Free AAISM Exam Dumps Preview

AAISM's risk management content describes red-teaming in generative AI as focused on deliberately crafting adversarial prompts to test whether the model produces unexpected or undesired outputs that violate safety, integrity, or compliance standards. The goal is not to stress system performance or randomly disrupt outputs, but rather to uncover vulnerabilities in how the model responds to manipulative inputs. This allows organizations to improve resilience against prompt injection, jailbreaking, or harmful content generation. The correct answer is therefore generate outputs that are unexpected using adversarial inputs.

References:
AAISM Exam Content Outline - AI Risk Management (Red-Team Testing and Adversarial Exercises) AI Security Management Study Guide - Penetration Testing in Generative AI Contexts

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.

References:
AAISM Exam Content Outline - AI Risk Management (Testing and Assurance Practices)
AI Security Management Study Guide - Adversarial Testing Against Prompt Manipulation

The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance.
While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.

References:
AI Security Management (AAISM) Body of Knowledge: AI Governance and Program Management -
Security program alignment, control objectives, and requirements traceability AI Security Management Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance

AAISM identifies the strongest immediate control for preventing data leakage into generative AI systems as technically restricting or blocking user entry of sensitive data.
Audits (A) are retrospective. Testing tools (C) does not prevent user error. Regulatory compliance (D) does
not stop operational leakage.

References:
AAISM Study Guide - Data Leakage Prevention and Input Restriction Controls.

AAISM stresses high explainability for safety-critical and human-impacting decisions such as healthcare predictions. Technical performance (A, C, D) does not outweigh transparency requirements.
References:
AAISM Study Guide ?Explainability for High-Impact Use Cases.

AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI-specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.

References:
AI Security Management (AAISM) Body of Knowledge - Governance & Program Controls;
Control Selection and Tailoring
Threat-to-Control Mapping for AI Systems
Risk Appetite & Control
Assurance Alignment.

AAISM requires formal change management for AI systems, including vendor-initiated changes: pre-approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces
exposure but does not control model substitutions.

References:
AI Security Management. (AAISM) Body of Knowledge - AI Governance: Change Control &
Release Management
Third-Party AI Assurance and Approval Workflows
Accountability and Sign-off for
Model Changes.

The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data.
According to the AI Security Management. (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased-reflecting skewed representation, sampling imbalance, or embedded prejudice-the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.
Why Option A is Correct: Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.
It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance-key concerns in AAISM's AI Risk Management and Governance principles.
Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.
Why Other Options Are Incorrect:

Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.
Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.
Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation,
not statistical computation pipelines.
Exact Extract from Official AAISM Study Guide:

"Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting."

References:
AI Security Management. (AAISM) Body of Knowledge: AI Risk Identification and Evaluation, Bias and Fairness Management in AI Systems.
AI Security Management. Study Guide: Systemic Bias Mitigation Techniques, Fairness Assurance in AI
Analytics.
ISO/IEC 23894:2023 - Clause 7.2: Bias identification and treatment within AI risk frameworks.

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.

References:
AAISM Study Guide - AI Governance Documentation
Transparency and Model Cards.

AAISM guidance specifies that before introducing AI into any business or technical workflow, an AI Impact Assessment must be conducted early to determine potential risks, privacy implications, misuse scenarios, governance gaps, and required security controls. This aligns with the principle that AI adoption must begin with governance and risk identification, not training or policy modification.
Training developers (B) is important but occurs after identifying risks. Updating policies (C) is also downstream of the assessment. Cost-benefit analysis (D) supports business justification but does not address security.

References:
AAISM Study Guide - AI Governance
Impact Assessment Required Before Deployment.

When setting RPOs and RTOs for AI systems, especially generative AI, the critical 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 with data protection requirements for both training and inference data.
The ability to restore large-scale training datasets is 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 the primary requirement.

AAISM emphasizes that bias issues first require understanding whether training data lacks diversity for cultural contexts. Retraining (B) comes after root cause analysis. Filtering (C) is a compensating control.
Sampling (D) does not address bias.
References:
AAISM Study Guide ?Bias Identification
Data Representativeness Checks.

AAISM emphasizes robustness as the key requirement for fraud-detection systems because they must resist adversarial manipulation, data poisoning, spoofing, and input tampering.
Accuracy (B) matters but does not protect against adversarial attacks. Explainability (C) is important but secondary. Adaptability (D) is useful but not the top security requirement.

References:
AAISM Study Guide - AI Robustness
Fraud Detection and Adversarial Resistance.

AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
A (adversarial training) targets perturbation robustness, not primary for inversion.
B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible;
among the listed options, regularization most directly addresses inversion risk.
References:
. AI Security Management (AAISM) Body of Knowledge: Model Security-Privacy leakage
threats (membership inference, inversion) and mitigation via regularization and output minimization.. AI
Security Management Study Guide: Overfitting controls, calibration and confidence suppression as
defenses against inference attacks.

AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements. Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.

References:
AAISM Study Guide - AI Governance and Program Management (Third-Party Contracts and Audit Rights) ISACA AI Security Management - Vendor Governance Controls

AAISM states that an AI management system policy provides organizational structure by: defining AI objectives
aligning governance . outlining accountability
defining roles, responsibilities, and guiding principles Regulatory compliance (C) is a part of governance but not the overall purpose. Accuracy (B) and environmental impact (A) are narrower focus areas.

References:
AAISM Study Guide - AI Management System Policies
Governance Framework
Requirements.

AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re-evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation.
Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers. The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.

References:
AAISM Exam Content Outline - AI Risk Management (Innovation and Risk Control)
AI Security Management Study Guide - Phased Implementation Strategies

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.

References:
AAISM Study Guide - AI Risk Management (Adversarial Threats and Availability Risks) ISACA AI Security Management - Attack Classifications

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.

References:
AI Security Management. (AAISM) Body of Knowledge - Incident Handling &
Communications
Human Oversight and Approval Gates
Trust Restoration During AI Incidents.

AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest-impact near-term intervention for human-factor AI risks. By mapping concrete "do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.
A (blocking) is a technical containment option but is not an awareness-initiative control and may cause workarounds; AAISM treats it as complementary, not a substitute for behavior change.
B generic modules fail to address the specific misuse pattern.
D signatures provide attestations without ensuring comprehension or changed behavior.
References:
. AI Security Management. (AAISM) Body of Knowledge: Human-centric Controls-Role-based
training, policy-to-practice mapping, and scenario exercises for rapid risk reduction.. AI Security Management. Study Guide: Awareness program design for generative AI misuse
behavior-anchored
training outcomes.

The AAISM governance framework emphasizes that AI transparency cannot be evaluated using only technical statistics; it requires a combination of quantitative and qualitative metrics. The best pairing is
ethical impact assessments (qualitative) with user feedback metrics (quantitative and perception-based).
Availability and accuracy metrics measure performance, not transparency. Explainability reports and bias metrics are useful but still technical and limited. Comprehensive evaluation of transparency requires consideration of ethical dimensions and stakeholder perspectives, which is achieved through ethical impact analysis and user feedback.

References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Accountability) ISACA AI Security Management - Measuring Ethical AI Practices

The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction.
Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.

References:
AAISM Study Guide - AI Risk Management (Data Integrity and Quality Controls) ISACA AI Governance Guidance - Data Preparation and Bias Mitigation

AAISM indicates that white-box testing allows evaluators full visibility into:
.
internal logic
.
weights
.
decision pathways
.
model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.

References:
AAISM Study Guide - AI Testing
Explainability Through White-Box Analysis.

AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice.
Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:
. AI Security Management (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-
gate compliance, audit readiness. AAISM Study Guide: Governance metrics for Responsible AI-coverage of reviews, pass/fail rates, exceptions handling

In the AAISM. guidance, vendor management for AI adoption highlights that large AI providers often resist contractual changes, particularly when customers seek to impose stricter security, transparency, or ethical obligations. The official study materials emphasize that while organizations must evaluate AI risk and build internal expertise, the primary challenge lies in negotiating acceptable contractual terms with dominant AI vendors who may not be willing to adjust their standardized agreements. This resistance limits the ability of organizations to enforce oversight, bias controls, and compliance requirements contractually.

References:
AAISM Exam Content Outline - AI Risk Management
AI Security Management Study Guide - Third-Party and Vendor Risk

According to AAISM, high-impact use cases involving financial decisions require an impact assessment FIRST to understand legal, societal, fairness, and rights-based implications. Fairness testing (B) and bias/ drift evaluations (D) occur later. HITL review (A) is a control but not the initial step.
References:
AAISM Study Guide ?AI Governance&
Impact Assessment Requirements.

AAISM stresses that security must be embedded from the earliest phase-design, ensuring:
.
threat modeling
.
secure architecture
.
data protection requirements
.
system boundaries
.
security-by-design principles Introducing cybersecurity later increases unmitigated vulnerabilities.
Testing (A), training (C), and deployment (B) occur after foundational security decisions have already been made.

References:
AAISM Study Guide - Secure AI Lifecycle
Early Security Involvement.

According to AAISM governance principles, when AI functionality is added to existing services, the first consideration is contractual and service-level accountability. If AI outputs cannot be predefined, the existing service agreements may no longer reflect performance responsibilities or liability. Revising or updating the agreement ensures governance alignment, accountability, and risk management for AI-driven behavior. Phased approaches and performance explanations are valuable but occur later in project management. Developer accountability for customer inquiries is not a primary governance step. The most immediate consideration is revising service agreements when AI introduces new uncertainties.

