ISACA • AAISM

ISACA Advanced in AI Security Management (AAISM) Practice Test

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.

Exam Details

Questions

600

Duration

150 minutes

Passing Score

450/800

Difficulty

Associate

Last Updated

Feb 2026

Exam Domain Breakdown

AI Governance and Program Management31%

AI Risk Management31%

AI Technologies and Controls38%

Exam Overview

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.

Official exam page

Who Should Take This Exam

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.

Prerequisites

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.

Exam Format

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.

Skills Measured

1.Domain 1 — AI Governance and Program Management (31%): Covers the ability to advise stakeholders on implementing AI security solutions through policy design, data governance frameworks, AI security program management, and incident response planning. Includes establishing AI security policies and providing oversight of AI system inputs and outputs for quality and safety compliance.
2.Domain 2 — AI Risk Management (31%): Validates skills in assessing and managing risks, threats, and vulnerabilities associated with enterprise-wide AI adoption, including supply chain security issues and risks within the AI solution development life cycle. Candidates must demonstrate proficiency in advising on security risk, conducting AI-specific risk assessments, and managing vendor and third-party AI risk.
3.Domain 3 — AI Technologies and Controls (38%): Tests knowledge of security technologies, techniques, and controls tailored specifically to AI systems. Subtopics include AI security architecture and design, data management controls, privacy controls, ethical and trust and safety controls, and security monitoring for AI models. This domain carries the highest weight and reflects the technical depth required to operationalize AI security.

Study Tips

Download and thoroughly review the official AAISM Exam Content Outline from ISACA's website before beginning any study — it maps all three domains, their percentage weights, and the 22 core competencies tested, giving you a precise study blueprint.
Use the ISACA AAISM Questions, Answers & Explanations (QAE) Database (12-month subscription) as your primary practice tool. With 200+ questions, it allows you to build custom quizzes by domain and track progress over time, directly mirroring the exam's structure.
Take the free 12-question AAISM Practice Quiz on ISACA's website early in your preparation to calibrate your baseline knowledge across the three domains before committing to a full study schedule.
Leverage the official AAISM Online Review Course and Review Manual (available in digital and print) — these are authored to align directly with the exam content outline and are the most reliable sources for understanding how ISACA frames AI governance and risk concepts.
Map your existing CISM or CISSP knowledge to the AAISM domains deliberately: Domain 1 and Domain 2 extend familiar governance and risk frameworks into AI-specific contexts, so focus your energy on the new AI threat landscape, supply chain risks, and model lifecycle considerations rather than re-studying foundational security management theory.
Join ISACA member study groups (available through the Engage platform) to discuss Domain 3's technical controls content — AI security architecture and data management controls are areas where peer discussion of real-world AI implementations can reinforce conceptual exam knowledge.
Pay extra attention to Domain 3 (AI Technologies and Controls at 38%) since it is the heaviest-weighted domain. Focus on security architecture design for AI systems, model lifecycle management, and privacy and ethical controls, which represent the most AI-specific and potentially least familiar content for CISM/CISSP holders.

Career Benefits

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.

Sample Questions

Preview — answers shown

5 sample questions with correct answers and explanations. Start a practice session to test yourself across all 600 questions.

1. An organization implements OWASP LLM Top 10 controls for a customer-facing chatbot. The security team identifies that the LLM has permissions to execute database queries, send emails, and modify user accounts without approval workflows. Which vulnerability requires immediate remediation? (Select one!)

ALLM10 Unbounded Consumption causing resource exhaustion

BLLM06 Excessive Agency granting over-permissioned autonomous actions

CLLM01 Prompt Injection allowing manipulation of system behavior

DLLM02 Sensitive Information Disclosure exposing training data

Explanation

LLM06 Excessive Agency occurs when LLMs are granted over-permissioned capabilities to perform autonomous high-risk operations without appropriate guardrails or human oversight. The scenario describes direct access to critical functions like database modification and email sending without approval workflows, which is the exact definition of excessive agency. Mitigations include implementing least privilege principles, human-in-the-loop requirements for high-risk actions, and granular access controls. Prompt injection manipulates behavior but does not specifically address permission levels. Sensitive information disclosure involves data leakage, not permission boundaries. Unbounded consumption addresses resource limits, not functional permissions.

