Our AAISM study materials deliver authentic and updated exam questions for the Accredited Agile Information Security Manager certification. Each question is supported with verified answers, detailed explanations, and helpful references to strengthen your understanding. With access to our online practice platform and sample questions, professionals trust Cert Empire to prepare thoroughly and succeed in the AAISM exam.
All the questions are reviewed by Laura Brett who is a AAISM certified professional working with Cert Empire.
Exam Questions
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Q: 1
As organizations increasingly rely on vendors to develop AI systems, which of the following is the
MOST effective way to monitor vendors and ensure compliance with ethical and security standards?
Options
Correct Answer:
A
Explanation
Conducting regular, independent audits is the most effective method for monitoring vendor compliance. Audits provide direct, verifiable assurance that the vendor's processes, controls, and outputs align with contractually mandated ethical and security standards. This approach moves beyond trust-based models like self-attestation, offering a structured and evidence-based mechanism to assess the entire AI development lifecycle. It allows the contracting organization to proactively identify gaps, enforce accountability, and ensure that principles of fairness, transparency, and security are being actively implemented, rather than just claimed.
Why Incorrect
B. Requiring vendors to monitor their adherence to ethics and security standards: This approach lacks independent verification and creates a potential conflict of interest, making it less reliable than an external audit.
C. Mandating that vendors share source code and AI documentation with the contracting party: While useful for due diligence, code and documentation review is a static, point-in-time assessment and does not guarantee ongoing process compliance.
D. Allowing vendors to self-attest ethical AI compliance and implement benchmark monitoring: Self-attestation is the weakest form of assurance, as it is not independently verified and may not accurately reflect actual practices.
References
1. ISACA. (2023). Artificial Intelligence Audit Toolkit. In the section on "Third-Party Management
" the toolkit emphasizes the need for organizations to "Perform audits of the third party" and "Review third-party attestation reports (e.g.
SOC 2)" to gain assurance over the controls at the vendor. This directly supports the practice of conducting audits (Option A) over relying on vendor self-monitoring or attestation (Options B and D).
2. ISACA. (2021). Auditing Artificial Intelligence. This white paper states
"The audit and assurance professional should obtain an understanding of the third-party relationships and related processes to determine whether the enterprise has implemented appropriate governance and monitoring controls." It highlights the auditor's role in verifying these controls
which is the essence of an audit. (Page 18
Third-Party Management).
3. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0). In the "Govern" function
section 4.4 "AI System and Third-Party Relationships
" the framework outlines the importance of establishing processes for "monitoring and reviews of third-party providers
" including "audits
and assessments." This underscores that active
independent verification like audits is a core component of managing third-party AI risk. (Page 21).
Q: 2
During the creation of a new large language model (LLM), an organization procured training data
from multiple sources. Which of the following is MOST likely to address the CISO's security and
privacy concerns?
Options
Correct Answer:
B
Explanation
Data minimization is a core privacy and security principle that involves processing only the data that is absolutely necessary for a specific purpose. In the context of training a large language model (LLM), applying data minimization means proactively identifying and removing any sensitive, personal, or proprietary information from the training datasets before the model is trained. This directly addresses the CISO's concerns by fundamentally reducing the risk surface. By ensuring sensitive data is not included, the organization mitigates the possibility of the model memorizing and later exposing this information, thus preventing potential data breaches and ensuring compliance with privacy regulations.
Why Incorrect
A. Data augmentation: This technique is used to increase the volume of training data to improve model accuracy and generalization, not to address security or privacy risks.
C. Data classification: This is a foundational step to identify sensitive data, but it does not, by itself, mitigate the risk; it only categorizes it.
D. Data discovery: This process locates data across systems. Like classification, it is a preliminary step that identifies the problem but does not implement a solution.
References
1. National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). (NIST AI 100-1).
Page 29
Table 7
MAP.T10: In the section on documenting training data
this subcategory explicitly lists "adherence to data minimization principles" as a key element for managing AI risks. This directly supports using data minimization to handle training data concerns.
2. ISACA. (2023). Artificial Intelligence: An Audit and Assurance Framework.
Page 21
Section 3.2.1 Data Governance: The framework states
"Data used to train AI models should be relevant
accurate and appropriate for the intended purpose... It is important to ensure that the data used for training does not contain any sensitive or confidential information that could be inadvertently exposed by the AI model." This aligns with the principle of data minimization to remove unnecessary sensitive data.
3. European Union. (2016). General Data Protection Regulation (GDPR).
Article 5(1)(c): This article establishes "data minimisation" as a core principle of data protection
stating that personal data shall be "adequate
relevant and limited to what is necessary in relation to the purposes for which they are processed." This legal and privacy principle is the direct solution to the CISO's concerns when applied to AI training data.
