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

Foundations of Secure Local AI

• The significance of local and on-premises AI within regulated sectors
• Comparing cloud AI with internal deployments for sensitive workloads
• Typical enterprise applications for private assistants and workflow assistance
• Essential elements of a secure local AI architecture

Basics of Ollama and Open Models

• The role of Ollama in a local development stack
• Locally pulling, executing, and managing models
• Selecting models based on size, quality, hardware requirements, and licensing
• Aligning model capabilities with specific business needs

Preparing the On-Premises Environment

• Preparing hosts, workstations, and servers
• Installing and setting up Ollama for local inference
• Utilizing containers and internal development tools
• Validating API access and ensuring basic operational readiness

Effective Use of Local Models

• Executing prompts and refining outputs through system instructions
• Reusing templates for consistent enterprise tasks
• Managing model versions and internal artifacts
• Basic performance optimization for CPU and GPU setups

Developing Practical Agentic Workflows

• Defining what constitutes an agentic workflow in a controlled context
• Simple patterns for planning, tool utilization, and response loops
• Designing task-oriented assistants for internal operations
• Incorporating human review, fallback mechanisms, and error handling

Private Retrieval Workflows

• Fundamentals of Retrieval-Augmented Generation (RAG) for accessing internal knowledge
• 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 limits and privacy implications
• Access control, logging, and audit capabilities
• Prompt safety, output controls, and guardrails
• Governance milestones 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 standards
• 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 model
• Implementing retrieval over approved internal documents
• Introducing simple agentic actions and safety controls
• Reviewing deployment, operational, and governance checkpoints

Adoption Planning and Next Steps

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