AI Control Libraries & Continuous Compliance for ML, LLM & Agents Fundamentals Training by Tonex
Modern AI programs thrive when controls, evidence, and monitoring are engineered in from day one—not bolted on before audits. This course shows practitioners how to design reusable AI control libraries, map them to regulations and standards, and operationalize continuous monitoring. Security and privacy are treated as first-class requirements across data, models, and agentic tools. Impact on cybersecurity includes reducing attack surface through preventive and detective AI controls, improving incident readiness with high-fidelity telemetry, and aligning risk posture with enterprise defense goals. You will leave with patterns, templates, and dashboards you can deploy in the real world.
Learning Objectives
- Build standard AI control libraries for ML, LLM, and agents
- Structure controls across data, model, infra, and operations
- Map controls to NIST AI RMF, ISO/IEC 42001, and EU AI Act
- Define metrics, KRIs, and evidence for audit readiness
- Design continuous control monitoring and dashboards
- Integrate governance into delivery pipelines and MLOps
- Strengthen cybersecurity by embedding guardrails, privacy controls, and resilient logging across the AI lifecycle
Audience
- AI and ML Engineers
- Data Scientists and MLOps Leads
- AI Product Managers
- Risk and Compliance Officers
- Governance, Risk and Assurance Teams
- Cybersecurity Professionals
Course Modules
Module 1 – Control Library Foundations
- Control taxonomy and scope
- Data, model, infra layers
- Preventive vs detective controls
- Evidence and test procedures
- Ownership and RACI patterns
- Versioning and lifecycle management
Module 2 – ML Controls Catalog
- Data lineage and provenance
- Training data quality gates
- Model validation and testing
- Bias, drift, and stability checks
- Model registry and approvals
- Deployment and rollback controls
Module 3 – LLM and RAG Controls
- Prompt, context, output policies
- PII minimization and redaction
- Hallucination and toxicity guardrails
- Retrieval governance and caching
- Conversation logging and retention
- Safety incident triage workflows
Module 4 – Agentic AI Controls
- Tool permissioning and scopes
- Sandboxing and egress limits
- Human-in-the-loop escalation
- Capability discovery and gating
- Action audit trails and replay
- Third-party tools and secrets
Module 5 – Control Mapping and Standards
- NIST AI RMF function alignment
- ISO/IEC 42001 requirement links
- EU AI Act article mapping
- Risk classification and tiers
- Policy statements and exceptions
- Evidence catalogs and attestations
Module 6 – Metrics and Continuous Monitoring
- KRIs, KPIs, and thresholds
- Control effectiveness scoring
- Telemetry schema and events
- Dashboards for roles and tiers
- Alert routing and runbooks
- Review cadences and reporting
Ready to operationalize AI assurance without big-bang audits each year? Enroll now to build durable control libraries, clear mappings to major frameworks, and dashboards that keep leadership informed and auditors satisfied.