Q: 3
An organization needs large data sets to perform application testing. Which of the following would
BEST fulfill this need?
Options
Correct Answer:
C
Explanation
Open-source data repositories are centralized platforms (e.g., Kaggle, UCI Machine Learning Repository, Google Dataset Search) that host a wide variety of large, often cleaned and labeled, datasets. They are specifically designed to provide data for research, development, and testing of applications, particularly in the AI and machine learning domains. Using these repositories is the most direct, efficient, and common method for an organization to acquire the large-scale data needed for comprehensive application testing without the significant overhead of collecting and preparing the data from scratch.
Why Incorrect
A. AI model cards are documents that provide transparency about a model's performance, limitations, and ethical considerations, not a source of raw data.
B. Incorporating data from search content is impractical; it is often unstructured, may have copyright or privacy restrictions, and requires extensive effort to scrape, clean, and label.
D. AI data augmentation is a technique to artificially increase the size of an existing dataset. It cannot be used without an initial dataset to augment.
References
1. ISACA
Artificial Intelligence Audit and Assurance Framework
2023. In the "AI Data Life Cycle" section
the framework discusses "Data Sourcing and Acquisition
" emphasizing the need to obtain relevant and sufficient data for training and testing AI models. Open-source repositories are a primary means of fulfilling this requirement for large-scale data needs.
2. Ng
A. (2023). Machine Learning (CS229) Course Materials. Stanford University. Lecture notes and project guidelines frequently reference the use of public datasets from repositories like the UCI Machine Learning Repository and Kaggle as standard practice for obtaining data for model development and evaluation. (See
for example
project guidelines which list acceptable data sources).
3. Roh
Y.
Heo
G.
& Whang
S. E. (2021). A survey on data collection for machine learning: a big data - AI integration perspective. IEEE Transactions on Knowledge and Data Engineering
33(4)
1328-1347. This paper discusses various data collection methods
highlighting public datasets as a crucial resource. Section 3.1
"Using Existing Datasets
" explicitly states
"The easiest way to collect data is to use publicly available datasets... Many websites share public datasets
An organization concerned about the ethical and responsible use of a newly developed AI product
should consider implementing:
Options
Correct Answer:
C
Explanation
An accountability model establishes a clear governance structure, defining roles, responsibilities, and oversight mechanisms for AI systems. This framework is fundamental to ensuring that an AI product is used ethically and responsibly throughout its lifecycle. It addresses the organization's concerns by creating clear lines of authority for managing risks related to fairness, bias, transparency, and societal impact. This model provides the foundation upon which specific tools and practices, such as model cards and security protocols, are implemented and enforced.
Why Incorrect
A. Model cards are a tool for transparency, which is a component of a responsible AI strategy, but they do not constitute the entire governance framework needed for accountability.
B. Vendor monitoring is relevant only when using third-party AI systems and does not address the organization's internal responsibility for a product it has developed.
D. Security by design is a critical practice focused on protecting the AI system from threats, but it does not cover the full spectrum of ethical considerations like fairness and bias.
References
1. ISACA
"Artificial Intelligence: An Audit and Assurance Framework
" 2023. Page 14
under the "AI Governance Framework" section
states
"A governance framework establishes accountability
roles and responsibilities
and decision rights... It also provides a structure for oversight to ensure that AI systems are aligned with the organization’s ethical principles."
2. National Institute of Standards and Technology (NIST)
"AI Risk Management Framework (AI RMF 1.0)
" January 2023. Section 3.1
"The GOVERN Function
" emphasizes that this function is central to responsible AI. It states
"Governance processes should be in place to ensure accountability for AI risks and their management... This includes assigning roles and responsibilities for all stages of the AI lifecycle."
501-507. This academic paper argues that high-level ethical principles are insufficient without practical implementation through "mechanisms of accountability" and robust governance structures to translate principles into practice. (DOI: https://doi.org/10.1038/s42256-019-0114-4)
Q: 5
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness
of an organization’s AI risk management program?
Options
Correct Answer:
C
Explanation
A Key Risk Indicator (KRI) for an AI risk management program should measure the program's effectiveness in governing AI initiatives and ensuring they adhere to established policies and controls. The "Percentage of AI projects in compliance" is the most direct measure of this effectiveness. It quantifies how well the organization's AI activities are following the prescribed risk management framework, including mandatory assessments, controls, and documentation. A high compliance rate indicates a successful and effective program, while a low rate serves as an early warning that the program is not being implemented properly, increasing overall AI-related risk.
Why Incorrect
A. Number of AI models deployed into production: This is a volume or activity metric. It indicates the scale of AI adoption and potential risk exposure but does not measure the effectiveness of the program managing that risk.
