Lifecycle Policies, Procedures and Third-Party Risk
This topic focuses on policies that apply across the AI life cycle, including use-case assessment, risk management, ethics by design, data governance, development, testing, deployment, monitoring, documentation, reporting, incidents, and third-party risk.
How to study for IAPP AIGP
Treat each question as a governance decision: identify the AI role and life-cycle stage, classify the legal or risk issue, then choose the control, evidence, and accountability path.
Core concepts
Concept 1
AI policies should cover the full life cycle from use-case intake through design, data, development, testing, deployment, monitoring, documentation, reporting, and incident handling.
Exam cue: Use life-cycle coverage when the scenario asks for governance that persists beyond launch.
Concept 2
Existing privacy, security, data governance, intellectual property, procurement, HR, and acceptable-use policies often need AI-specific updates.
Exam cue: Update existing policies when AI changes data, security, IP, HR, or procurement risk.
Concept 3
Third-party AI risk management should address procurement, contracts, supply chain, vendor obligations, acceptable use, and ongoing assurance.
Exam cue: Use contracts and assessments when the organization relies on a vendor or external model.
Risk pitfalls and guardrails
Approving a use case without monitoring, documentation, or incident procedures.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Treating vendor AI output as risk-free because it is purchased externally.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Creating an AI policy that conflicts with privacy, security, or data governance policies.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Memory anchors
Use-Case Intake
Use-case intake captures purpose, stakeholders, data, risk level, and approval needs before AI work proceeds.
Ethics by Design
Ethics by design embeds responsible AI principles into requirements, development, and controls.
Incident Management
Incident management defines how AI issues are reported, investigated, remediated, and documented.
Third-Party Risk
Third-party risk is the exposure created by external AI providers, models, data, or services.
Acceptable Use
Acceptable use defines permitted and prohibited ways to use AI systems.
Procurement Review
Procurement review evaluates vendor terms, risks, controls, and governance obligations before purchase.
Policy Update
A policy update adapts existing governance rules to AI-specific risks and responsibilities.
Documentation Duty
Documentation duty preserves evidence of decisions, controls, risk assessments, and monitoring.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
What are the broad stages of the AI system lifecycle?
Why is governing the data stage of the AI lifecycle critical?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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