References:
AAISM Exam Content Outline - AI Governance and Program Management (Policies and Service Agreements) AI Security Management Study Guide - Accountability in AI Deployments

AAISM highlights that membership inference exploits confidence discrepancies between training and non-training samples. Restricting confidence scores reduces signal exposure, weakening the attack surface.
Transparency (B) may worsen risk. Explanations (C) increase leakage potential. Changing learning paradigms (D) does not mitigate inference attacks.
References:
AAISM Study Guide ?Privacy Attacks
Membership Inference Mitigations.

AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.

References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization

AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
.
awareness
.
practical adoption
.
reliance on policy guidance
.
safe decision-making behavior
Violations (A) are lagging indicators. Compliance reviews (B) measure oversight, not behavior. Policy review frequency (D) tracks governance updates, not usage.

References:
AAISM Study Guide - AI Policy Effectiveness Metrics.

The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.

References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration

An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is
strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:
. AI Security Management. (AAISM) Body of Knowledge: AI Program Architecture -alignment
of capabilities, processes, and controls to business objectives. AI Security Management. Study Guide: Architecture-driven governance, traceability from business goals to technical and control design

AAISM states that the most effective short-term mitigation for unintentional data leakage into public AI tools is targeted, role-based AI security awareness, focused on: what data cannot be entered consequences of leakage real-world scenarios employees face
An acceptable-use policy (C) is necessary but insufficient alone. Blocking LLMs (D) may reduce access but does not change user behavior and may cause shadow AI usage. Generic phishing training (B) does not address AI misuse risks.

References:
AAISM Study Guide - AI Security Awareness
Human-Centric Data Leakage Prevention.

Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits. While fairness (A) and security controls (C) are essential, they are distinct obligations; data subject rights such as rectification/erasure (B) exist regardless
of consent and are not "enabled" by it. Therefore, the greatest significance of consent is that it legally permits processing under declared purposes and constraints.

References:
AI Security Management. (AAISM) Body of Knowledge - Privacy & Data Protection in AI;
Lawful Basis and Purpose Limitation. AAISM Study Guide - Consent, Transparency, and Data Subject Rights
Privacy-by-Design Controls in AI Pipelines.

AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities.
Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.

References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance

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's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.

References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security)
AI Security Management Study Guide - Open-Source Package Risk Mitigation

AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that
frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.

References:
AI Security Management. (AAISM) Body of Knowledge - Incident Response & Post-Incident
Improvement
Model Risk Treatment & Drift Management
Bias Detection and Remediation Governance.

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.

References:
AI Security Management (AAISM) Study Guide - AI Risk Identification and Impact
Assessment
Data Governance and Bias Section.

When an AI system experiences an attack after being in production for an extended period, the most effective mitigation strategy is to update the deployed training data with new adversarial data. This process strengthens the model's resilience by retraining it to recognize and resist attack vectors that were previously unknown or unaccounted for. According to the AI Security Management (AAISM) framework, risk mitigation for AI systems must address model robustness through adversarial retraining, data quality improvement, and model lifecycle hardening rather than relying solely on reactive measures.
Why Option B is Correct: Incorporating adversarial examples into the training set enhances the system's ability to correctly classify and withstand malicious inputs.
This approach directly mitigates the vulnerability exploited in the attack and supports a proactive,
continuous risk management cycle.
Why Other Options Are Incorrect: Option A: Monitoring helps detect suspicious activity but does not resolve the underlying vulnerability.

Option C: Concealing confidence scores may reduce model transparency but does not address the attack mechanism or its root cause.
Option D: Implementing access controls protects the model's architecture but does not improve model robustness against input manipulation attacks.
Exact Extract from Official AAISM Study Guide: "AI risk management requires continuous improvement following incidents. After an adversarial or data poisoning event, the preferred risk treatment involves retraining the model using adversarial data and updated datasets to enhance robustness. This ensures the AI model adapts to evolving threat landscapes rather than merely restricting access or obscuring outputs."

References:
AI Security Management (AAISM) Body of Knowledge: AI Risk Treatment and Mitigation Strategies, Adversarial Robustness and Resilience Engineering.
AI Security Management Study Guide: Model Lifecycle Security, Continuous Risk Treatment through Adversarial Retraining.
ISO/IEC 23894:2023, Clause 8.3.2 - Risk treatment through robustness improvement and adversarial data inclusion.

The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems.
Effective AI security testing requires:
AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
Adversarial attack simulations (white-box, black-box, gradient-based attacks)
Evaluation of robustness and manipulation resistance Option B captures these requirements precisely.
Options A, C, and D do not address AI-specific attack vectors.

References:
AAISM Study Guide - AI Security Testing and Adversarial Evaluation.