B. Percentage of critical business systems with AI components: This metric measures the organization's inherent risk or attack surface related to AI. It identifies where risk management is crucial but does not evaluate how well it is being performed.
D. Number of AI-related training requests submitted: This is an ambiguous indicator. It could signify a positive culture of risk awareness or, conversely, a lack of adequate foundational training, but it does not directly measure the program's control effectiveness.
References
1. NIST AI Risk Management Framework (AI RMF 1.0): The "Measure" function of the framework is dedicated to tracking risk management effectiveness. It states
"Measurement enables learning from experience and improves the design
development
deployment
and use of AI systems." A compliance metric directly aligns with this goal of evaluating and improving risk management practices. (Source: NIST AI 100-1
January 2023
Section 4.4
"Measure
" Page 21).
2. ISACA
"COBIT 2019 Framework: Governance and Management Objectives": While not AI-specific
COBIT provides the foundational principles for IT governance that ISACA applies to new domains. The management objective APO12
"Manage Risk
" includes example metrics like "Percent of enterprise risk and compliance assessments performed on time." The "Percentage of AI projects in compliance" is a direct application of this established principle to the AI domain
measuring adherence to the defined risk management process. (Source: COBIT 2019 Framework
APO12
Page 113).
3. Thelen
B. D.
& Mikalef
P. (2023). "Artificial Intelligence Governance: A Review and Synthesis of the Literature." Academic literature on AI governance emphasizes the need for "mechanisms for monitoring and enforcement" to ensure compliance with internal policies and external regulations. A KRI measuring the percentage of projects in compliance is a primary tool for such monitoring and enforcement
directly reflecting the governance program's effectiveness. (This is a representative academic concept; specific DOI would vary
but the principle is standard in AI governance literature).
Q: 6
When integrating AI for innovation, which of the following can BEST help an organization manage
security risk?
Options
Correct Answer:
D
Explanation
Adopting a phased approach is the most effective strategy for managing the security risks associated with integrating a novel technology like AI. This method allows an organization to introduce AI capabilities incrementally, starting with pilots or limited-scope projects. Each phase serves as a controlled environment to identify, assess, and mitigate emergent security risks before they can impact the entire enterprise. This iterative process facilitates learning, allows for the refinement of security controls and governance policies, and ensures that the complexities and potential vulnerabilities of AI are understood and managed before scaling up. It is a foundational risk management practice for complex technology adoption.
Why Incorrect
A. Re-evaluating the risk appetite is a critical governance activity that sets tolerance levels but does not, by itself, constitute a management strategy for implementation risks.
B. Seeking third-party advice is a valuable, supportive measure for gaining expertise but is not the primary, overarching strategy for managing the integration process.
C. Evaluating compliance requirements is essential for establishing a baseline and avoiding legal penalties but often fails to address novel, technology-specific security risks not yet covered by regulations.
References
1. ISACA
COBIT® 2019 Implementation Guide
2018. Chapter 3
"The Implementation Lifecycle
" details a seven-phase
iterative approach. Phase 4
"Plan
" and Phase 5
"Design
" emphasize planning for incremental implementation. This phased methodology is a core principle for managing risk during the implementation of any significant IT change
including AI. The guide states
"The lifecycle should be iterative
meaning that the enterprise can cycle through the phases as needed." This directly supports a phased approach for managing complex integrations.
2. ISACA
"Auditing Artificial Intelligence
" White Paper
2021. Page 14 discusses the "AI Journey" and the importance of starting with pilot projects. It states
"The AI journey is a marathon
not a sprint... It is important to start small with a few pilot projects to learn and build momentum." This recommendation to "start small" is the essence of a phased approach to manage the risks and complexities of AI adoption.
3. Hiekkanen
K.
et al. "This Time It’s Different? A Review of the AI Governance Literature." Proceedings of the 53rd Hawaii International Conference on System Sciences
which often implicitly or explicitly recommend iterative and adaptive strategies for AI adoption. The concept of "sandboxing" and pilot programs is highlighted as a key mechanism for exploring AI's potential while containing risks
which is a form of a phased approach.
Q: 7
Which area of intellectual property law presents the GREATEST challenge in determining copyright
protection for AI-generated content?
Options
Correct Answer:
B
Explanation
The greatest challenge in applying copyright law to AI-generated content is determining rightful ownership. Traditional copyright frameworks, such as the U.S. Copyright Act, are predicated on the principle of "human authorship." Because an AI is not a legal person, it cannot be considered an "author" in the legal sense. This creates a fundamental conflict: if there is no human author, it is unclear who, if anyone, owns the copyright. The debate involves whether ownership should fall to the user providing the prompt, the AI developer, the owner of the computing resources, or if the work should enter the public domain. This foundational issue of authorship and ownership must be resolved before other issues, like licensing, can be effectively addressed.
