk scoring, drift detection
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Observability, telemetry, and evaluation workflows for production AI
What you receive
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Architecture diagrams
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Threat modeling templates
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Security and governance policies
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AI SDLC and RAG security checklists
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Evaluation and firewall comparison matrices
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A complete AI security control stack
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Practical rollout plan for the first 30, 60, and 90 days
Why this course matters
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It is practical, not theoretical
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It focuses on real AI attack surfaces, not generic cybersecurity
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It gives you the frameworks, controls, and artifacts needed to secure enterprise AI
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It prepares you for the growing demand for engineers who understand AI security at depth
If you need a focused, well structured, and actionable guide to securing modern AI systems, this course gives you everything required to build, defend, and operate safe and reliable GenAI applications from day one.
