Enterprise AI Security Architecture: Protecting AI Apps
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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
A full breakdown of the AI Security Reference Architecture
Real world GenAI threats: prompt injection, data exposure, model exploitation
AI firewalls, guardrails, filtering engines, and safe tool permission models
AI SDLC practices: provenance, evaluations, red teaming, versioning
Data governance for RAG pipelines: ACLs, filtering, encryption, secure embeddings
Identity and access patterns for AI endpoints and tool integrations
AI Security Posture Management: asset inventory, risk scoring, drift detection
Observability, telemetry, and evaluation workflows for production AI
What you receive
Architecture diagrams
Threat modeling templates
Security and governance policies
AI SDLC and RAG security checklists
Evaluation and firewall comparison matrices
A complete AI security control stack
Practical rollout plan for the first 30, 60, and 90 days
Why this course matters
It is practical, not theoretical
It focuses on real AI attack surfaces, not generic cybersecurity
It gives you the frameworks, controls, and artifacts needed to secure enterprise AI
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.
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
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
Engineers and developers creating applications powered by LLMs
ML practitioners and data specialists working with model pipelines
Solution architects defining AI system structures and security controls
Cybersecurity and DevSecOps teams overseeing AI deployments
Technical leaders aiming to manage AI risk and governance in their organizations
