ISACA · AAISM
Validates the ability to manage AI security across three domains: AI governance and program management, AI risk management including threats and supply chain issues, and AI technologies and controls, covering security architecture design and model lifecycle management.
Practice Questions
600
≈ 4 practice exams
Duration
150 minutes
Passing Score
450/800
Difficulty
AssociateLast Updated
Feb 2026
Use this AAISM practice exam to prepare for ISACA Advanced in AI Security Management (AAISM) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for ISACA AAISM, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The ISACA Advanced in AI Security Management (AAISM) is the first and only AI-centric security management certification, launched by ISACA in August 2025. It validates a security professional's ability to manage enterprise-wide AI adoption while identifying, assessing, monitoring, and mitigating AI-specific risks. The credential covers three interconnected practice areas: AI governance and program management, AI risk management including supply chain and threat landscape considerations, and AI technologies and controls encompassing security architecture, data lifecycle management, and safety controls for AI systems.
AAISM was developed in direct response to the accelerating pace of AI tool adoption in enterprises, which frequently outpaces organizational policy and security frameworks. Rather than replacing existing security credentials, it layers AI-domain expertise on top of proven security management foundations. The exam tests 22 core competencies spanning governance frameworks, vendor oversight, incident response for AI systems, and security architecture design specific to AI model lifecycles.
AAISM is exclusively designed for experienced IT security professionals who already hold an active CISM (Certified Information Security Manager) or CISSP (Certified Information Systems Security Professional) credential — these are hard prerequisites, not recommendations. Candidates should also have hands-on experience assessing, implementing, and maintaining AI systems within an enterprise context.
The certification is well-suited for security managers, CISOs, security architects, and risk advisors who are responsible for governing or advising on AI adoption within their organizations. It targets professionals seeking to formalize and validate their AI security expertise as organizations increasingly integrate AI into critical operations, and who need to bridge the gap between traditional security management practices and emerging AI-specific threat landscapes.
Candidates must hold an active CISM or CISSP certification at the time of exam registration — this is a mandatory requirement with no exceptions. There is no formal application process prior to registering for the exam, but ISACA expects candidates to have demonstrated experience in security or advisory roles and some practical expertise with AI systems, including assessing AI risks and implementing or maintaining AI-driven solutions.
While no specific number of years of experience is mandated beyond what CISM or CISSP already require, the exam content assumes familiarity with enterprise security governance, risk management frameworks, and at least a working knowledge of AI technologies, data pipelines, and machine learning model lifecycles. Professionals newer to AI who hold CISM or CISSP should supplement their candidacy with hands-on AI exposure before attempting the exam.
The AAISM exam consists of 90 multiple-choice questions and must be completed within 150 minutes (2.5 hours). It is delivered as a computer-based exam, available either at authorized PSI testing centers worldwide or via live remote proctoring. Note that residents of India, Mainland China, and Hong Kong are restricted to in-person testing at PSI centers and cannot use remote proctoring.
The passing score is 450 on a scale of 800. Exam registration is continuous with no application windows — candidates can register at any time and have a 12-month eligibility window from the date of registration to schedule and sit the exam. Exams can be scheduled up to 90 days in advance and as early as 48 hours after payment is confirmed. The member exam fee is US$459 and the non-member fee is US$599, plus a US$50 application processing fee required after passing to obtain the certification.
AAISM positions certified professionals as specialized experts at the intersection of enterprise security management and artificial intelligence — a niche that is rapidly growing in organizational demand as AI adoption accelerates across industries. The credential supplements the widely respected CISM and CISSP certifications with validated AI-specific expertise, making holders distinctly qualified for roles such as AI Security Manager, Chief AI Security Officer, Security Architect (AI/ML), and AI Risk Advisor. It also strengthens the candidacy of existing CISOs and security directors who need to demonstrate governance competence over AI-driven business transformation.
ISACA has positioned AAISM as the definitive credential for security managers navigating AI governance — a role that did not exist at scale five years ago but is now embedded in enterprise risk and compliance programs globally. As regulators in the EU (AI Act) and other jurisdictions codify AI security and governance requirements, certified professionals are increasingly sought to operationalize compliance. While specific salary benchmarks for AAISM holders are not yet widely published given the credential's 2025 launch, it builds directly on CISM and CISSP — both of which consistently rank among the highest-paying IT certifications globally — and adds a premium AI specialization layer that is expected to command meaningful salary differentiation in the market.
