Isaca Updated AAIA Exam Questions and Answers by mia-rose

" Model degradation " (or model decay) happens when a model ' s performance slowly worsens because the real-world environment has changed since its last training. The most effective safeguard is " Periodic human reviews. " Humans can identify " contextual shifts " or " common-sense errors " that automated monitoring might miss. This " Human-in-the-Loop " (HITL) control ensures that the AI’s decisions remain grounded, justifiable, and accurate over time. Relying on model-generated data (Option A) can actually accelerate degradation through a phenomenon known as " Model Collapse, " where the AI begins to learn from its own mistakes.

Reinforcement Learning (RL) is characterized by an " agent " (the robot) that learns to achieve a goal in a dynamic environment through trial and error. It receives " rewards " for correct movements and " penalties " for errors, allowing it to optimize its " Policy Function " over time. Unlike supervised learning (Option A), which requires a pre-labeled dataset, RL allows for continuous improvement based on real-time feedback. The ISACA AAIA™ Study Guide identifies RL as the standard for robotics and autonomous systems where the " correct " action depends on the state of the physical environment.

Excessive agency occurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is to minimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA’s guidance on limiting AI autonomy , applying least-privilege principles, and restricting unnecessary actions.

Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open-source extensions (D) improve transparency but do not inherently control excessive agency. Scope limitation is the most targeted and effective mitigation.

While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the " AI Policies " themselves are the most comprehensive support for governance objectives. Policies provide the " enforceable standards " for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization’s AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the " Source of Truth " that guides committees and stakeholders in their decision-making processes.