AI Risk Management: What It Is and How It Supports Safer AI

Key takeaways

• Artificial intelligence (AI) risk management helps organizations identify, evaluate, and reduce potential risks associated with AI systems so they can use AI safely and responsibly.

• Organizations often use risk management frameworks such as the NIST AI Risk Management Framework (AI RMF), AI trust, risk, and security management (AI TRiSM), and the EU AI Act to assess and manage AI risks.

• The four primary types of AI risk are data privacy and security risks, operational risks, model risks, and ethical and compliance risks, each requiring different governance and management strategies.

Developing skills in AI governance frameworks, cybersecurity, regulatory compliance, analytical thinking, and communication can help you prepare for a career in AI risk management. Discover the role AI risk management plays in protecting organizations. If you’re interested in learning more about cybersecurity and AI governance, consider enrolling in the Google Cybersecurity Professional Certificate. In just six months, you can learn how to identify cybersecurity risks, protect systems and data, mitigate common threats, and build practical skills with Python, Linux, SQL, and SIEM tools.

What is AI risk management?

AI risk management is the process of identifying, reducing, and responding to the risks and vulnerabilities associated with artificial intelligence. With more and more organizations using artificial intelligence, managing the potential risks that come along with it is becoming increasingly important. These practices help organizations protect data, meet regulatory requirements, and promote responsible and ethical use of AI. AI risk management helps organizations balance AI’s benefits with the need to reduce risk [1].

Why dies AI risk management matter?

The use of AI has grown rapidly in recent years. According to McKinsey, 88 percent of organizations use AI in at least one business function as of 2025, up from 78 percent in 2024 [2]. With such fast-paced growth, organizations face new security challenges that they may not have planned for. One example is shadow AI, which occurs when employees use AI tools their organization's IT team has not approved. Because these tools operate outside established security controls, they can create security and visibility challenges.

Effective AI risk management can provide several business benefits, including:

• Reducing security incidents, operational disruptions, and potentially costly issues

• Building trust among leadership, legal, and compliance teams, and increasing organizational compliance

• Giving development teams clear policies and processes

• Helping organizations prepare for audits and comply with evolving AI regulations

• Protecting data, intellectual property, and organizational reputation

Read more: Risk Management: Key Concepts, Certifications, and Exam Preparation Tips

Agentic AI: New risks, new challenges

Agentic AI is a type of artificial intelligence that can independently pursue goals and complete tasks with minimal human oversight. While many AI systems can perform specific tasks, only AI systems with certain characteristics qualify as agentic. These systems are goal-oriented, context-aware, action-driven, capable of multistep reasoning, and able to improve over time [3].

Because agentic AI systems can act more independently, they can pose new risks, including pursuing objectives that differ from human intent, accessing resources without authorization, or behaving in unexpected ways, such as self-replication or resisting shutdown. They can also perform in anthropomorphic (humanlike) or socially persuasive ways.

Agentic AI may even perform irreversible actions before a human can intervene, allowing errors to spread across multiple steps. These changes in behavior may go unnoticed until they become significant. Overall, the capabilities of agentic AI support a wide range of new applications, but they also introduce cybersecurity and risk management challenges that organizations must address.

Key skills for AI risk management

As AI technologies continue to evolve, the skills needed to manage AI-related risks are evolving as well. Professionals working in AI risk management should have a strong foundation in cybersecurity and a basic understanding of how to develop, deploy, and maintain machine learning (ML) models. Protecting AI systems also requires understanding how AI models, data pipelines, and deployment methods can introduce new vulnerabilities. Some additional skills for professionals in AI risk management include knowledge of AI governance frameworks, understanding of regulations and compliance, analytical thinking, ethical decision-making, and a strong ability to communicate and collaborate with teams.

What are the 4 types of AI risk?

AI risks generally fall into four main categories: data privacy and security risks, operational risks, model risks, and ethical and compliance risks. Understanding these categories can help organizations identify potential vulnerabilities and apply appropriate governance and risk management strategies. Take a closer look at each type:

• Data privacy and security risks: Since AI systems depend on data, protecting the data from bias, tampering, cyberattacks, and breaches is an important part of risk management. Data-related risks generally fall into three categories: data security, data privacy, and data integrity. Together, these risks can affect the confidentiality, accuracy, and reliability of AI systems.

