Security Controls, Monitoring and Metrics
Control operation requires monitoring, logging, testing, metrics, alerts, dashboards, remediation tracking, continuous improvement, and assurance evidence.
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 monitoring should track security events, policy violations, access, output safety, quality drift, model changes, incidents, and control health.
Exam cue: Use control metrics when leadership needs posture and trend visibility.
Concept 2
Metrics should support management decisions by showing risk trends, control effectiveness, remediation, exceptions, and incident outcomes.
Exam cue: Use alerts when thresholds require investigation or escalation.
Concept 3
Continuous improvement uses monitoring, testing, incidents, audits, and feedback to update AI controls.
Exam cue: Use evidence logs to prove controls operated when audited.
Risk pitfalls and guardrails
Monitoring model quality but not security control health.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Collecting logs without owners, thresholds, or review procedures.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Reporting metrics that cannot drive a decision or remediation.
Guardrail: Avoid treating AI security as only a technical scan, accepting risk without authority, or leaving prompts and data outside governance.
Memory anchors
Control Health
Control health indicates whether an AI security control is operating, tested, effective, and remediated when needed.
Security Metric
A security metric measures risk trend, control performance, incident response, remediation, or exception status.
Alert Threshold
An alert threshold defines when monitoring signals require review, escalation, or response.
Assurance Evidence
Assurance evidence proves control design, operation, testing, and remediation.
Continuous Improvement
Continuous improvement updates controls using incidents, audits, tests, feedback, and risk changes.
Dashboard
A dashboard presents AI security posture, trends, exceptions, incidents, and remediation for stakeholders.
Remediation Tracker
A remediation tracker records gaps, owners, due dates, status, and validation results.
Log Review
Log review examines AI access, prompts, outputs, model changes, alerts, and control events for risk signals.
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
Model administrators can also alter training data, approve releases, and disable logs. Which control is MOST appropriate?
Which evidence BEST demonstrates that controls over least privilege 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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