AI Asset, Data and Lifecycle Management
Governance requires inventorying AI assets, classifying data, managing model and dataset lifecycle states, and keeping lineage and ownership current.
How to study for ISACA AAISM
Treat each item as a management decision: identify the AI asset and stakeholder, assess risk, select governance or control action, then document evidence and accountability.
Core concepts
Concept 1
AI asset inventories should include models, datasets, prompts, embeddings, agents, tools, pipelines, endpoints, owners, and criticality.
Exam cue: Inventory AI assets before assessing risk or assigning controls.
Concept 2
Lifecycle management tracks AI assets from intake and development through approval, deployment, monitoring, change, retirement, and disposal.
Exam cue: Track lineage when training, tuning, retrieval, or output data must be explained.
Concept 3
Data classification and lineage support access control, privacy, retention, audit, and incident investigation.
Exam cue: Use lifecycle states to prevent unmanaged prototypes from becoming production dependencies.
Risk pitfalls and guardrails
Leaving shadow AI assets outside inventory and risk review.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Tracking models but not prompts, tools, datasets, or endpoints.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Retiring an AI system without handling retained data and logs.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
AI Asset Inventory
An AI asset inventory records models, data, prompts, tools, agents, endpoints, owners, criticality, and status.
Lifecycle State
A lifecycle state shows whether an AI asset is proposed, developed, approved, deployed, changed, retired, or disposed.
Data Classification
Data classification labels sensitivity, business value, legal constraints, and handling requirements.
Lineage
Lineage shows where data, models, prompts, and outputs came from and how they changed.
Shadow AI
Shadow AI is unapproved AI use or assets outside governance, inventory, security review, and monitoring.
Criticality
Criticality indicates how important an AI asset is to business operations, compliance, or safety.
Retention Rule
A retention rule defines how long AI data, logs, outputs, and evidence must be kept or deleted.
Disposal Control
A disposal control ensures retired AI assets, data, credentials, and endpoints are removed safely.
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
Business units can activate AI capabilities in SaaS products without registering them in the central inventory. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over AI asset inventory operated throughout the review period?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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