2. A retail organization assesses its AI governance maturity using the RAI Institute Maturity Model. The assessment reveals that AI governance practices are well-characterized and standardized, with documented processes applied consistently within specific business units but not yet organization-wide. At which maturity stage is the organization currently operating? (Select one!)

ASystemic stage with consistent cross-organizational application

BOperational stage with standardized processes

CEmerging stage beginning to formalize governance

DInitial stage with ad hoc, reactive practices

Explanation

The operational stage is characterized by well-characterized, standardized processes that are documented and followed within business units. The scenario describes exactly this maturity level. Initial stage would show ad hoc, reactive approaches without standardization. Emerging stage shows beginning formalization but lacks the well-characterized, standardized processes described. Systemic stage requires consistent application across the entire organization, which the scenario explicitly states has not yet been achieved.

3. An AI system deployed in a regulated industry experiences a security incident where adversarial perturbations caused the model to misclassify critical safety inputs. The organization follows algorithm locking requirements that restrict model updates. Which action should the incident response team take FIRST? (Select one!)

AUpdate input validation rules to filter detected adversarial patterns

BImmediately retrain the model to improve adversarial robustness

CIsolate the compromised AI system and implement manual review processes as compensating control

DDeploy adversarial training to harden the existing model against perturbations

Explanation

In regulated industries with algorithm locking restrictions, immediate model retraining or updates may violate regulatory requirements. The first priority is containment through system isolation and implementing manual review as a compensating control to prevent further safety incidents while maintaining regulatory compliance. This follows the CoSAI incident response framework's containment phase. Retraining, adversarial training, and model updates all require regulatory approval processes that prevent immediate deployment. Input validation updates may be possible but are insufficient as the primary containment measure when safety-critical misclassifications have occurred.

4. An organization discovers employees are using ChatGPT and other free-tier generative AI tools with personal accounts, inputting customer data and proprietary code for assistance. Security logs show these activities have persisted for an average of 400 days before detection. What is the PRIMARY risk category this shadow AI usage represents? (Select one!)

ARegulatory compliance gaps in AI vendor contracts

BModel accuracy degradation from untested AI outputs

CData leakage and intellectual property exposure to external services

DIncreased computational costs from unauthorized cloud usage

Explanation

Data leakage and intellectual property exposure is the primary risk when employees input sensitive company data into unsanctioned external AI services, as this data may be used for model training, stored indefinitely, or accessible to service providers. Research shows organizations using shadow AI experience 670,000 dollars higher breach costs on average. Model accuracy risks are secondary to the immediate data exposure. Computational costs are minimal with free-tier services. Vendor contract issues exist but the primary risk is the data already exposed.

5. A manufacturing company implements AI-driven predictive maintenance for critical industrial equipment. The AI security team must establish metrics to monitor solution effectiveness and security posture. According to ISACA best practices, which metric is MOST important for demonstrating that the AI system maintains acceptable security risk levels? (Select one!)

AModel accuracy measured as precision and recall on test datasets

BMean time to detect and respond to model drift requiring retraining

CPercentage of predictions that are explainable through SHAP or LIME techniques

DNumber of detected adversarial inputs blocked by input validation controls per month

Explanation

The number of adversarial inputs detected and blocked directly measures the effectiveness of security controls in treating risk to acceptable levels, which aligns with ISACA's focus on security risk management and control effectiveness. This metric demonstrates active threat mitigation and security posture. Explainability percentage supports transparency and trust but does not directly measure security risk levels. Model accuracy is a performance metric rather than a security metric. Mean time to detect drift is important for model maintenance but measures operational resilience rather than security control effectiveness against threats.