AAISM Questions and Answers

AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data-security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization’s data for training or secondary purposes and that mandate retention limits and secure deletion. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.

According to AAISM, decision trees provide the highest explainability because their structure clearly shows how inputs map to decisions. This is essential in HR applications subject to fairness, bias, and compliance requirements.

SVMs (A) and gradient boosting (D) are less interpretable. Neural networks (B) are explicitly listed as low-explainability models.

AAISM identifies the operational phase as the stage where the AI system is actively deployed and requires continuous monitoring of performance, drift, security, and reliability.

Business alignment (B) belongs to planning and design. Optimization (A) and user feedback (D) are part of development or improvement cycles, not primary operational objectives.

Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive. Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks. Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.

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.

AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.

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 identifies supervised learning as involving:

• labeled categories

• ground-truth datasets

• model training with known outcomes

This aligns exactly with categorizing data and training on labeled datasets.

Reinforcement (B) involves reward feedback loops. Unsupervised (C) uses unlabeled data. "Machine learning" (D) is too broad and not a specific technique.

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.

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 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.

AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non-compliance, discrimination, or financial loss.

Availability (D) is important but secondary to correctness in sensitive decision scenarios. Response time (B) and accessibility ratings (A) do not outweigh the need for accurate, reliable predictions.

According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.

Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human-interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.

Option A addresses data origin/volatility, not explainability. Option B mischaracterizes model behavior as mutable “knowledge” without logs. Option C notes probabilistic foundations but that alone does not make systems inexplicable.

AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:

• attack samples become harder to inject

• anomalies are easier to detect

• corrupted data has reduced influence on model behavior

Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.

Sustained stakeholder sponsorship hinges on demonstrated, quantified business value communicated in terms they own (KPIs, ROI, cost-to-serve, risk-adjusted outcomes). AAISM frames stakeholder alignment as a value-assurance loop: define value hypotheses, measure realized value, and continuously communicate results to sponsors. While an AI roadmap (C), risk optimization (B), and training (A) are important, they support rather than drive ongoing executive buy-in. Quantified value narratives secure resources and reinforce alignment to strategic goals.

AAISM materials emphasize that the primary ethical concern with generative AI is the risk to information integrity. Generative models can create content that appears authentic but is fabricated, misleading, or manipulated. This undermines trust in information ecosystems and can have wide-reaching social, legal, and organizational impacts. While confidentiality breaches and bias are concerns, they are not the central ethical issue inherent to generative models. Availability is less relevant in this context. The most pressing concern is that generative AI may compromise the integrity of information.

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.

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.

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.

AAISM recommends that AI KPIs should emphasize:

• Error rates — measure correctness and reliability

• Bias detection metrics — assess fairness and harm risk

These are the core governance KPIs for AI systems interacting with end users.

Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business-focused. F1 score (D) is useful but not as critical as bias monitoring.

AAISM stresses that AI systems and their supporting infrastructure must be explicitly included in disaster recovery and continuity planning, since disruptions to models, feature stores, or pipelines can halt critical business functions.

Explainability (A) and retraining (B) are operational improvements, not continuity mechanisms. Multi-zone redundancy (D) improves availability but does not represent complete BCP integration.

Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage—directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. “Ensure responsible use” is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.

AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.

Validating outputs (B) supports accuracy but is not trust-restoring. Monitoring (C) and prioritization (D) relate to operations, not trust rebuilding.

The AAISM study materials highlight that AI-powered security tools provide the greatest benefit by reducing false positives in monitoring and access control systems. This improves efficiency, prevents alert fatigue, and enables security teams to focus on true threats. While timely analysis and incident response are benefits, they are not unique to AI-based tools and can be achieved with traditional methods. AI also does not remove the need for data classification, as classification underpins governance and compliance. The standout advantage is the improved accuracy and reduced false positives provided by AI.

AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.

While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.

AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.

Output moderation detects:

• maliciously influenced responses

• unsafe outputs

• security-policy violations

IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine-tuning (A) cannot guarantee full prevention.

When preventive input hardening isn’t feasible for LLMs, AAISM prescribes compensating detective and corrective controls—notably human review and annotation of outputs prior to downstream action—to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.

AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for non-critical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.

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.

The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage—employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.

AAISM stresses that AI tools must align with the organization’s existing control objectives and governance requirements, ensuring consistency with risk management, detection philosophy, and operational processes.

Integration with SIEM (D) is important but secondary. Anomaly detection (B) is a feature, not an architectural requirement. Automated orchestration (A) is optional.

When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.

AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.

Pseudonymization (B) and minimization (D) reduce exposure but do not prevent inversion. Output monitoring (A) detects anomalies but doesn’t block reconstruction.

AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations. Full autonomy (A) risks non-compliance; simply mirroring legacy workflows (B) may not meet current obligations; broad auto-containment (C) lacks necessary oversight controls.