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 4 full-length practice exams.
Preview — answers shown1. A CISO develops AI-specific security awareness training covering acceptable use, risks, and security practices. During training design, which two topics are MOST critical for employees to understand regarding generative AI usage? (Select two!)
Multiple correct answersExplanation
Understanding that public AI tools may use input data for training is critical to prevent sensitive data leakage, and recognizing prompt injection risks helps employees avoid accidentally creating security vulnerabilities. Advanced Python programming is for developers, not general awareness training. Statistical concepts like bias-variance tradeoff are too technical for general employee training. Memorizing NIST AI RMF details is unnecessary for basic security awareness, which should focus on practical risks and safe usage behaviors.
2. An AI incident response team follows the Coalition for Secure AI (CoSAI) framework for handling a suspected model inversion attack against a production ML system. During the Detection and Analysis phase, which AI-specific indicators of compromise should the team prioritize monitoring? (Select two!)
Multiple correct answersExplanation
Model inversion attacks attempt to extract sensitive training data information by analyzing model outputs. AI-specific IOCs include unusual API query patterns showing systematic attempts to reconstruct training samples through prediction analysis, and model confidence scores indicating probing of decision boundaries to infer training data characteristics. These require AI-specific telemetry monitoring beyond traditional security tools. Network port scanning is a generic network security indicator unrelated to model inversion. Memory state changes are relevant for other AI attacks but not specifically model inversion. Standard malware signatures detect traditional threats, not model inversion which exploits legitimate model functionality.
3. A CISO develops key performance indicators to measure AI security program effectiveness following ISACA supporting task 18. The organization operates production ML models for fraud detection, customer segmentation, and demand forecasting. Which combination of metrics provides the MOST comprehensive measurement of AI security posture? (Select two!)
Multiple correct answersExplanation
Comprehensive AI security program measurement requires both operational effectiveness metrics and governance coverage metrics. Mean time to detect AI-specific security incidents measures the program's ability to identify threats including model drift, adversarial attacks, and data poisoning in production systems. Percentage of AI systems with documented risk assessments and approved risk treatment plans measures governance maturity and ensures systematic risk management across the AI portfolio. Together these metrics assess both reactive security capabilities and proactive risk management. Total model count is an inventory metric, not a security measure. Training completion counts measure awareness but not program effectiveness. Infrastructure cost per training iteration is an efficiency metric unrelated to security posture.
4. A deployed natural language processing model shows declining F1 scores over six months. Investigation reveals that the relationship between input features and target outputs has fundamentally changed due to evolving language patterns and user behavior. Which type of drift has occurred and what detection method is MOST appropriate? (Select one!)
Explanation
Concept drift occurs when the underlying relationship between input features and target outputs changes, even if input distributions remain stable. This is evidenced by declining F1 scores despite similar input patterns as user behavior and language evolve. DDM and EDDM are specifically designed to detect concept drift by monitoring model error rates and the distance between prediction errors over time. Data drift (covariate shift) refers to changes in input feature distributions while relationships remain constant. Prediction drift detects changes in output distributions without necessarily identifying concept relationship changes. Feature drift addresses individual feature changes rather than the fundamental X to Y relationship.
5. An organization discovers employees are using 400+ unauthorized AI tools with an average lifespan of 400 days before detection. Analysis shows 68% use free-tier services via personal accounts and 57% input sensitive company data. What is the PRIMARY risk category this represents? (Select one!)
Explanation
Shadow AI refers to unauthorized AI tools, agents, models, or services used without organizational visibility. The scenario describes classic shadow AI characteristics: unsanctioned tools, personal accounts, sensitive data exposure, and extended undetected usage. Shadow AI creates data leakage risk ($670,000 higher breach costs), compliance violations, and accountability gaps. Detection requires Cloud Access Security Brokers, API monitoring, and browser extension detection. Governance approaches include tool whitelisting and employee training. Third-party vendor risk applies to authorized vendors. Model drift addresses performance degradation. Supply chain risk focuses on compromised dependencies rather than unauthorized tool usage.
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