AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management™ (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)

An AI impact assessment prioritizes alignment with stated business objectives and intended purpose of the AI use case. Establishing whether the AI system measurably supports defined objectives (value, outcomes, success criteria) is the primary lens before assessing secondary concerns (workforce effects, awareness initiatives, reputational considerations). Governance requires confirming purpose limitation, success metrics, and risk-benefit trade-offs tied to organizational objectives; only then are downstream impacts evaluated.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance & Impact Assessments; Purpose and Objective Definition; Risk-Benefit Evaluation. AAISM Study Guide - Business Alignment, Intended Use, and Success Criteria in AI Impact Assessments.

The AAISM governance framework specifies that the foundation of continuous monitoring is real-time tracking of key risk indicators. This ensures immediate detection of deviations, model drift, and operational anomalies. Automated alerts, dashboards, and reporting templates all support monitoring, but they rely on the presence of accurate, real-time KRI measurement as their source. Without live monitoring, the other controls are reactive rather than proactive. The most important course of action in establishing effective continuous monitoring is therefore real-time KRI tracking.
References:
AAISM Study Guide - AI Governance and Program Management (Continuous Monitoring and Assurance) ISACA AI Risk Guidance - Monitoring Key Risk Indicators

Output filtering and moderation directly address the risk of harmful, offensive, or biased content by reviewing and controlling generated responses before they reach users. This helps enforce safety, ethical, and compliance standards in LLM outputs.

AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.