AAISM Prerequisites and Certification Requirements Explained

 

What Are the AAISM Prerequisites?

Before preparing for AAISM Certification, candidates should first confirm that they meet the eligibility requirements.

The main AAISM prerequisite is holding an active CISM or CISSP certification.

This means AAISM is not intended as a first cybersecurity credential.

Instead, it builds on existing security management knowledge and applies it to artificial intelligence.

Why Does AAISM Require an Existing Certification?

AI security management involves more than understanding AI terminology.

Professionals may need to make decisions involving:

  • Governance

  • Enterprise risk

  • Privacy

  • Security architecture

  • Incident response

  • Data management

  • Vendor risk

  • Regulatory obligations

The AAISM requirements ensure that candidates already have a foundation in cybersecurity before specializing in AI security.

AAISM Exam Structure

The current AAISM exam contains:

  • 90 multiple-choice questions

  • 150 minutes of testing time

  • 3 exam domains

The domains are weighted as follows:

AAISM Domain

Weight

AI Governance and Program Management

31%

AI Risk Management

31%

AI Technologies and Controls

38%

Candidates should use these percentages when planning their study schedule.

AAISM Exam Fee

The current AAISM certification exam cost is:

  • Member: $459

  • Non-member: $599

After passing the exam, candidates also need to complete the certification application process, which includes a $50 processing fee.

Preparing After Meeting the Requirements

Once eligibility is confirmed, candidates can choose among several preparation methods:

  1. Review the exam domains.

  2. Identify AI-specific knowledge gaps.

  3. Select an AAISM course.

  4. Use an ISACA AAISM study guide.

  5. Complete practice questions.

  6. Review difficult AI security scenarios.

  7. Schedule the exam when consistently prepared.

An active CISM or CISSP may satisfy the prerequisite, but passing AAISM still requires focused preparation around AI governance, risk, technologies, and controls.


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