Why Incorrect
A. Enforcing trademark rights is concerned with the branding of the AI system itself (e.g., its name or logo), not the copyright of the content it generates.
C. Protecting trade secrets applies to the AI's underlying algorithms, models, and training data, which are distinct from the copyright status of the AI's output.
D. Establishing licensing frameworks is a secondary issue that depends entirely on first resolving the primary challenge of who holds the ownership rights to be licensed.
References
1. U.S. Copyright Office. (2023). Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence. Federal Register
88(51)
16190-16194.
Page 16192
Section III.A: The guidance states
"the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author." This directly highlights that the lack of human authorship is the central barrier to establishing copyright
and thus ownership.
2. World Intellectual Property Organization (WIPO). (2020
May). WIPO Conversation on Intellectual Property (IP) and Artificial Intelligence (AI): Revised Issues Paper on Intellectual Property Policy. WIPO/IP/AI/2/GE/20.
Page 11
Issue 11: Authorship and Ownership: The paper explicitly poses the core question: "Should copyright be attributed to original literary and artistic works that are autonomously generated by AI? If so
who should be considered the author or owner of the copyright...?" This frames the ownership question as the primary policy challenge for international IP bodies.
3. Stanford University Human-Centered AI (HAI). (2023
September 12). Generative AI and the Future of Work.
In the section on "Intellectual Property
" the report discusses the legal ambiguity surrounding AI-generated works
stating
"Current U.S. copyright law protects only works with human authors
leaving the ownership of purely AI-generated content in a gray area." This reinforces that ownership is the central unresolved issue.
Q: 8
When documenting information about machine learning (ML) models, which of the following
artifacts BEST helps enhance stakeholder trust?
Options
Correct Answer:
C
Explanation
A model card is a standardized documentation artifact designed to increase transparency and accountability for machine learning models. It provides a structured summary of a model's intended uses, performance metrics (often disaggregated across different groups), limitations, ethical considerations, and the data used for training and evaluation. By presenting this crucial information in a concise and accessible format, model cards enable various stakeholders—including developers, policymakers, and end-users—to understand the model's capabilities and risks. This transparency is a cornerstone for building trust in AI systems, as it demonstrates due diligence and provides a basis for informed decision-making.
Why Incorrect
A. Hyperparameters: These are low-level technical settings for the training algorithm. They are not meaningful to most stakeholders and do not describe the model's real-world performance or impact.
B. Data quality controls: While essential for building a reliable model, these are processes and metrics related to the input data, not a comprehensive summary document about the finished model itself.
D. Model prototyping: This is an early, experimental phase in the model development lifecycle. A prototype is not a formal documentation artifact for a deployed model and lacks the rigorous evaluation needed for trust.
References
1. ISACA
Artificial Intelligence Security and Management (AAISM) Study Guide
1st Edition
2024. Chapter 3
"AI Model Development and Training
" emphasizes the need for comprehensive documentation to ensure transparency and accountability. It identifies model cards as a key tool for documenting model details
performance
and limitations to communicate effectively with stakeholders.
2. Mitchell
M.
Wu
S.
Zaldivar
A.
et al. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness
Accountability
and Transparency
pp. 220-229. This foundational academic paper introduces model cards as a framework to "encourage transparent model reporting" and provide stakeholders with essential information to "better understand the models." (DOI: https://doi.org/10.1145/3287560.3287596)
3. Stanford University
Center for Research on Foundation Models (CRFM). (2023). Transparency Section. The courseware and publications from Stanford's AI programs
such as those from the CRFM
consistently highlight the role of artifacts like model cards in achieving AI transparency and building trust. They are presented as a best practice for responsible AI development and deployment.
Q: 9
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which
of the following types of attacks is this an example of?
Options
Correct Answer:
A
Explanation
The scenario describes an attacker crafting specific inputs to bypass an LLM's built-in restrictions and manipulate its output. This technique is known as prompt injection. The attacker injects malicious instructions within the prompt, causing the model to ignore its original system-level instructions and follow the attacker's commands instead. This directly exploits the model's interpretation of input to compromise its output integrity, making it the most accurate description of the attack.
Why Incorrect
B. Jailbreaking is a specific goal or outcome of a prompt injection attack, aimed at bypassing safety and ethical filters, rather than the attack technique itself.
C. Remote code execution involves executing arbitrary code on the server, a more severe and distinct attack that is not inherently part of manipulating LLM text output.