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 highlights threat modeling and supply chain integrity checks as key controls for managing AI-specific risks, including hidden backdoors in third-party models, libraries, or fine-tuning artifacts. The official guidance states that organizations should “identify adversarial insertion points, verify component integrity, and continuously test for malicious behaviors introduced via external components.” This is precisely what option B describes. Simply using open-source (A) does not guarantee security; code can still contain malicious modifications. Disabling runtime logs (C) would reduce visibility, making backdoor detection harder. Choosing unsupervised learning (D) is a modeling approach and has no inherent relation to backdoor risk reduction. The explicit combination of AI-focused threat modeling and integrity verification of external components is the recommended best practice to mitigate this class of attack.

In AAISM, usage of AI for activities involving personal data and profiling, such as personalized advertising, is explicitly mapped to stringent regulatory and compliance requirements (e.g., data protection, consent, profiling limitations, fairness obligations). The material notes that these activities may trigger “heightened regulatory scrutiny, mandatory impact assessments, and potential penalties for non-compliance.” While reputational (B), fraud (A), and commercial (C) risks are all relevant, the primary, non-optional constraint is compliance with applicable regulations governing personal data, automated profiling, and targeted content. Failure in this area can lead not only to reputational harm but also to legal sanctions, enforced remediation, and operational restrictions. Therefore, regulatory risk is identified as the most important consideration when deploying generative AI for personalized advertising.

Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.

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.

In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.

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 lists adversarial training as the strongest method to harden models against input manipulation attacks. By exposing models to adversarial examples during training, the system learns to resist evasion techniques.

Access restriction (B) protects confidentiality, not detection evasion. Monitoring (C) is reactive, not preventive. Differential privacy (D) protects individual data, not adversarial inputs.

For pre-delivery phishing detection and classification, the most appropriate capability is NLP—tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.

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.

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.

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.

AAISM identifies training as the phase with the highest inherent risk because this is where:

• data poisoning can occur

• sensitive data may be exposed

• bias can be introduced

• model inversion risks originate

• security and privacy vulnerabilities are embedded

Preparation (C) carries risk but is less critical. Maintenance (B) and monitoring (A) involve operational safeguards, not foundational risk creation.

AAISM notes that high-security industries (e.g., aerospace) should prefer private, controlled environments with restricted data exposure. Developing a private LLM for non-critical workloads minimizes operational and security risk while enabling innovation.

Public LLMs for critical functions (C) violate safety expectations. Purchased datasets (D) introduce unknown provenance. Outsourcing (B) increases third-party risk.

AAISM identifies continuous monitoring of AI outputs—especially generative outputs—as the most effective security control, ensuring that violations, unsafe responses, data leakage, and policy-breaking behavior are detected and corrected.

A kill switch (C) is a last-resort measure, not a primary control. Exceeding benchmarks (A) does not ensure relevance. Validating training data (D) is important but insufficient for generative output risks.

AAISM explains that model cards provide structured documentation about AI models, including:

• intended use cases

• training data characteristics

• ethical considerations

• known limitations

• risk factors

• performance benchmarks

They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).

Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance. Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.

AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B) describes perturbation of predictions, not training; model weight theft (C) is model extraction, not poisoning.

The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.

AAISM describes data splitting as the process of dividing datasets into:

• training

• validation

• test sets

This is essential for reducing overfitting and ensuring robust evaluation.

Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test data.

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.

AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.

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 classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth. Unsupervised (B) discovers structure without labels; reinforcement (A) optimizes behavior via rewards; “machine learning” (C) is the broad field, not the specific technique.

AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for attackers. As such, it is identified as the most easily exploited vulnerability.

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.

The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing. Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.

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 governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.

According to AAISM, hyperparameter tuning uses validation data, not training or test data. Validation datasets are specifically designed to evaluate different hyperparameter configurations without contaminating the training or testing sets.

Training data (C) teaches the model. Test data (D) is used only for final evaluation. Configuration data (B) is unrelated to performance optimization.

AAISM directs that potential privacy, ethical, or compliance risks must be escalated to the AI Governance Panel, the body responsible for oversight, risk approval, and corrective action.

Fine-tuning (B) is premature and may worsen risk. Code review (C) does not address model-level inference issues. Escalating directly to the CIO (D) bypasses the required governance process.

AAISM directs that AI adoption in security operations be governed through explicit operating models and RACI-mapped responsibilities spanning security operations, data science/ML, platform engineering, privacy, and compliance. Role clarity comes from updating the security program to codify AI-specific responsibilities (model monitoring, incident handling for AI failures, data governance, change control for models, bias/fairness reviews, supplier assurance) rather than deferring implementation, outsourcing core accountability, or relying on generic certifications. This ensures measurable accountability, reduces hand-off risk, and aligns day-to-day SOC practices with AI control objectives.

AAISM defines deepfakes as manipulated media where individuals appear in synthetic or altered video/audio. Indicators include:

• blurred or inconsistent facial rendering

• mismatched frames

• low-quality or distorted transitions

These characteristics match the scenario provided.

Hallucinations (A) relate to model outputs, not video manipulation. Drift (B) affects model performance. Poisoning (C) affects training data, not video content.

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.

AAISM directs organizations to embed security, safety, and compliance controls at design time (“secure-by-design” and “shift-left”), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.

AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.