Isaca ISACA Advanced in AI Security Management (AAISM) Exam AAISM Exam Dumps: Updated Questions & Answers (December 2025)

In AI Security Management, bias evaluation is primarily a data problem before it is a model or performance problem. The official AI Security Management content explains that impact assessments must specifically analyze training data representativeness against the demographics and characteristics of all relevant users and stakeholders. If the data used to train an AI system underrepresents or omits particular groups, the resulting model will systematically disadvantage them, regardless of how fast or “accurate” it is on average. While comparing outputs to historical data (option B) can help, historical data itself may be biased. Performance-related options (C and D) relate to efficiency and scalability, not fairness. Therefore, systematically assessing whether the training data covers all relevant end-user groups is the most direct and effective way to evaluate and mitigate bias.

AAISM emphasizes strong contractual restrictions on how vendors use customer data, especially prohibiting vendors from using customer inputs to train or fine-tune shared models.

This protects against:

• data leakage

• intellectual property exposure

• regulatory violations

• shadow training of external models

Log sharing (B) and query limits (D) are operational controls but do not directly prevent data misuse. Unlimited retraining (A) has no relevance to security.

AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs—directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario’s stated objective—accessing confidential information—most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.

AAISM incident response guidance states the first foundational step is forming a cross-functional AI-aware incident response team, including model developers, data stewards, security leads, and compliance officers. Without the team established, recovery (C), containment (D), or communication channels (A) cannot be effectively designed or executed.

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 states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:

• AI model validation

• Change management

• Governance oversight

This aligns precisely with option D.

Lack of dashboards (C) affects monitoring but does not explain disabled risk signals. Computing resources (A) would not cause intentional disabling. Reliance on consultants (B) is not connected to improper internal model changes.

Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with. While risk assessments (Option B) and governance policies (Option C) set expectations, they do not technically prevent forged media. Input validation (Option D) does not address media authenticity once generated or received.

AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:

• model behavior

• compliance gaps

• security improvements

• governance alignment

Internal auditors (B) perform assessments but do not implement changes. System architects (A) support technical fixes but do not own compliance. End users (C) are not responsible for audit remediation.

AAISM stresses AI-specific change management as essential for vendor-driven or system-driven updates. Proper change control includes:

• impact assessments

• ethical review

• risk evaluation

• approval checkpoints

• rollback plans

An MSA (A) supports contracts but does not manage operational change. Shared responsibility (C) describes roles, not change control. Data minimization (D) reduces exposure but doesn't control model updates.

AAISM materials specify that the composition of an AI red team must be tailored to the organization’s AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.