Top 5 AI Risk Management Frameworks You Should Know Before the AIGP Exam
Artificial Intelligence is transforming businesses across the globe, but with great power comes great responsibility (and risk). In fact, a whopping 95% of U.S. companies reported using AI in production, yet many are scrambling to manage the new risks, from biased outputs to advanced cyberattacks. A recent survey found that 59% of teams are very concerned about AI-related business risks, but only 18% have aligned their risk and compliance efforts to address those risks. This gap is alarming, and it is exactly why AI risk management frameworks have become the need of the hour. These frameworks serve as a much-needed roadmap for developing AI safely and responsibly, ensuring we do not trade innovation for chaos.
If you are a cybersecurity professional or preparing for the IAPP’s Artificial Intelligence Governance Professional (AIGP) certification, understanding these frameworks is not just helpful; it is essential. (The 2025 AIGP exam places a strong emphasis on governance frameworks and global AI regulations, reflecting how crucial they are to trustworthy AI.) In this article, we will break down the top 5 AI risk management frameworks you should know before walking into that exam hall.
Top 5 AI Risk Management Frameworks You Should Know Before the AIGP Exam
1. NIST AI Risk Management Framework (AI RMF)
The NIST AI RMF Core outlines four functions: Govern, Map, Measure, and Monitor/Manage, to address AI risks systematically.
When it comes to AI risk frameworks, the U.S. NIST AI Risk Management Framework (AI RMF) is a great starting point. Released by the National Institute of Standards and Technology in early 2023, this framework is voluntary and industry-agnostic, meaning any organization, big or small, in any sector, can use it as a blueprint. NIST’s approach is risk-based and iterative, emphasizing continuous improvement and alignment with trustworthy AI principles like transparency, accountability, and fairness.
Why does this matter to you?
For one, the NIST AI RMF has quickly become a de facto standard for many looking to build “trustworthy AI”. It does not hand you a rigid checklist of controls; instead, it helps you develop fundamental capabilities to handle AI risks in a consistent way.
2. EU AI Act
The EU AI Act uses a tiered pyramid of risk levels, minimal, limited, high, and unacceptable, to regulate AI systems based on potential harm.
On the other side of the pond, the European Union’s AI Act is making waves, and for good reason. The EU AI Act (expected to take effect soon, adopted in late 2023) is a legally binding framework that will apply across all 27 EU member states. Unlike voluntary guidelines, this is hard law with teeth, including hefty fines for non-compliance.
The Act takes a risk-tiered approach to AI regulation. It categorizes AI systems into four risk levels: minimal, limited, high, and unacceptable risk. Most everyday applications (like AI in spam filters or trivial chatbots) are minimal risk and largely unregulated. But as the potential for harm increases, so do the rules. For example, “unacceptable risk” systems, such as mass surveillance of citizens or AI social scoring, are flat-out banned under this law.
Next, “high-risk” AI (such as AI for hiring decisions, medical diagnostics, or driving a car) is allowed but heavily regulated. High-risk AI providers must meet strict requirements for risk assessment, data governance, transparency, and human oversight, and they will undergo audits or conformity assessments before their systems hit the market.
3. S. Executive Order on AI
The United States has taken a slightly different route for now; not through an AI-specific law (yet), but via a high-level policy directive. In 2025, the White House issued a sweeping Executive Order on AI (EO 14179, titled “Removing Barriers to American Leadership in Artificial Intelligence”). While an executive order is not legislation, it is binding for U.S. federal agencies and carries significant influence in setting the tone for AI governance. So what does it do? In brief, it directs federal agencies to enhance their AI risk management practices. Agencies have been told to draft AI, use Action Plans, update any outdated AI policies, and ensure that when they deploy AI, they do so responsibly and without bias.
From a risk management perspective, the Executive Order requires a lot of the same good practices you have found in frameworks: agencies must conduct thorough testing, validation, and ongoing monitoring of their AI systems, especially those deemed high-risk.
4. ISO/IEC AI Risk Management Standards (ISO 23894 and 42001)
The world of AI risk is not just about governments and national institutes; international standards bodies are also involved. The International Organization for Standardization (ISO) and the IEC have been busy publishing guidelines to help organizations everywhere speak the same language when it comes to AI governance. One of the most notable is ISO/IEC 23894:2023, an international standard focused specifically on managing AI-related risk.
Unlike a law or a country-specific framework, ISO 23894 is voluntary; however, it is gaining traction globally as companies strive to demonstrate that they meet best practices. What is special about it? For starters, it provides a principle-based framework for AI risk management, meaning it lays out fundamental principles (like fairness, accountability, transparency, and privacy) and a structured process to assess and mitigate AI risks.
Another closely related standard is ISO/IEC 42001:2023, which introduces a framework for AI management systems (think of it as the AI equivalent of ISO 27001 for security). While ISO 42001 takes a holistic approach to building an AI governance program, ISO 23894 drills down on risk management processes as part of that program.
5. G7 Code of Conduct for Advanced AI (2023)
Rounding out our top five is a framework that emerged from international diplomacy: the G7 Code of Conduct for Advanced AI. In 2023, the G7 nations (the U.S., Canada, the UK, France, Germany, Italy, Japan, and the EU) agreed on a voluntary code to guide the development of cutting-edge AI, particularly generative AI and foundation models.
Why was this needed? Well, 2023 was the year that generative AI (such as large language models like ChatGPT) became a household name, and with it came fresh concerns, from deepfakes to AI-driven disinformation, that traditional frameworks had not fully addressed. The G7 Code of Conduct steps in to fill some of those gaps by outlining 11 principles for safe, secure, and trustworthy AI across these advanced technologies.
It emphasizes things like transparency about AI capabilities and limitations, assessment and mitigation of risks, protection against malicious use, and promotion of cybersecurity in AI development. Importantly, this code is action-oriented; it is not just high-level ethics. It gives concrete recommendations for AI Developers and Deployers, like implementing robust testing before releasing AI models and reporting significant AI incidents to authorities or stakeholders.
AIGP Training with InfosecTrain
As AI continues its lightning-fast evolution, having a solid grasp of these frameworks will not only help you ace the AIGP exam; it will make you the go-to person in your organization for navigating AI’s complexities. Each framework we have discussed plays a unique role: NIST’s AI RMF provides the practical toolkit for risk-based AI governance, the EU AI Act lays down the law for high-stakes AI in society, the U.S. Executive Order drives public sector leadership in responsible AI, ISO/IEC standards offer a universal playbook that organizations can voluntarily adopt, and the G7 Code of Conduct points toward a collaborative global effort on emerging AI challenges. Together, they paint a comprehensive picture of how we can innovate with AI without leaving ethics and security behind.
InfosecTrain’s AIGP Training course is not just about passing an exam; it is a strategic toolkit centered on those very AI risk management frameworks we just explored:
- The NIST AI RMF
- EU AI Act
- S. Executive Orders
- ISO Standards
- The G7 Code of Conduct
By mastering these frameworks, you will not only ace the AIGP exam with confidence but you will also be well-equipped to lead AI governance initiatives within your organization, interpreting frameworks, implementing strategies, and bridging operational risk with trusted AI practices.
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