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

AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models—especially when they affect individuals' rights, opportunities, or access to services.

Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.

Deployment instructions (B) are not part of model cards. They do not reduce data needs (C), nor is model type selection (D) their primary purpose.

AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.

AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data-security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization’s data for training or secondary purposes and that mandate retention limits and secure deletion. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.

AAISM identifies the operational phase as the stage where the AI system is actively deployed and requires continuous monitoring of performance, drift, security, and reliability.

Business alignment (B) belongs to planning and design. Optimization (A) and user feedback (D) are part of development or improvement cycles, not primary operational objectives.

Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.

AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:

• improved efficiency

• reduced cost

• reduced risk

• business growth

Training (B) and risk optimization (C) are important but do not guarantee stakeholder support. A roadmap (D) guides planning but does not secure buy-in.

AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:

• attack samples become harder to inject

• anomalies are easier to detect

• corrupted data has reduced influence on model behavior

Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.

AAISM emphasizes data lineage, provenance tracking, and inventory completeness as essential controls to ensure data security and accountability across all AI data life-cycle phases. This enables detection of unauthorized modifications, improper use, and compliance violations.

Periodic reviews (B) are necessary but insufficient without lineage. Restricting third-party use (C) is one control but not comprehensive. Open-source model choice (A) does not secure data.