D. Evasion is an adversarial attack that typically causes a model (e.g., a classifier) to make an incorrect prediction, which is different from overriding an LLM's core instructions.
References
1. OWASP Foundation. (2023). OWASP Top 10 for Large Language Model Applications. In the "LLM01: Prompt Injection" section
the vulnerability is defined as: "Prompt injection vulnerabilities allow a malicious user to manipulate the output of a Large Language Model (LLM) through crafted inputs... This can lead to data exfiltration
unauthorized access
or other security breaches."
2. National Institute of Standards and Technology (NIST). (2023). Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST.AI.100-2 E2023). Section 3.2.1
"Data/Input Poisoning
" describes how attackers can manipulate inputs. Prompt injection is cited as a prime example for LLMs where "an attacker can carefully craft a prompt to an LLM to have it ignore previous instructions."
3. Greshake
K.
Abdelnabi
S.
Mishra
S.
Endres
C.
Holz
T.
& Fritz
M. (2023). Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv preprint arXiv:2302.12173. Section 2
"Background
" defines prompt injection as an attack where an "adversary controls a part of a prompt... to manipulate the LLM's behavior." (https://doi.org/10.48550/arXiv.2302.12173)
Q: 10
Which of the following is MOST important to consider when validating a third-party AI tool?
Options
Correct Answer:
B
Explanation
The right to audit is the most critical consideration because it provides the organization with the contractual authority to independently verify and validate the third-party AI tool's performance, security controls, data handling processes, and compliance with legal and ethical standards. Given the often opaque nature of AI models, this right is a fundamental governance mechanism for ongoing assurance. It allows the organization to move beyond relying solely on vendor attestations and certifications, enabling direct assessment against specific internal requirements and risk tolerances, which is the core of validation.
Why Incorrect
A. Terms and conditions: While essential, this is a broad legal document. The right to audit is a specific, crucial clause within the terms and conditions that directly enables validation.
C. Industry analysis and certifications: These are valuable for initial due diligence but are often generic, point-in-time assessments that may not cover the organization's specific use case or risk profile.
D. Roundtable testing: This is a specific, often informal, testing technique. It is only one small component of a comprehensive validation strategy, not the most important overarching consideration.
References
1. ISACA
Auditing Artificial Intelligence
2021. Chapter 5
"Auditing AI Governance and Risk Management
" emphasizes the importance of managing third-party AI risks. It states that contracts with AI service providers should include "clauses for independent security assessments and the right to audit to ensure that the organization’s data are adequately protected and that the AI solution is performing as expected." (Page 63).
2. ISACA
Artificial Intelligence: An Audit/Assurance Framework
2023. In the "GOVERN" function
section 3.4
"Third-Party Management
" discusses the need to "Establish and monitor third-party relationships to ensure that AI-related risks are managed." The right to audit is a primary mechanism for such monitoring and for gaining assurance over the third party's controls and processes.
3. NIST
AI Risk Management Framework (AI RMF 1.0)
January 2023. The GOVERN function (Section 4.1
GOVERN 3) discusses policies and procedures for third-party AI systems. It highlights the need for organizations to have mechanisms to understand and manage risks from externally sourced AI
for which audit and assessment rights are a prerequisite for effective implementation. (Page 21).
What is the ISACA AAISM Exam, and What Will You Learn from It?
The ISACA Artificial Intelligence and Information Security Manager (AAISM) certification is designed for professionals who manage and secure AI-enabled systems within an enterprise environment. It validates your ability to develop, implement, and oversee AI governance, security, and risk management programs, ensuring that AI technologies are used responsibly, securely, and ethically.
Through the AAISM certification, you will gain practical skills in AI risk management, cybersecurity controls for AI systems, data privacy governance, compliance frameworks, and AI lifecycle security. This certification bridges the gap between AI innovation and information security management, empowering professionals to lead secure AI-driven transformations.
Exam Snapshot
Exam Detail
Description
Exam Code
AAISM
Exam Name
ISACA Artificial Intelligence and Information Security Manager Certification
Vendor
ISACA
Version / Year
Current Version
Average Salary
USD $120,000 – $160,000 annually
Cost
USD $275 (Members) / USD $350 (Non-Members)
Exam Format
Multiple-choice and scenario-based questions
Number of Questions
75
Duration (minutes)
120 minutes
Delivery Method
Online remote proctored exam
Languages
English
Scoring Method
Percentage-based
Passing Score
65%
Prerequisites
Recommended: Experience in cybersecurity, IT management, or AI risk governance
Retake Policy
Retakes allowed with ISACA’s standard waiting period
Target Audience
Information security managers, AI project leaders, governance professionals
Certification Validity
Lifetime
Release Date
2024
Prerequisites Before Taking the ISACA AAISM Exam
There are no strict prerequisites for the AAISM exam, but ISACA recommends that candidates have:
Working knowledge of cybersecurity principles and frameworks (e.g., ISO 27001, NIST CSF).
