Deployment Decision Factors and Risks
This topic covers business objectives, performance requirements, data availability, ethical considerations, workforce readiness, model types, proprietary versus open source choices, model size, modality, deployment options, fine-tuning, RAG, agentic architectures, and fit-for-purpose techniques.
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
Deployment decisions should account for use-case context, business objectives, performance requirements, data availability, ethics, workforce readiness, and operating constraints.
Exam cue: Use workforce readiness when people must understand, monitor, or rely on AI output.
Concept 2
Model choice depends on classic versus generative behavior, proprietary versus open source models, model size, language or multimodal capability, and governance needs.
Exam cue: Use deployment option analysis when location, control, latency, or compliance constraints matter.
Concept 3
Deployment architecture can include cloud, on-premise, edge, unmodified model use, fine-tuning, retrieval, agentic workflows, or other fit-improving techniques.
Exam cue: Use model type and modality when the use case requires text, image, audio, language, or multimodal capability.
Risk pitfalls and guardrails
Deploying a model because it is available rather than because it fits the use case.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Ignoring employee readiness and operational ownership.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Treating open source, proprietary, cloud, edge, and on-premise options as governance-equivalent.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Memory anchors
Use-Case Context
Use-case context defines objectives, users, affected parties, data, constraints, and risk level.
Workforce Readiness
Workforce readiness means people are trained and prepared to operate or rely on AI appropriately.
Model Type
Model type distinguishes classic, generative, language, multimodal, small, large, proprietary, or open source models.
Deployment Option
A deployment option is the environment and method used to run or access an AI model.
Edge Deployment
Edge deployment runs AI close to devices or users when latency, connectivity, or locality matters.
RAG Deployment
RAG deployment uses retrieval to add external context without embedding all knowledge in the model.
Agentic Architecture
Agentic architecture coordinates models, tools, memory, and workflow actions.
Fit Improvement
Fit improvement adapts AI behavior through retrieval, prompt design, fine-tuning, controls, or process changes.
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
Why is a go/no-go decision important before deploying an AI system?
Why should residual risk be formally accepted by an accountable party before deployment?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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