AAISM Exam Dumps - Isaca AI-Centric Security Management Questions and Answers

According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization’s risk appetite.

AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.

Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.

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 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.

AAISM describes prompt injection as an attack where adversaries craft inputs that manipulate model behavior or override system instructions. The recommended control pattern is to implement robust input validation and constraint mechanisms that sanitize and structure user inputs before they are processed by the model. The guidance includes techniques such as template-based prompts, restricted instruction sets, and validation rules to filter malicious or out-of-scope content. Human-in-the-loop (A) provides oversight but may not scale and is not a primary technical protection. Increasing model parameters (C) relates to capacity and performance, not security. Continuous monitoring (D) is important for detection but does not prevent prompt injection at the point of entry. Therefore, input validation, combined with controlled prompt construction, is identified as the best direct control against prompt injection attacks in customer-facing chatbots.

AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.

Per AAISM’s ML lifecycle controls, hyperparameter tuning is performed on the validation set, reserving the test set strictly for final, unbiased performance estimation. The training set is used to fit parameters; the validation set guides model selection and hyperparameter optimization; the test set is untouched until the end to prevent leakage and optimistic bias. “Configuration” is not a dataset type in the lifecycle split.

The AAISM material frames an AI impact assessment as broader than a technical model review. It emphasizes that governance of AI includes assessing societal, environmental, and human-rights impacts in addition to organizational risk. The official guidance notes that high-level impact assessments must explicitly consider “potential consequences for individuals, communities, and the environment” as part of responsible AI governance. Activities such as testing/validation (A) and reproducibility (B) are critical in the development lifecycle but are not, by themselves, sufficient to constitute an impact assessment. A CSA (C) is focused on security controls, which is narrower than overall impact. The mandatory element that differentiates an AI impact assessment is the assessment of environmental and societal consequences, making option D the correct answer in alignment with governance expectations.

AAISM stresses that AI-enabled security programs require updated governance structures, including defined cross-functional roles across security, data, development, and operations teams.

Role clarity emerges from updated policies, responsibilities, and oversight—not from delaying adoption (A), outsourcing (D), or simply training (B).