AI Security Architecture Design
Architecture controls cover secure design, trust boundaries, identity, network segmentation, endpoint protection, tool permissions, logging, and resilience.
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
Secure AI architecture should define trust boundaries, identity, data flows, model endpoints, tool access, monitoring, and resilience.
Exam cue: Use architecture review when an AI system integrates data, tools, endpoints, and users.
Concept 2
Least privilege should apply to users, services, agents, tools, datasets, functions, and model endpoints.
Exam cue: Use least privilege for agent tools and service identities.
Concept 3
Security architecture decisions should be documented so control rationale and risk assumptions can be reviewed.
Exam cue: Document trust boundaries before selecting controls.
Risk pitfalls and guardrails
Letting an agent inherit broad user or service permissions.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Securing the model endpoint but ignoring retrieval and tool paths.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Skipping architecture evidence needed for assurance review.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
Trust Boundary
A trust boundary marks where data, identity, permissions, or control responsibility changes.
Least Privilege
Least privilege grants only the access required for the AI task and no more.
Service Identity
A service identity lets applications call resources with controlled non-human credentials.
Tool Permission
Tool permission limits what an AI agent or workflow may call, read, change, or execute.
Data Flow
A data flow documents how information moves through input, retrieval, model calls, outputs, logs, and integrations.
Segmentation
Segmentation separates AI components or data paths to limit exposure and blast radius.
Secure Design Review
A secure design review checks architecture, threats, controls, assumptions, and residual risk before deployment.
Resilience Control
A resilience control helps AI-supported services withstand failure, abuse, dependency outage, or recovery events.
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
An internal AI service trusts any request originating from the corporate network. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over zero-trust architecture 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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