Migrating the LLM workload to a private instance and restricting access most effectively reduces the risk of sensitive patient data leakage. This approach provides stronger control over data handling, processing environments, access management, and exposure pathways, making it the most appropriate risk reduction measure for healthcare data.

For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance.
AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.

A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
* true positives
* true negatives
* false positives
* false negatives
Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture.
Hyperparameter tuning (A) is unrelated.
References: AAISM Study Guide - AI Model Evaluation Metrics; Confusion Matrix.

Verifying data provenance is the best course of action because it helps confirm the origin, authenticity, and integrity of submitted images, reviews, and behavioral data. This directly addresses the risk of malicious or fabricated data contaminating the platform and influencing AI- driven recommendations or analytics.

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