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Specialised Certification

AI Risk Management Professional (ARMP)

AI systems introduce risks that do not exist in traditional IT or operations: bias, drift, hallucination, adversarial manipulation, and emergent behaviour in agentic systems. These risks require their own identification methods, assessment criteria, and treatment approaches. A specialised certification for professionals who need to manage AI risk with methodology that accounts for how AI systems actually fail.

Exam Duration
110 minutes
Validity
3 years
Format
Online Sessions

Target Roles

  • Risk Managers
  • Compliance Teams
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AI Risk Management Professional (ARMP) certification badge

Why Pursue This Certification?

Without AI-specific risk methodology, risk professionals default to one of two approaches. They apply enterprise risk frameworks that treat AI systems like any other technology asset, which misses failure modes like drift, hallucination, and emergent behaviour entirely. Or they defer to technical teams who evaluate model performance in isolation, without connecting it to organisational risk appetite or regulatory obligations.

In both cases, the risk register provides false assurance: it shows risks as identified and managed when the most consequential ones were never captured. Portfolio-level risk accumulation stays invisible because each AI use case is assessed on its own. Boards receive reporting that confirms process compliance while substantive risk goes unreported because no one in the risk function has the vocabulary to surface it. The ARMP certification exists because RESAIA believes this gap is not a tooling problem or a resourcing problem. It is a knowledge problem.

Learning Outcomes

What You Can Expect

  • Distinguish AI-specific risk categories from conventional IT and operational risks, including bias, drift, hallucination, adversarial manipulation, and emergent behaviour in agentic systems.
  • Conduct risk assessments that account for the full spectrum of AI system types, from traditional ML through generative AI to agentic systems, and connect findings to organisational risk appetite.
  • Identify portfolio-level risk accumulation across multiple AI use cases, not just risks within individual systems.
  • Establish risk appetite statements and tolerance thresholds calibrated to AI use case risk profiles, and translate them into language boards and audit committees can act on.
  • Integrate AI risk management into enterprise risk governance so that AI risks are visible alongside every other category of organisational risk.
  • Design risk treatment plans that map identified risks to specific controls, assign clear accountability, and produce the evidence an assurance function needs to verify them.

Study Material

RESAIA Body of Knowledge

The ARMP draws primarily from the BOK's AI Risk Management chapter, with supporting material from AI Strategy and Oversight, AI Ethics, Continuous Monitoring and Drift Detection, and AI Use Case Portfolio Management. The BOK teaches these disciplines not as independent topics but as parts of a connected governance system, so candidates understand where risk management sits within the broader governance architecture and how it depends on and feeds into other disciplines.

Explore RESAIA BOK

Application Process

  1. 01

    Choose Program

    Review the role profile and confirm ARMP is the right specialization.

  2. 02

    Learning

    Work through risk identification, assessment, treatment, and monitoring guidance.

  3. 03

    Take Exam

    Complete the 110-minute online certification assessment.

  4. 04

    Earn Certification

    Receive a three-year professional credential after passing.

Resources

Your one-stop solution for responsible AI development learnings.

RESAIA exists to make that practice measurable, teachable, and accountable. It gives every discipline that touches AI a shared structure, a shared language, and a shared standard to measure against.

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