Basic understanding of AI systems, algorithms, and data governance.
Familiarity with risk management practices and regulatory compliance requirements.
Professionals holding certifications like CISM, CRISC, or AAIA will find the AAISM a natural progression in their career.
Main Objectives and Domains You Will Study for ISACA AAISM
The AAISM exam assesses your ability to design, implement, and manage information security and governance programs tailored for AI environments.
Topics to Cover in Each AAISM Exam Domain
Domain 1: AI Security Governance and Frameworks
Establishing governance for AI-driven security systems.
Understanding AI security policies, accountability, and compliance roles.
Applying COBIT 2019 and NIST CSF for AI governance.
Domain 2: AI Risk and Threat Management
Identifying risks unique to AI systems (bias, adversarial attacks, data poisoning).
Building risk mitigation strategies for AI models and data pipelines.
Integrating AI-specific risks into enterprise risk management frameworks.
Domain 3: Secure AI Development and Implementation
Ensuring security throughout the AI lifecycle: design, training, deployment, and maintenance.
Applying privacy-by-design and security-by-design principles in AI projects.
Managing secure data collection, labeling, and model versioning.
Domain 4: Regulatory Compliance and Ethical AI Management
Understanding global AI and data protection regulations (GDPR, ISO/IEC 42001).
Managing AI ethics, transparency, and accountability frameworks.
Establishing internal compliance programs for AI-driven enterprises.
Domain 5: Incident Response and Continuous Improvement
Developing AI-aware incident response plans.
Implementing AI-driven threat detection systems.
Conducting post-incident reviews and continuous improvement for AI resilience.
Changes in the Latest Version of the AAISM Exam
The latest version of the AAISM exam incorporates updates aligned with emerging AI security frameworks and global compliance standards:
Inclusion of Generative AI security controls and LLM governance.
Expanded focus on AI-driven cybersecurity operations.
Updated compliance mappings with ISO/IEC 42001 (AI Management System).
Integration of ethical AI principles in governance and security policies.
These updates ensure the certification remains current with global AI governance and cybersecurity advancements.
Register and Schedule Your ISACA AAISM Exam
You can register for the AAISM exam directly through the official ISACA website.
Steps to register:
Log in or create your ISACA account.
Select AAISM (Artificial Intelligence and Information Security Manager) from the certifications list.
Choose your exam delivery option, online or test center.
Select your exam date and time.
Complete the payment and receive a confirmation email.
Exams are offered on-demand, giving you the flexibility to schedule when ready.
ISACA AAISM Exam Cost, and Can You Get Any Discounts?
Candidate Type
Exam Price (USD)
ISACA Members
$275
Non-Members
$350
ISACA members enjoy discounted pricing and exclusive access to study materials and professional communities.
Get ready with high-quality practice questions and full-length practice tests fromCert Empire, trusted by IT professionals to strengthen exam confidence and understanding.
Exam Policies You Should Know Before Taking the AAISM Exam
Before taking your exam, review ISACA’s official testing policies:
The exam contains 75 multiple-choice and scenario-based questions.
You must score at least 65% to pass.
You may retake the exam following ISACA’s retake policy.
The certification is valid for life.
Exams are delivered via online remote proctoring for convenience.
What Can You Expect on Your ISACA AAISM Exam Day?
On exam day, ensure you have:
A stable internet connection and a quiet environment.
A government-issued ID for identity verification.
The exam features questions that test your ability to manage AI security risks, compliance, and governance in real-world scenarios. You will analyze risk cases, propose mitigation strategies, and apply best practices for securing AI infrastructures.
Your results are displayed immediately after submission, and successful candidates receive a digital certificate from ISACA.
Plan Your AAISM Study Schedule Effectively with 5 Study Tips
Tip 1: Review the ISACA AAISM Study Guide to understand all domains and objectives. Tip 2: Learn AI-related cybersecurity and compliance frameworks such as ISO 42001 and NIST AI RMF. Tip 3: Use practice questions to reinforce your knowledge in each domain. Tip 4: Take timed practice tests fromCert Empire to simulate exam pressure. Tip 5: Review AI governance case studies to strengthen real-world understanding.
Best Study Resources You Can Use to Prepare for ISACA AAISM
ISACA Official AAISM Study Guide
ISACA Online Learning Modules and Webinars
COBIT 2019 and NIST AI Risk Management Framework (AI RMF)
ISO/IEC 42001 AI Management System Standards
Practice Questions and Practice Tests fromCert Empire
Research papers on AI security and governance
Using these materials ensures comprehensive preparation and alignment with ISACA’s official exam framework.
