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

AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.

AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:
* improved efficiency
* reduced cost
* reduced risk
* business growth
Training (B) and risk optimization (C) are important but do not guarantee stakeholder support. A roadmap (D) guides planning but does not secure buy-in.
References: AAISM Study Guide - AI Governance; Stakeholder Engagement & Value Communication.

AAISM prioritizes training data suitability--lawful sourcing, provenance, quality, representativeness, and safety--especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.

Training data obtained from untrusted vendors may contain maliciously manipulated or corrupted data designed to influence model behavior. This creates a high risk of poisoned data insertion, which can compromise the integrity, reliability, and security of the AI model.