AAISM Exam Dumps - ISACA Advanced in AI Security Management (AAISM) Exam

AAISM directs organizations to embed security, safety, and compliance controls at design time (“secure-by-design” and “shift-left”), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.

AAISM identifies the strongest immediate control for preventing data leakage into generative AI systems as technically restricting or blocking user entry of sensitive data.

Audits (A) are retrospective. Testing tools (C) does not prevent user error. Regulatory compliance (D) does not stop operational leakage.

AAISM’s risk management content describes red-teaming in generative AI as focused on deliberately crafting adversarial prompts to test whether the model produces unexpected or undesired outputs that violate safety, integrity, or compliance standards. The goal is not to stress system performance or randomly disrupt outputs, but rather to uncover vulnerabilities in how the model responds to manipulative inputs. This allows organizations to improve resilience against prompt injection, jailbreaking, or harmful content generation. The correct answer is therefore generate outputs that are unexpected using adversarial inputs.

The data Preparation phase—covering sourcing, collection, labeling, cleansing, and provenance—presents the greatest inherent risk because it is where privacy, consent, representativeness, bias, quality, lineage, and legality must be established. Decisions and defects here propagate into training and downstream use, amplifying ethical, regulatory, and accuracy risks.

While Training can introduce additional risks (e.g., poisoning, leakage), these are frequently mitigated by controls that depend on having trustworthy prepared data. Monitoring and Maintenance are later-life-cycle phases oriented toward detection and correction; they are critical but inherently rely on the foundation set during Preparation.

AAISM training guidance specifies that social engineering is the awareness topic most impacted by AI-enabled risks. With generative AI and deepfake technologies, attackers can create highly convincing phishing messages, synthetic voices, or fake executive requests, increasing the sophistication of social engineering attacks. Clean desk policies, insider threat awareness, and authentication procedures remain relevant but are not directly altered by AI advancements. The most likely revision to employee awareness programs in the AI era is therefore enhanced social engineering awareness.

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

An MSA (A) supports contracts but does not manage operational change. Shared responsibility (C) describes roles, not change control. Data minimization (D) reduces exposure but doesn't control model updates.

The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain’s immutability and traceability make it the recognized technology for mitigating deepfake challenges.

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.