AAISM Exam Dumps - ISACA Advanced in AI Security Management (AAISM) Exam

AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.

Output moderation detects:

• maliciously influenced responses

• unsafe outputs

• security-policy violations

IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine-tuning (A) cannot guarantee full prevention.

AAISM vendor governance guidance identifies regular audits of vendor processes as the most effective method of ensuring compliance with ethical and security standards. Independent audits provide verifiable assurance that vendors are meeting agreed-upon requirements. Self-attestation, internal monitoring, or documentation sharing provide some transparency but do not guarantee compliance. The best practice, particularly for high-risk AI deployments, is independent and recurring audits of vendor processes.

AAISM highlights threat modeling and supply chain integrity checks as key controls for managing AI-specific risks, including hidden backdoors in third-party models, libraries, or fine-tuning artifacts. The official guidance states that organizations should “identify adversarial insertion points, verify component integrity, and continuously test for malicious behaviors introduced via external components.” This is precisely what option B describes. Simply using open-source (A) does not guarantee security; code can still contain malicious modifications. Disabling runtime logs (C) would reduce visibility, making backdoor detection harder. Choosing unsupervised learning (D) is a modeling approach and has no inherent relation to backdoor risk reduction. The explicit combination of AI-focused threat modeling and integrity verification of external components is the recommended best practice to mitigate this class of attack.

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.

According to AAISM privacy governance guidance, the primary reason for conducting a PIA is to analyze how personal data is collected, processed, shared, and retained by an AI system. This analysis ensures compliance with privacy laws, mitigates risks to individuals, and informs necessary safeguards. Identifying regulations is part of compliance but is secondary to analyzing actual data handling. Building customer confidence is an outcome, not the main purpose. Checking for poisoned data relates to data quality, not privacy assessment. The fundamental purpose of a PIA is therefore to analyze the handling of personal data.

AAISM defines adversarial attacks as manipulations of input data (text, image, audio, numeric values) designed to cause the model to produce incorrect or harmful predictions.

Hardware attacks (A) are infrastructure threats. Social engineering (C) targets people, not models. DoS attacks (D) affect availability, not model decision pathways.

The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property–protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.

The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider’s technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.