Auditing AI Guidelines & Frameworks (ML, LLM, Agentic) Essentials Training by Tonex
Organizations race to deploy AI, yet assurance often lags behind innovation. This course equips auditors and technical stakeholders to evaluate AI programs end-to-end—governance, data, models, operations, and outcomes—against leading frameworks including NIST AI RMF, ISO/IEC 42001, and the EU AI Act. You’ll learn to scope audits across ML, LLM/RAG, and agentic systems, evidence control effectiveness, and write defensible findings. Cybersecurity impact is explicit: participants learn to test model and pipeline controls that protect data, resist prompt and tool misuse, and contain failure modes. Robust AI assurance reduces attack surface, improves incident readiness, and enables compliant, secure scale.
Learning Objectives
- Plan risk-based AI audits aligned to NIST AI RMF, ISO/IEC 42001, and EU AI Act
- Evaluate governance, lifecycle, and operational controls across ML, LLM, and agentic systems
- Design evidence-based tests for robustness, safety, privacy, and reliability
- Identify nonconformities, rate risks, and craft pragmatic remediation plans
- Build and reuse an AI control testing library across audit cycles
- Strengthen cybersecurity by validating controls for model abuse prevention, data protection, and secure tooling
Audience
- Internal auditors, IT/IS auditors
- External assessors and consultants
- Compliance officers and risk managers
- Technical leads supporting audits
- Data science and MLOps managers
- Cybersecurity Professionals
Course Modules
Module 1 – AI Audit Basics
- Audit types: governance, technical, compliance, security
- Map criteria to NIST, ISO/IEC 42001, EU AI Act
- Scope boundaries: models, systems, risks
- Materiality, impact, and likelihood rating
- Control objectives, test design, sampling
- Evidence hygiene, traceability, and chain-of-custody
Module 2 – ML Systems Checklist
- Data rights, lineage, consent, and licenses
- Data quality, imbalance, and bias controls
- Development documentation and validation artifacts
- Reproducibility, versioning, and drift testing
- Monitoring, alerts, and human oversight gates
- Model change control and rollback readiness
Module 3 – LLM and RAG Audits
- Prompt governance, policy catalogs, and approval
- RAG ingestion: PII screening and access control
- Indexing, retrieval quality, and logging coverage
- Hallucination, groundedness, and red-teaming tests
- Content safety, jailbreak resistance, rate limits
- Usage analytics, abuse detection, and throttling
Module 4 – Agentic AI Assurance
- Tool inventories and permission boundaries
- Guardrails, constraints, and task scoping
- Decision logging, traces, and explainability
- Boundary condition and failure mode testing
- Evasion, escalation, and tool misuse probes
- Kill-switches, containment, and fallback paths
Module 5 – Execute and Report
- Workpapers, sampling, and interview planning
- Stakeholder walkthroughs: technical and business
- Control testing, results, and cross-validation
- Findings, risk ratings, and remediation design
- Management action plans and ownership
- Closure tracking and verification of fixes
Module 6 – Capstone Audit Practice
- Case review: one ML, one LLM/RAG, one agentic
- End-to-end scoping and risk hypothesis drafting
- Evidence collection, trace mapping, and gaps
- Control tests for governance and operations
- Draft audit report with risk-rated findings
- Debrief, lessons learned, and library updates
Ready to elevate AI assurance with clear, defensible audits that satisfy regulators and strengthen security? Enroll your team in Tonex’s Auditing AI Guidelines & Frameworks Essentials Training and start building a repeatable, high-impact AI audit program today.