Secure Generative AI and LLM Deployment
26mIntermediate2026-04-14
Authors
Caroline Wong
Vice President of Cobalt.io
Course details
Deploying generative AI systems securely within an organization requires an understanding of LLM architecture, common vulnerabilities, and practical defenses like prompt sanitization and secret management. This course provides simple, guided labs so you can practice securing data, detecting risks, and implementing guardrails that align with enterprise policies. Discover how to build, monitor, and govern internal ChatGPT-style deployments with confidence and compliance. Whether you work in IT, engineering, cybersecurity, system administration, or DevOps, you can gain knowledge and hands-on skills apply today and prepare for the future.
Learning objectives
Describe the core architecture and deployment models for internal generative AI systems, including how data flows and how security boundaries interact.
Implement key security controls—such as input sanitization, secret management, and role-based access—to protect internal LLM applications from common threats.
Analyze risks and incidents related to prompt injection, data leakage, and misuse, and apply effective monitoring and response strategies.
Create practical governance artifacts—such as model cards, AI SBOMs, and deployment checklists—to ensure compliance and accountability in enterprise AI environments.
Learning objectives
Describe the core architecture and deployment models for internal generative AI systems, including how data flows and how security boundaries interact.
Implement key security controls—such as input sanitization, secret management, and role-based access—to protect internal LLM applications from common threats.
Analyze risks and incidents related to prompt injection, data leakage, and misuse, and apply effective monitoring and response strategies.
Create practical governance artifacts—such as model cards, AI SBOMs, and deployment checklists—to ensure compliance and accountability in enterprise AI environments.
Concepts
Introduction
- Introduction to secure generative AI and LLM deployment
Why Secure Generative AI and LLM Deployment Matters
- The rise of internal ChatGPTs
- The enterprise risk shift
Architecture and Deployment Models
- How internal LLMs are built
- Build vs. buy vs. hybrid
Prompt Security and Guardrails
- What is prompt injection
Data Protection and Access Control
- Securing sensitive inputs
Monitoring, Logging, and Detection
- Observability for LLMs
Governance and Compliance
- Frameworks that matter (NIST AI RMF, ISO IEC 42001, SOC 2)
- Documentation and AI SBOM basics
Secure Deployment Checklist
- Next-gen defenses - Autonomous guardrails and AI agents