AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
- model behavior
- compliance gaps
- security improvements
- governance alignment

The most effective SOC application of AI is in detecting subtle, hard-to-find attack patterns that reduce false negatives.
AAISM technical control guidance notes that AI in SOCs is best applied to:
* Enhance detection accuracy and sensitivity to anomalies.
* Assist analysts in identifying hidden patterns that traditional rule-based systems miss.
* Augment--not replace--human decision-making for high-confidence outcomes.

The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations

AAISM states that AI agent security training should focus on the unique risks of agentic systems, which include:
* prompt injection
* memory control and context hijacking
* unsafe tool execution (agents triggering unauthorized actions)
These risks are specific to autonomous or semi-autonomous AI agents.
Bias, fairness (B) and output moderation (C) are important but not the most critical for agent security. API abuse and plug-in risk (D) matter but are secondary.
References: AAISM Study Guide - Agentic AI Security; Prompt Injection and Tool Execution Risks.

AAISM highlights that uncontrolled access to generative AI in critical systems introduces the highest level of risk, as such models can:
* expose sensitive data
* execute unintended actions
* be manipulated through injected prompts
* cause operational instability
Monitoring (A) is important but not the core risk. Bias (B) is significant but secondary in critical systems.
Regulatory enhancements (C) are indirect.
References: AAISM Study Guide - Generative AI Operational Risk; Access Control Priority.