Implementing AI Guidelines & Frameworks for ML, LLM & Agentic Systems Essentials

Implementing AI Guidelines & Frameworks for ML, LLM & Agentic Systems Essentials Training by Tonex

Modern AI programs need more than principles—they need operating playbooks that translate frameworks into day-to-day controls, roles, and evidence. In this 2-day course, participants convert leading standards and regulations into actionable governance across ML, LLM, and agentic systems. You will practice mapping use cases to risk categories, selecting controls, and creating audit-ready artifacts that survive scrutiny. Cybersecurity is addressed throughout: attendees learn how AI risks intersect with identity, data protection, and secure operations. You also design guardrails that reduce prompt leakage, toxic outputs, and exfiltration risks—strengthening overall cybersecurity posture while accelerating compliant delivery.

Learning Objectives

• Map ML, LLM, and agentic AI use cases to NIST AI RMF, ISO/IEC 42001, EU AI Act, and OECD principles
• Design end-to-end governance across data, training, deployment, and monitoring
• Implement technical and organizational controls tailored to ML, LLM, and agentic patterns
• Produce audit evidence including policies, risk registers, DPIAs, model cards, test logs, and monitoring records
• Operationalize RAG controls, guardrails, output filters, and error-mode mitigations
• Apply lifecycle controls that reinforce cybersecurity across identity, data, and runtime environments

Audience

• AI/ML Engineers and Data Scientists
• AI Product Owners and Architects
• MLOps and Platform Engineering Leads
• Governance, Risk, and Compliance Professionals
• Legal and Policy Teams working with AI
• Cybersecurity Professionals

Course Modules

Module 1 – AI governance landscape

• Global frameworks overview and scope
• EU AI Act risk categories and duties
• NIST AI RMF functions at a glance
• ISO/IEC 42001 management system concepts
• Roles, accountability, and decision rights
• Governance workflows and approval gates

Module 2 – System taxonomy basics

• ML versus LLM lifecycle differences
• RAG reference patterns and variants
• Agentic AI tools, memory, autonomy
• Multi-agent orchestration considerations
• Risk deltas across ML, LLM, agents
• Control patterns and design choices

Module 3 – NIST AI RMF in practice

• Govern function policies and committees
• Map function: context, risks, impacts
• Measure function: metrics and tests
• Manage function: mitigations and follow-up
• Tailoring for ML credit risk models
• Tailoring for LLM assistants and agents

Module 4 – ISO/IEC 42001 implementation

• Scope, context, stakeholders, interfaces
• Policy, objectives, roles, responsibilities
• Risk and impact assessment procedures
• Operational controls and process controls
• Documentation, records, and evidence sets
• Internal audit and management review

Module 5 – Controls for ML systems

• Data lineage, quality, and bias testing
• Feature governance and privacy safeguards
• Model documentation and model cards
• Performance, drift, and stability monitoring
• Change control, retraining, and approvals
• Access control, environment hardening, logging

Module 6 – Controls for LLM and RAG

• Prompt policy and prompt library management
• Retrieval controls, PII handling, minimization
• Guardrails, output filtering, policy enforcement
• Hallucination, leakage, jailbreak mitigations
• Evaluation, red teaming, continuous testing
• Incident response, traceability, safe rollback

Ready to operationalize AI governance with evidence your auditors—and your customers—can trust? Enroll your team in the Implementing AI Guidelines & Frameworks for ML, LLM & Agentic Systems Essentials Training by Tonex to turn standards into practical controls, accelerate compliant delivery, and strengthen cybersecurity across your AI portfolio.