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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - Data management and privacy controls
|
| Topic 2: AI Governance and Program Management | 31% | - AI asset and data lifecycle management
|
| Topic 3: AI Risk Management | 31% | - AI threat and vulnerability management
|
ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification exams are a great way to analyze and evaluate the skills of a candidate effectively. Big companies are always on the lookout for capable candidates. You need to pass the ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification exam to become a certified professional. This task is considerably tough for unprepared candidates however with the right AAISM prep material there remains no chance of failure.
NEW QUESTION # 151
Which of the following is the MAIN objective of the operational phase of AI life cycle management?
Answer: A
Explanation:
In the operational phase, AAISM emphasizes continuous monitoring of models for performance, stability, robustness, drift, data quality, security events, and policy compliance. This includes telemetry, thresholds, alerts, incident response for AI failures, and evidence collection for audits. Alignment to business needs is established earlier in planning/governance; algorithmic optimization and feedback collection are supporting activities, but the primary operational objective is live monitoring and assurance to keep risk within tolerance.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Life Cycle-Operate/Monitor; Ongoing Performance & Drift Monitoring; AI Incident Management* AAISM Study Guide: Operational Controls, Metrics & SLAs/SLOs; Evidence & Audit Readiness in Production
NEW QUESTION # 152
Personal data used to train AI systems can BEST be protected by:
Answer: B
Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk. Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
NEW QUESTION # 153
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?
Answer: A
Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
* proof of lawful data disposal
* auditability
* regulatory compliance
* defensibility during inspections
Post-destruction risk assessments (A) are not primary compliance evidence. Backup tests (B) are operational tasks, not decommissioning proof. Policy updates (C) are future improvements.
References: AAISM Study Guide - AI Decommissioning & Regulatory Compliance; Data Destruction Evidence.
NEW QUESTION # 154
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?
Answer: D
Explanation:
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage-directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
References: AI Security Management™ (AAISM) Body of Knowledge: Model Development & Evaluation Controls; Data Selection and Representativeness; Operationalization to Reduce Human Error. AAISM Study Guide: Tuning Detection Systems with Historical Corpora; Alert Quality, Precision/Recall, and SOC Workflow Integration.
NEW QUESTION # 155
Which of the following strategies BEST ensures generative AI tools do not expose company data?
Answer: A
Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input- governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow- lists for approved contexts provides the highest assurance of preventing exposure.
NEW QUESTION # 156
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