In July 2017, China became the first country to publish an AI framework, known as the New Generation Artificial Intelligence Development Plan. Since then, countries across the world have proposed acts, strategies, and executive orders around AI, but there are still no universally accepted standards that function as AI risk management frameworks for all enterprises.

While governments broadly agree on the need for fairness and transparency, they differ significantly in how they enforce these principles. Some AI risk management frameworks, such as those implemented in the EU impose large fines for non-compliance. Others offer guidance, but are not prescriptive.

For enterprise leaders adopting technologies such as private AI, this creates growing pressure and uncertainty around whether AI deployments could violate emerging regulations, exposing them to legal, reputational, or operational risks.

In this article, we’ll help you make sense of the global AI regulatory environment, including key emerging laws and the practical steps to stay compliant while continuing to innovate with AI.

How do AI risk management frameworks help organizations?

AI risk management frameworks provide structured guidance for identifying, assessing, and mitigating risks associated with artificial intelligence (AI).

Although some of the guidance may be complex, adopting these frameworks can streamline AI system development and support outcomes that are ethical, transparent, and aligned with defined objectives.

In addition to supporting national security and public interests, AI risk management frameworks help organizations proactively detect and address potential risks, reducing exposure to operational, legal, and reputational vulnerabilities. This promotes internal alignment and supports consistent, effective AI deployment.

These frameworks enable enterprises to prioritize risk areas and make evidence-based decisions about AI development and deployment, facilitating safer and more deliberate AI integration.

5 Key AI risk management frameworks in 2025

Below are five influential frameworks guiding responsible AI development and deployment in enterprise and public-sector contexts.

FrameworkTypeApproachApplication
NIST AI RMFVoluntaryAugments existing practices; aligned with laws, regulations, normsStructured guidance for safe, responsible AI; widely adopted across sectors
EU AI ActLegally binding (EU-wide)Classifies AI by risk; bans certain high-risk usesRegulatory obligations for high-risk AI; penalties for non-compliance
U.S. Executive Order on AIBinding for U.S. federal agenciesStrategic mandates for AI oversight and useEmphasizes civil rights, safety, and national security in government AI
ISO/IEC StandardsVoluntaryPrinciple-based framework for risk managementShared terminology and guidance across organizational functions
G7 Code of Conduct for Advanced AI (2023)Voluntary for G7 countriesActionable governance for generative AI and foundation modelsSupplements G7 and OECD principles with implementation-oriented best practices

1. The NIST AI RMF

The NIST AI Risk Management Framework (AI RMF) is a voluntary resource developed by the U.S. National Institute of Standards and Technology. It promotes a risk-based, iterative approach to AI development and deployment, emphasizing transparency, accountability, and fairness.

The framework includes the AI RMF Core, which outlines functions to govern, map, measure, and manage AI-related risks.

Developed over 18 months with input from more than 240 organizations, the AI RMF offers practical guidance for evaluation, verification, and validation activities.

2. The EU AI ACT

The EU AI Act is a legally binding regulation that applies across all EU member states. It classifies AI systems into categories based on risk – from minimal-risk systems, such as retail chatbots, to those that affect user safety or fundamental rights.

The Act bans certain “unacceptable risk” systems, such as those used for biometric surveillance or social scoring. High-risk systems must comply with strict requirements, including conformity assessments, transparency obligations, data governance, and logging protocols.

Examples of high-risk applications include a healthcare chatbot that provides medical advice or a financial chatbot used to assess creditworthiness.

3. The U.S Executive Order on AI

While not legislation, the U.S. Executive Order on AI establishes federal priorities for AI governance. The current directive – Executive Order 14179, Removing Barriers to American Leadership in Artificial Intelligence – was issued in 2025 and replaces a 2023 order.

It directs federal agencies to submit AI Action Plans, update outdated policies, and accelerate responsible AI use in public services. It emphasizes national security, civil rights protections, and risk mitigation in sensitive areas such as biotechnology, cybersecurity, and critical infrastructure.

Compliance requirements include testing, performance evaluation, and continuous monitoring throughout the AI system lifecycle.

4. ISO/IEC standards

ISO/IEC 23894:2023 is an international standard focused on AI risk management. It is broadly adopted across industries due to its clear definitions of key AI terms and roles, facilitating cross-functional coordination among legal, compliance, IT, and development teams.

The standard formalizes principles such as fairness, accountability, and privacy, helping organizations align AI practices with both internal policies and external regulatory obligations.

5. The G7 Code of Conduct for Advanced AI (2023) 

The G7 Code of Conduct for Advanced AI establishes a unified governance approach for advanced AI technologies across G7 nations. It outlines methods to assess, mitigate, and report risks in the deployment of generative AI and foundation models.

The Code encourages transparency regarding system capabilities, limitations, and use cases. It contains 11 principles promoting safe, trustworthy AI and includes developer-oriented recommendations with actionable practices.

What are the risks associated with AI systems?

AI systems offer transformative potential, but they also introduce a range of risks that organizations must carefully manage to ensure secure, ethical, and sustainable deployment. 

While frameworks provide a roadmap, enterprise leaders must stay alert to specific threats that can undermine safe deployment.

Data and model risks

Unauthorized access, accidental exposure, or data loss can compromise confidentiality and trust. Risks to the AI model itself include adversarial attacks, where input data is subtly altered to deceive the model, and prompt injection attacks, where crafted inputs cause the model to reveal sensitive information or generate misleading content.

Operational risks

AI system performance can degrade over time due to model drift, which occurs when changes in real-world data reduce model accuracy. Other challenges include limited maintenance, legacy integrations, or inconsistent updates, which can undermine long-term reliability.

AI systems can perpetuate harmful biases embedded in training data, resulting in biased, incomplete datasets that distort AI outputs and produce inaccurate predictions or unfair decisions, increasing exposure to legal challenges. Additionally, hallucinations can also mislead users or spread misinformation. 

How to embed AI risk management frameworks into your enterprise 

The following tools can help operationalize AI risk frameworks across technical and non-technical teams.

  • Model cards: Concise documents that outline an AI model’s purpose, training data, performance metrics, and known limitations. These are particularly valuable during audits and reviews, as they facilitate the early identification of issues.
  • Data sheets: Document the datasets used to train AI models. They include key details about data collection, structure, potential biases, and ethical concerns.
  • Risk cards: Summarize specific risks associated with an AI system, typically including a risk description, likelihood, potential impact, mitigation strategies, supporting metrics, and ethical considerations.
  • Scenario planning: The process of modeling different risk and performance scenarios to identify potential vulnerabilities, detect subtle data patterns, and forecast outcomes. This helps manage AI-related risks before they escalate.
How to embed AI risk management frameworks into your enterprise 

Tips for working with risk management frameworks 

To make AI risk frameworks effective, it’s essential to adopt a proactive, agile approach to anticipating risks before they escalate and considering all categories of risk as discussed above.

The most effective way to stay ahead is to foster collaboration across legal, technical, and business teams. Reminding stakeholders of the risks associated with non-compliance can help ensure that AI risk management is seen as a shared responsibility.

Next, categorize and assess risks systematically using metrics relevant to your operational context, prioritizing those with the greatest potential impact on users, your reputation, and applicable legal frameworks.

Finally, implement tailored mitigation strategies with confidence. Your governance approach should be flexible enough to respond to evolving legislation and ready to adapt as requirements change. When done right, embedding AI risk frameworks enables secure, scalable, and successful AI investments over the long term.