AAISM Questions and Answers

AAISM technical coverage identifies recall as the metric that specifically measures a model’s ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.

Membership inference attacks attempt to determine whether a particular data point was part of a model’s training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.

AAISM risk guidance underscores that transparent disclosure and informed consent are the most important factors in maintaining user trust in generative AI. Users must clearly understand how outputs are created, what data sources are used, and how risks such as bias or misinformation are managed. While bias minimization, access controls, and anonymization contribute to technical or ethical robustness, they are not sufficient to preserve user trust. Trust requires openness and consent, which align with governance expectations for transparency and accountability.

AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model’s decision-making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker’s goals reflects an evasion attack.

When setting RPOs and RTOs for AI systems, especiallygenerative AI, thecritical factor is the restoration of training data and model artifacts within the recovery window. Without this, restored systems may function inaccurately or incompletely, undermining business continuity.

AAISM risk management principles emphasize:

Recovery objectives must align withdata protection requirementsfor both training and inference data.

The ability to restorelarge-scale training datasetsis primary, since downtime without them leads to operational and compliance risks.

Computational efficiency and hardware consistency are secondary considerations, but not the primary drivers of RPO/RTO definitions.

Thus, ensuring backup and restore capabilities of training datasets directly within RTO is theprimary requirement.

Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs. Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.

AAISM’s governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined. Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.

AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy. Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.

In AAISM governance guidance, risk documentation is described as the structured record that defines the organization’s risk appetite and tolerance levels for AI initiatives. By outlining acceptable levels of risk, documentation ensures decision-makers can approve, monitor, and adjust AI projects within defined boundaries. While it may also serve audit functions, technical analysis, or communication to stakeholders, its primary role is to formalize risk acceptance thresholds and integrate them into governance and decision-making. This aligns directly with the governance requirement to align AI adoption with organizational risk appetite.

The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.

According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity. Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.

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 AAISM curriculum states that the most serious privacy concern occurs when AI systems infer and disclose sensitive personal or group information without the knowledge or consent of the individuals. This constitutes a direct breach of privacy rights and data protection principles, including those enshrined in GDPR and other global regulations. While litigation, reputational damage, or loss of trust are significant consequences, the unauthorized revelation of personal information through inference is classified as the most severe, because it directly undermines individual autonomy and confidentiality.

In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.

The AI Security Management™ (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the-loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals’ access to credit and create compliance risks.

Official ISACA AI governance guidance specifies:

High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.

The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.

Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.

Why other options are not the highest priority:

A. Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.

B. Appeals mechanisms are important for accountability, but they operate after harm has occurred. Governance frameworks emphasize prevention through human oversight in the decision loop.

D. Restricting criteria to “objective metrics” is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.

AAISM Domain Alignment:

Domain 1 – AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.

Domain 2 – AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.

Domain 3 – AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.

References from AAISM and ISACA materials:

AAISM Exam Content Outline – Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).

ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).

Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).

AAISM emphasizes that the primary objective of responsible AI is to establish and maintain trust in AI-driven decisions and predictions. Trust is achieved through transparency, accountability, fairness, and governance. While confidentiality and integrity are critical technical objectives, they are not the overarching purpose of responsible AI service provision. Autonomy and learning ability are features of AI, but without trust, adoption and compliance falter. The correct answer is that responsible AI services must focus on building trust in AI outcomes.

AAISM’s risk management guidance notes that the most effective application of AI in incident response is in automating triage activities. AI systems can rapidly analyze logs, alerts, and telemetry to prioritize incidents, reducing response times and allowing human analysts to focus on critical issues. Streamlining testing and improving playbooks are valuable but secondary benefits. Ensuring chain of custody is critical for legal admissibility of evidence but is primarily a human and process-driven control, not AI’s strength. The greatest efficiency and effectiveness comes from AI-driven triage automation.

The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.

According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.

The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets. Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model. Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.

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 that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.

AAISM emphasizes that when model drift occurs, the best response is not a quick fix but rather to revisit an earlier phase of the AI life cycle to address data quality, retraining, or evaluation processes. Simply taking the model offline halts functionality without resolution, while adjusting hyperparameters or issuing emergency changes treats the symptom rather than the root cause. Proper governance requires returning to the design or training phases to re-establish stability, accuracy, and compliance of the model. Thus, the correct approach is to return to an earlier AI lifecycle phase.