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

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.

AAISM materials describe data segregation and segmented access as core technical controls to prevent unintended information disclosure by AI systems. Siloing refers to logically or physically separating data into distinct repositories or contexts, ensuring that sensitive datasets are not available to components or applications that only require non-sensitive information. This is directly aligned with preventing a chatbot from accessing or mixing confidential data with general conversational content. Zero Trust (A) is an overarching security architecture principle, focusing on identity and continuous verification; it does not by itself guarantee separation of data. Sandboxing (B) isolates processes but is less about fine-grained data separation. Containerization (D) packages applications and their dependencies, again not necessarily solving the specific problem of mixing sensitive and non-sensitive datasets. Siloing is explicitly highlighted as a way to prevent cross-context leakage in AI use cases.

AAISM’s risk management framework stresses that the most effective defense against deepfake-enabled fraud, such as payment diversion, is resilient payment approval processes. This includes multi-step verification, segregation of duties, and independent confirmations for high-value transactions. Employee training, policies, or limiting payment frequency may reduce exposure, but they cannot guarantee prevention. Only process-based controls enforce structural safeguards that prevent fraudulent instructions from being executed, even if a deepfake impersonation attempt is successful.

AAISM categorizes unbounded consumption (also known as “resource exhaustion” or “infinite queries”) as an AI-specific vulnerability where attackers (or faulty prompts) trigger excessive computation, leading to high costs and degraded service.

This aligns precisely with unexpected large compute bills.

Excessive agency (A) refers to unsafe autonomy, while disclosure (B) and prompt leakage (D) do not relate to compute overuse.

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.

AAISM explains that secure aggregation ensures the server only sees aggregated model updates—not individual client contributions—so privacy is preserved even if the server is breached.

Encryption (A) is semi-correct but still allows the server to decrypt. Differential privacy (B) is separate. Isolation (D) does not guarantee confidentiality.

AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.

AAISM directs personnel to use established AI governance channels for suspected privacy, ethics, or compliance risks. The governance panel (risk, privacy, legal/compliance, security, product/data science) is chartered to triage, record, investigate, and direct remediation for potential inference of sensitive attributes and resulting decision impacts. Direct technical action (A or C) bypasses due process and accountability; escalating directly to a single executive (B) lacks the structured, cross-functional oversight required for regulated and ethical AI risk handling.