[100% Off] Securing Ai Applications: From Threats To Controls
Learn how to defend generative AI systems using firewalls, SPM, and data governance tools
What you’ll learn
- Examine how GenAI systems expand the attack surface across models
- data
- and tools
- Use an end to end AI security architecture to map protections onto each subsystem
- Develop comprehensive threat scenarios for LLM based applications and choose fitting safeguards
- Deploy guardrail frameworks and policy engines to control user inputs and model outputs
- Integrate security gates into AI delivery processes
- covering data validation and model assessments
- Set up authentication flows
- permission boundaries
- and controlled tool capabilities for AI services
- Apply data protection practices to RAG pipelines
- including filtering
- encryption
- and structured access
- Operate AI SPM solutions to track assets
- detect misconfigurations
- and monitor system drift
- Build monitoring pipelines that capture queries
- responses
- tool usage
- and evaluation metrics
- Design a full AI security control map and plan actionable rollout steps for organizational adoption
Requirements
- Basic understanding of software development or IT systems
- Familiarity with AI concepts such as LLMs or RAG is helpful but not required
- General knowledge of cybersecurity principles is beneficial
- Ability to read technical diagrams and system architectures
- No prior experience with AI security tools or frameworks needed
Description
AI systems introduce security challenges that are fundamentally different from anything traditional cybersecurity was built to handle. LLM applications, retrieval pipelines, vector databases, and agent based automations create new vulnerabilities that can expose sensitive data, enable unauthorized actions, and compromise entire workflows. This course gives you a complete and practical framework for securing GenAI systems in real engineering environments.
You will learn how modern AI threats operate, how attackers exploit prompts, tools, and connectors, and how data can leak through embeddings, retrieval layers, or model outputs. The course walks you through every layer of the AI stack and shows you how to apply the right defenses at the right places, using a structured and repeatable security approach.
What you will learn
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The full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layers
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How GenAI attacks work, including injection, leakage, misuse, and unsafe tool execution
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How to use AI firewalls, filtering engines, and policy controls for runtime protection
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AI SDLC best practices for dataset security, evaluations, red teaming, and version management
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Data governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddings
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Identity and access patterns that protect AI endpoints and tool integrations
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AI Security Posture Management for risk scoring, drift detection, and policy enforcement
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Observability and evaluation workflows that track model behavior and reliability
What is included
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Architecture diagrams and control maps
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Model and RAG threat modeling worksheets
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Governance templates and security policies
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Checklists for AI SDLC, RAG security, and data protection
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Evaluation and firewall comparison frameworks
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A complete AI security control stack
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A step by step 30, 60, 90 day rollout plan for teams
Why this course is essential
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It focuses on practical security for real AI deployments
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It covers every critical layer of modern LLM and RAG systems
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It delivers ready to use tools and artifacts, not theory
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It prepares you for one of the fastest growing and most demanded areas in tech
If you need a structured and actionable guide to protecting AI systems from modern threats, this course provides everything required to secure, govern, and operate GenAI at scale with confidence.
