AI Risk Governance & Responsible AI Management: Frameworks, Controls, and Oversight for AI Initiatives

Course Description

Duration 2 days – 14 hrs

Overview

This standard training equips leaders, product/tech teams, risk/compliance, and internal audit with a practical governance playbook for managing AI initiatives end-to-end—covering risk appetite, accountability, lifecycle controls, third-party/vendor risk, monitoring, and assurance.

The course is anchored on widely used governance/risk references such as:

NIST AI Risk Management Framework (AI RMF 1.0) with its GOVERN–MAP–MEASURE–MANAGE functions.
ISO/IEC 42001:2023 (AI management system) for establishing an AI governance management system.
ISO/IEC 23894:2023 (AI risk management guidance).
Regulatory awareness elements, including EU AI Act risk-based obligations (for organizations with EU exposure) and Philippines NPC Advisory No. 2024-04 for AI systems processing personal data (privacy governance).

Objectives

Define an AI governance operating model (decision rights, committees, roles, and accountability).
Build an AI use-case intake and approval process with risk-based gating (what needs EDD/impact assessment vs fast-track).
Apply practical AI risk concepts: bias/fairness, transparency, privacy/security, robustness, model drift, third-party risk, and incident response.
Use recognized frameworks (NIST AI RMF / ISO 42001 / ISO 23894) to design controls across the AI lifecycle.
Establish monitoring, KPIs/KRIs, documentation, audit trails, and reporting dashboards for management and board oversight.
Incorporate privacy and regulatory guardrails for AI handling personal data (PH NPC Advisory + Data Privacy Act; plus global awareness).

Target Audience

Executives / business owners sponsoring AI initiatives
Product owners, project/program managers, innovation leads
Data/AI/ML teams, architecture, cybersecurity
Risk management, compliance, legal, privacy/DPO office
Procurement/vendor management
Internal audit / assurance teams

Prerequisites

No AI technical background required

Helpful: familiarity with your org’s risk management process, SDLC/Change management, and vendor onboarding

Course Outline

Day 1 — Governance foundations and frameworks

Module 1: AI initiative risks and why governance fails

Typical AI failures: unmanaged use cases, “shadow AI,” weak accountability, poor data practices
What “good” oversight looks like (3 lines of defense + product ownership)

Module 2: AI governance operating model

Board/Exec oversight, AI Steering Committee, model owners, validators, risk/compliance, audit
RACI and decision rights (approve, monitor, stop, escalate

Module 3: Frameworks you can map to your org

NIST AI RMF (GOVERN, MAP, MEASURE, MANAGE)
ISO/IEC 42001 (AI management system approach)
ISO/IEC 23894 (AI risk management guidance)

Workshop A: AI use-case intake + classification

Teams classify 3 sample AI initiatives (customer-facing, internal, decisioning) and define required governance gates

Module 4: Policies and minimum control standards

Policy set: acceptable use, data governance, human oversight, documentation, vendor controls, incident reporting
Required artifacts: model cards, data sheets, risk assessments, change logs

Day 2 — Controls across the lifecycle + monitoring + assurance

Module 5: Lifecycle controls (build–buy–use)

Intake → design → build/configure → test/validate → deploy → monitor → retire
Change management and “re-approval” triggers (data shift, model updates, new decision impact)

Module 6: Risk assessment & impact assessment

Risk taxonomy: privacy, bias, explainability, security, resilience, safety, legal/regulatory
Practical scoring (likelihood/impact), compensating controls, residual risk sign-off
Privacy expectations for AI processing personal data (PH NPC Advisory + DPA principles)

Module 7: Third-party/vendor and GenAI governance

Vendor due diligence checklist: data handling, model IP, security, logging, SLAs, audit rights
Prompt/data leakage risks; approval rules for external GenAI tools

Module 8: Monitoring, metrics, and incident management

KRIs/KPIs: drift, error rates, fairness metrics, privacy incidents, security events
Incident response and governance escalation routes

Module 9: Assurance and audit readiness

Evidence packs and audit trails; testing cadence; independent validation
Regulatory awareness (EU AI Act high-risk concept + obligations if relevant to your footprint)

Workshop B: Build your “AI Governance Starter Pack”