ISACA Advanced in AI Security Management (AAISM) Exam Valid Exam Guide & AAISM Free Pdf Vce & ISACA Advanced in AI Security Management (AAISM) Exam Latest Practice Questions

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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Passing Score:	450 (out of 800)
Certificate Validity Period:	3 years
Exam Format:	Multiple-choice, Scenario-based
Real Exam Qty:	90
Available Languages:	English
Exam Price:	US$459 (members), US$599 (non-members)
Exam Duration:	150 minutes
Related Certifications:	CISM CISSP
Recommended Training:	ISACA Official AAISM Training
Exam Registration:	ISACA AAISM Registration
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:	Hold an active CISM or CISSP certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 2	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:

A. Membership inference
B. Model exfiltration
C. Evasion attacks
D. Poisoning attacks

Answer: C

Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios

NEW QUESTION # 179
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:

A. automated rule creation to increase model performance
B. classified real intrusion data based on labeled data
C. synthetic intrusion data to train the tool's components
D. validation data sets to enable highly realistic AI decisions

Answer: C

Explanation:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing.
Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.

NEW QUESTION # 180
An organization is considering a Software as a Service (SaaS)-based HR system that uses AI to automate resume vetting and pre-screening of candidates. Which of the following controls is MOST important to review?

A. Inclusion of diverse and representative training data
B. Availability of data backups to all customers
C. Periodic conformity assessments performed by the vendor
D. Encryption and isolation of data from other customers

Answer: A

Explanation:
AAISM states that HR systems performing candidate evaluation must prioritize training data fairness, representativeness, and bias mitigation because biased HR decisions carry regulatory, ethical, and litigation risks.

NEW QUESTION # 181
Which of the following BEST helps an organization protect confidential data while using a pre- trained open-source AI solution?

A. Monitor the solution's performance for reliability.
B. Verify and sanitize both inputs and outputs.
C. Periodically test the solution for vulnerabilities.
D. Continuously monitor usage to detect fraud and abuse.

Answer: B

Explanation:
Verifying and sanitizing inputs prevents sensitive data from being unintentionally exposed to the model, while sanitizing outputs helps ensure that confidential information is not leaked in responses, directly protecting data during use.

NEW QUESTION # 182
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?

A. Confirming the AI solution supports single sign-on (SSO)
B. Ensuring the vendor offers 24/7 technical support
C. Verifying the vendor has updated terms of service
D. Requiring the vendor to provide the model card

Answer: D

Explanation:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance. A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures-information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.
References: AI Security Management (AAISM) Body of Knowledge - Third-Party & Supply Chain Governance; Transparency Artifacts (Model Cards, Datasheets). AAISM Study Guide - Vendor Due Diligence Requirements; Documentation for Risk, Fairness, and Intended Use.

NEW QUESTION # 183
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