Career Opportunities You Can Explore After Earning ISACA AAISM
The ISACA AAISM certification opens pathways to leadership roles in both cybersecurity and AI governance. You can pursue positions such as:
Information Security Manager (AI Systems)
AI Risk and Compliance Manager
AI Governance Program Lead
Cybersecurity and AI Integration Consultant
Chief AI Security Officer
Enterprise Governance and Risk Director
This certification empowers professionals to manage AI-driven security ecosystems and ensure compliance with global governance standards.
Certifications to Go for After Completing ISACA AAISM
After completing your AAISM certification, consider advancing your credentials with:
ISACA CISM (Certified Information Security Manager)
ISACA CRISC (Certified in Risk and Information Systems Control)
ISACA CGEIT (Certified in the Governance of Enterprise IT)
COBIT 2019 Design and Implementation
ISO/IEC 42001 AI Management Implementer
These advanced certifications enhance your credibility as a strategic leader in AI security and governance.
How Does ISACA AAISM Compare to Other AI and Cybersecurity Certifications?
While technical certifications like CISSP or CompTIA Security+ focus on traditional security operations, the ISACA AAISM uniquely integrates AI risk governance and information security management. It provides a strategic, governance-oriented perspective, preparing professionals to oversee secure AI transformation at the enterprise level.
This makes AAISM one of the most forward-looking certifications for professionals combining AI innovation with cybersecurity leadership.
Strengthen your preparation with authentic ISACA AAISM practice questions and full-length practice tests fromCert Empire.
Prepare effectively, validate your expertise, and lead secure AI governance with confidence.
About AAISM Exam Questions
Why Practice Exam Questions Are Essential for Passing ISACA AAISM Exam in 2025
Passing the AAISM certification isn’t about memorizing terms or rot learning, it’s about developing the aptitude required of an AI system management and assurance professional. Loaded with detailed explanations and extensive references, Cert Empire’s AAISM Exam Questions are designed to help you think like an actual AI systems governance and assurance expert. These practice questions mirror the ISACA exam pattern, guiding you through what’s required to pass the exam on your first attempt.
Prepare Smarter with Exam Familiar Quiz
The AAISM exam is challenging and broad, but consistent practice transforms that difficulty into strength. By regularly solving real exam-style questions, you’ll improve your pacing, reduce anxiety, and recognize recurring question logic. Over time, the format will feel second nature, allowing you to focus on accuracy instead of uncertainty on exam day.
Master Every Domain with Real Exam Logic
The AAISM practice questions cover all official domains in the correct proportion. This means you’re not just preparing one domain, but all of them, making your exam preparation comprehensive. For broader learning, you can also explore complete ISACA certifications available on our site.
What’s Included in Our AAISM Exam Prep Material
It’s not just a question blob that we offer, but a whole experience that transforms your exam preparation. Here is exactly what you get:
PDF Exam Questions
Instant Access: Start preparing right after purchase with immediate delivery.
Study Anywhere: Access the soft form questions from your phone, laptop, or tablet.
Printable Format: Ideal for offline review and personal note-taking, and especially if you prefer to study from hard-form documents.
Interactive Practice Simulator
Question Simulation: Our online AAISM exam practice simulator is designed to help you interactively review and prepare for the exam with tailored features such as show/hide answers, see correct answers etc.
Flashcard-like Practice: Save your toughest questions and revisit them until you’ve mastered each domain.
Progress Tracking: The progress tracking feature of our quiz simulator lets you resume your study journey right from where you left.
3 Months of Unlimited Access
Enjoy full, unrestricted access for three months, long enough to practice, revise, and retake simulations until you are satisfied with your results.
Regular Updates
Artificial Intelligence system management is an ever-evolving field, so being current is the cornerstone of AAISM exam prep. Being mindful of that, CertEmpire’s certified exam coaches keep the content of the practice questions up to date with the latest exam requirements so that you always have the latest exam questions and resources available to you.
Free Practice Tests
To make the decision easy for you, we offer free practice tests for the AAISM exam. Look at the right side-bar and you will find the free practice test button that will take you to a sample free AAISM practice test. Go through the free AAISM exam questions section and discover the richness of our practice questions.
Free Exam Guides
Cert Empire offers free exam preparation guides for AAISM. You can find a trove of AAISM related exam prep resources at our website in our blog section. From tailored study plans for success in AAISM to exam day guidelines, we have covered it all. Cherry on the top, you do not have to be our customer to access this material, and it is free for all.
