Secure Local Agentic AI: On-Prem Ollama Development for Regulated Industries Training Course

Foundations of Secure Local AI

• Understanding local and on-premises AI in the context of regulated industries
• Comparing cloud AI versus internal deployment for sensitive workloads
• Common enterprise use cases for private assistants and workflow support
• Key components of a secure local AI architecture

Ollama and Open Model Essentials

• The role of Ollama within a local development stack
• Techniques for pulling, executing, and managing models locally
• Selecting models based on size, quality, hardware requirements, and licensing
• Aligning model options with practical business tasks

Preparing the On-Premises Environment

• Setup and preparation of hosts, workstations, and servers
• Installing and configuring Ollama for local inference
• Leveraging containers and internal development tooling
• Verifying API access and ensuring basic operational readiness

Effective Use of Local Models

• Executing prompts and refining outputs using system instructions
• Reusing templates to maintain consistency in enterprise tasks
• Managing model versions and internal artifacts
• Basic performance optimization for CPU and GPU deployments

Constructing Practical Agentic Workflows

• Defining agentic workflows within controlled settings
• Simple patterns for planning, tool utilization, and response loops
• Designing task-focused assistants for internal operations
• Incorporating human review, fallback logic, and error handling

Private Retrieval Workflows

• Basics of retrieval-augmented generation for internal knowledge access
• Preparing documents for chunking, indexing, and search
• Connecting a local vector store to an Ollama-based application
• Enhancing relevance and answer quality through improved retrieval patterns

Security, Governance, and Compliance Practices

• Data handling boundaries and privacy considerations
• Implementing access control, logging, and audit support
• Ensuring prompt safety, output controls, and guardrails
• Governance checkpoints for regulated deployment and operation

Enterprise Integration Patterns

• Exposing local AI capabilities via internal APIs
• Integrating assistants with internal applications and services
• Supporting use cases for assistant, batch, and workflow automation
• Maintaining solutions within controlled network boundaries

Evaluating Local AI Solutions

• Assessing quality, reliability, and consistency
• Testing against business, policy, and safety requirements
• Comparing model options for specific enterprise tasks
• Establishing a practical improvement cycle for internal teams

Hands-On Implementation Lab

• Building a private assistant using Ollama and an open-source model
• Implementing retrieval over approved internal documents
• Introducing simple agentic actions and safety controls
• Reviewing deployment, operations, and governance checkpoints

Adoption Planning and Next Steps

• Reviewing key design and deployment decisions
• Identifying common pitfalls in regulated AI projects
• Planning pilot use cases and aligning stakeholders
• Defining a roadmap for secure local AI adoption