AI Training

Practical workshops that help teams get more value from AI while operating with stronger governance, security, and compliance practices.

Business team using AI tools to improve productivity

AI Productivity

Configure AI tools for better output quality, consistency, and day-to-day execution

This client-facing training focuses on configuring AI productivity tools through custom instructions, system prompts, skills, and extensions. Proper configuration dramatically increases response quality, consistency, and relevance while reducing the need to re-explain context every session.

Syllabus

  • Foundations: How AI chat models process instructions, why default AI behavior is generic and costly, structured prompt frameworks, configuration terminology across platforms, and security and data hygiene in AI configuration.
  • Microsoft Copilot: M365 Copilot Chat custom instructions, GitHub Copilot repository instruction files, Microsoft Copilot Studio, and prompt techniques for Microsoft Graph-grounded queries.
  • Claude / Claude Code: CLAUDE.md, Claude Projects, Claude Code skills, commands, and hooks, plus prompt engineering patterns native to Claude.
  • Google Gemini: Gems, system instructions in the Gemini API, Google Workspace Gemini, and prompt design strategies for Gemini.
  • Perplexity: AI Profile, Spaces, research prompt techniques, and Focus modes for controlling the retrieval corpus.
  • ChatGPT (OpenAI): Custom instructions, GPT Builder, ChatGPT Memory, and the OpenAI Playground for production prompting.
  • Grok (xAI): Custom instructions and persona configuration, the xAI API, DeepSearch and Think, and prompt patterns for real-time X/Twitter data.
  • Cross-Platform Best Practices: Shared prompt libraries, prompt versioning, task-to-tool matching, and measuring productivity gains from AI configuration.
Enterprise AI governance training focused on security and compliance controls

AI Governance

Establish enterprise guardrails for safe, auditable, and compliant AI adoption

This training covers the mechanisms organizations can use to enforce governance and guardrails when prompting AI tools. As generative AI adoption expands across the enterprise, the attack surface for data leakage, policy violations, and regulatory non-compliance grows with it, making these controls essential for IT teams, security practitioners, and governance officers.

The material is designed for IT administrators, security teams, compliance officers, and governance leads responsible for overseeing AI tool use within their organizations. It covers platform-specific safety systems, administrator controls, policy enforcement mechanisms, and cross-platform governance strategies such as content filtering, data loss prevention, acceptable use policy development, and alignment with frameworks including NIST AI RMF and ISO/IEC 42001.

Syllabus

  • Foundations of AI Governance: Why AI governance matters, defense-in-depth for AI systems, governance frameworks overview, and audit logging and incident response.
  • Microsoft Copilot: M365 Copilot admin controls and agent policy, content harm filtering, jailbreak and prompt injection defenses, data residency and compliance certifications, Microsoft Purview integration, GitHub Copilot content exclusions, and enterprise policy hierarchy.
  • Claude / Claude Code: Permission systems, managed settings deployment via MDM and GPO, hooks as governance enforcement controls, sandbox and network controls, and MCP server governance.
  • Google Gemini: Gemini API safety settings and harm categories, Vertex AI enterprise governance controls, and Google Workspace admin controls for Gemini.
  • Perplexity: Spaces access control, data handling and privacy controls, and enterprise plan governance features.
  • ChatGPT / OpenAI: Azure OpenAI content filtering, Prompt Shields and jailbreak detection, ChatGPT Enterprise admin console, and custom GPT governance and usage policies.
  • Grok / xAI: Usage policies and prohibited content, API-level governance and rate controls, and real-time data access governance considerations.
  • Enterprise Governance and Compliance Frameworks: NIST AI Risk Management Framework, ISO/IEC 42001 AI management systems, EU AI Act requirements, and building an AI acceptable use policy.

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