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. An organization implements ISACA's six-step due diligence process for third-party AI vendor management. After planning and supplier identification, the security team conducts comprehensive vendor assessment. The due diligence depth and rigor should be adjusted based on which primary factor? (Select one!)
Explanation
Due diligence rigor should be proportionate to the potential business impact and risk level of the AI solution being procured. High-impact systems processing sensitive data or making critical decisions require more extensive assessment than low-risk applications. Vendor company size does not directly correlate with AI security capabilities or risk. Geographic location may affect regulatory compliance but is secondary to risk-based assessment. Marketing materials and sales presentations provide limited insight into actual security practices and should not drive due diligence depth.
2. A pharmaceutical company implements robust learning algorithms to protect their drug discovery AI from data poisoning. Which three defensive techniques are characteristic of robust learning approaches? (Select three!)
Multiple correct answersExplanation
Robust learning specifically refers to statistical techniques that make models resistant to poisoned or outlier data. Trimmed mean squared error loss excludes extreme values that may represent poisoning attempts. Median-of-means tournaments aggregate updates using robust statistical estimators resistant to manipulation. Model ensembles reduce individual model compromise impact through voting mechanisms. These three techniques directly address statistical robustness. Differential privacy protects individual data point privacy but is not a robust learning technique. Homomorphic encryption enables computation on encrypted data but does not address statistical robustness to poisoning. Adversarial training improves robustness to evasion attacks, not data poisoning.
3. A Chief AI Officer establishes an AI governance committee with responsibility for overseeing AI implementation and ensuring ethical alignment. When determining committee membership composition, which three factors are most critical for effective governance? (Select three!)
Multiple correct answersExplanation
Technical AI/ML expertise ensures the committee understands model capabilities and limitations when making governance decisions. Ethical and philosophy background provides the foundation for translating broad principles into actionable guidance and identifying moral implications. Legal and regulatory knowledge ensures compliance with frameworks like the EU AI Act, GDPR, and sector-specific requirements. These three areas form the core competencies required for AI governance committees as documented in NIST AI RMF and ISO 42001 standards. Marketing experience and financial authority, while valuable for business operations, are not core governance competencies. Diverse perspectives are important but represent a characteristic of membership selection rather than a domain expertise requirement.
4. An organization adopting IEEE standards for ethical AI development must integrate values-based engineering methodology to ensure ethical considerations are systematically addressed throughout system design. Which IEEE standard provides the framework for this values-based engineering approach? (Select one!)
Explanation
IEEE 7000-2021 is correct because it specifically establishes the Value-Based Engineering methodology for systematically integrating ethical values throughout system design and development. IEEE 7001 addresses transparency rather than the overarching methodology. IEEE 7003 focuses specifically on algorithmic bias rather than comprehensive value integration. IEEE 7009 addresses fail-safe design for specific autonomous system safety concerns. IEEE 7000-2021 provides the foundational framework that guides how organizations systematically incorporate ethical considerations across the entire development lifecycle.
5. A multinational corporation establishes an AI ethics committee to oversee responsible AI deployment. The CISO recommends an internal board structure with employee members, while the Chief Legal Officer advocates for an external board with independent members. Which factor should be the PRIMARY consideration when selecting the committee composition? (Select one!)
Explanation
The primary consideration is balancing organizational proximity with objectivity. Internal boards provide deep understanding of company culture, systems, and constraints, enabling faster decision-making and practical guidance. External boards offer independence from internal politics and conflicts of interest, providing unbiased ethical oversight. According to IBM IBV research, 47% of organizations have established generative AI ethics councils, with successful implementations balancing both perspectives. The choice depends on organizational maturity, risk appetite, and specific governance needs. ISO 42001 does not mandate external boards. Cost reduction alone does not ensure effective governance. Committee composition significantly impacts governance quality and ethical oversight effectiveness.
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