Important Note
Our AAISM Exam Questions are updated regularly to match the latest ISACA exam version.
The Cert Empire content team, led by certified AAISM professionals, has taken the newest release and added updated concepts, frameworks, and AI management principles, governance models, and risk control frameworks to ensure relevance.
✔ Each question includes detailed reasoning for both correct and incorrect options, helping you understand the full context behind every answer. ✔ Every solution links to official ISACA references, allowing you to expand your knowledge through verified documentation. ✔ Mobile-Compatible – Both the PDF and simulator versions are easy to use across smartphones, tablets, laptops, and even in printed form.
The AAISM remains one of the most respected and highest-paying certifications in AI systems management, proving mastery of AI operations, governance, and performance assurance.
Is this Exam Dump for ISACA AAISM?
No, Cert Empire offers exam questions for practice purposes only. We do not endorse using ISACA Exam Dumps. Our product includes expert crafted and verified practice exam questions and quizzes that emulates the real exam. This is why you may find many of the similar questions in your exam, which can help you succeed easily. Nonetheless, unlike exam dumps websites, we do not give any sort of guarantees on how many questions will appear in your exam. Our mission is to help students prepare better for exams, not endorse cheating.
FAQS
Frequently Asked Questions (FAQs)
What is the ISACA AAISM exam?
The ISACA AAISM exam validates your ability to manage, monitor, and assure artificial intelligence systems within enterprise environments. It measures your expertise in AI lifecycle management, governance alignment, and risk mitigation in intelligent system operations.
Who should take the ISACA AAISM exam?
This exam is ideal for AI managers, IT auditors, governance professionals, and assurance specialists responsible for managing AI systems or assessing their effectiveness. It’s designed for professionals aiming to strengthen their expertise in AI governance and assurance.
How difficult is the ISACA AAISM exam?
The AAISM exam is moderately challenging, testing your ability to apply governance and assurance principles to real-world AI system management scenarios. Consistent preparation with Cert Empire’s updated exam questions helps you develop both theoretical understanding and practical confidence.
What’s a good follow-up certification to pursue after ISACA AAISM?
You might consider ISACA CGEIT as a follow-up, since it expands on the foundational concepts introduced inISACA AAISM. Explore more about CGEIT to continue building your ITSM capabilities.
What topics are covered in the ISACA AAISM exam?
The AAISM exam covers AI lifecycle governance, risk management, data integrity, system monitoring, and ethical AI operations. Each domain follows ISACA’s official blueprint, ensuring full coverage of all key topics tested in the certification exam.
How do Cert Empire’s ISACA AAISM questions help in preparation?
Cert Empire’s AAISM practice questions are crafted to replicate the official ISACA exam format. Each question includes a clear explanation to help you understand the logic, application, and reasoning behind every answer, improving your readiness for real exam conditions.
Are these ISACA AAISM questions real exam dumps?
No. Cert Empire provides verified and legitimate practice resources, not unauthorized exam dumps. The AAISM Exam Questions simulate the real testing environment ethically, helping you learn and build applicable knowledge.
How often is the ISACA AAISM content updated?
The AAISM content is regularly reviewed and updated by certified ISACA experts to reflect the most recent governance updates and AI management standards. This ensures all materials remain aligned with ISACA’s latest exam objectives.
Can I access the ISACA AAISM PDF on mobile devices?
Yes. Cert Empire’s PDFs and simulators are fully optimized for desktops, tablets, and smartphones. You can study flexibly from anywhere, even offline, without any limitations.
How long will I have access to the ISACA AAISM study material?
You’ll receive three months of unlimited access to your study materials. This duration provides enough time to practice thoroughly, identify weak areas, and build confidence before attempting the official exam.
Does Cert Empire offer a free ISACA AAISM practice test?
Yes. A free AAISM practice test is available on the right sidebar of the product page. It contains sample questions similar to those in the real exam, allowing you to experience Cert Empire’s quality and structure before purchasing.
2 reviews for Isaca AAISM Exam Questions 2025
Rated 5 out of 5
Jimmy Kim (verified owner)–
I felt ready for the AAISM exam after reviewing the practice questions and study resources. The content was well-organized, and I was able to pass the exam without much difficulty.
Rated 5 out of 5
Tara Fernandez (verified owner)–
AAISM contained brief quizzes at the end of each chapter. They were useful for testing understanding and reinforcing what I had just studied.
Jimmy Kim (verified owner) –
I felt ready for the AAISM exam after reviewing the practice questions and study resources. The content was well-organized, and I was able to pass the exam without much difficulty.
Tara Fernandez (verified owner) –
AAISM contained brief quizzes at the end of each chapter. They were useful for testing understanding and reinforcing what I had just studied.