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.
References:
AAISM Study Guide - AI Governance and Program Management (Data Classification and Inventory) ISACA AI Security Management - Automated Data Cataloging for AI Projects

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

AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations

AAISM materials emphasize that in operational AI systems, key risk indicators (KRIs) must reflect risks to performance and reliability rather than technical design factors alone. In the case of threat detection, the most relevant KRI is the frequency of system overrides by human analysts, as this indicates a lack of trust, frequent false positives, or poor detection accuracy. Training epochs, model depth, and training time are technical metrics but do not directly measure operational risk. Analyst overrides represent a practical measure of system effectiveness and risk.
References:
AAISM Study Guide - AI Risk Management (Operational KRIs for AI Systems) ISACA AI Security Management - Monitoring AI Effectiveness

AAISM training guidance specifies that social engineering is the awareness topic most impacted by AI- enabled risks. With generative AI and deepfake technologies, attackers can create highly convincing phishing messages, synthetic voices, or fake executive requests, increasing the sophistication of social engineering attacks. Clean desk policies, insider threat awareness, and authentication procedures remain relevant but are not directly altered by AI advancements. The most likely revision to employee awareness programs in the AI era is therefore enhanced social engineering awareness.
References:
AAISM Exam Content Outline - AI Risk Management (Human Factors and Awareness) AI Security Management Study Guide - Social Engineering Risks with AI