• Operational risks: Although AI systems may appear highly intelligent, they are built on software and machine-learning algorithms. Like any technology, they are vulnerable to operational risks. Operational risks involve how the AI performs from day to day. Common examples include model drift, sustainability issues, integration challenges, and gaps in accountability.

• Model risks: Attackers may target AI models to steal or alter them without authorization. These attacks can change how a model operates, reducing its accuracy, reliability, or overall performance.

• Ethical and compliance risks: AI can significantly impact employees. As organizations automate tasks, these changes can affect the workforce, and employees may need support as they adapt. Another common challenge with AI is bias. If you train an AI system on biased or unrepresentative data, it can produce unfair or inaccurate outcomes in areas such as hiring, lending, and health care. For example, if past decisions favored certain groups, an AI system that learns from that data may continue making similar decisions until developers identify and correct the bias.

What are AI risk management frameworks?

Several widely recognized frameworks provide organizations with guidance for identifying, assessing, and managing AI risks. Differences in industry, region, and regulatory environment have led to the development of multiple frameworks.

NIST AI Risk Management Framework (AI RMF)

In January 2023, the National Institute of Standards and Technology (NIST) introduced the AI Risk Management Framework (AI RMF), giving organizations a structured way to identify, assess, and manage AI risks [4]. Since its release, the framework has become a leading standard for AI risk management. Many organizations use more than one framework, often relying on the AI RMF as their foundation. Developed with contributions from the public and private sectors, it is a voluntary framework that organizations across industries and regions can adopt.

The AI RMF Core identifies four functions that help organizations manage AI risks:

• Govern: Promotes an organizational culture of AI risk management

• Map: Puts the AI risks into specific contexts to better understand the full picture

• Measure: Assesses and monitors AI risks using quantitative, qualitative, or mixed evaluation methods

• Manage: Addresses measured risks as identified and defined by the governing function

AI TRiSM

AI trust, risk, and security management (AI TRiSM) is a framework that helps organizations keep AI systems trustworthy, secure, and compliant by continuously monitoring, validating, and governing their performance. Gartner introduced the AI TRiSM framework to help organizations manage AI systems and keep them aligned with organizational policies and regulatory requirements. AI TRiSM is built on five core pillars to guide how organizations oversee their AI systems:

• Explainability: Makes AI decisions understandable so people can review, interpret, and trust model outputs

• Model operations (ModelOps): Manages AI models throughout their life cycle by monitoring performance, updates, and changes after deployment

• AI-specific security: Protects AI systems from threats such as prompt injection, data poisoning, and model theft

• Privacy: Protects sensitive data by governing how organizations collect, process, store, and use information throughout AI training and operation

• Regulatory compliance: Helps organizations ensure AI systems meet legal, regulatory, and organizational requirements

Overall, the goal of AI TRiSM is to promote principles of trustworthy AI, which include accountability, explainability, and interpretability.

EU AI Act

The European Union’s (EU) AI Act establishes rules for AI systems that emphasize risk management, transparency, and data governance. The EU AI Act took effect on August 1, 2024, with its requirements scheduled to become fully applicable over the following three years. It uses a risk-based approach, applying stricter requirements to AI systems as their potential risks increase across industries such as health care, education, manufacturing, technology, entertainment, and general-purpose artificial intelligence (GPAI) models. The rule applying to GPAI became effective in August 2025.

Widely regarded as the world’s first comprehensive AI law, the EU AI Act bans certain AI applications while establishing governance, risk management, and transparency requirements for others. Prohibitions relating to specific AI systems became effective in February 2025 [5]. The EU AI Act groups AI systems into four risk categories based on their potential impact on people’s rights and safety [6]:

• Unacceptable risk: Bans AI applications considered to pose unacceptable risks, including social scoring, manipulative AI, and some real-time biometric surveillance

• High risk: Requires AI systems used in areas such as transportation, medical devices, and critical infrastructure to meet strict regulatory requirements before deployment

• Transparency risk: Requires all AI applications, such as chatbots and AI-generated content, such as deepfakes, to meet transparency requirements so users know they are interacting with AI

• Minimal risk: Allows the majority of AI applications, including AI-powered video games and virtual assistants, to operate with minimal regulatory requirements

Who plays a role in managing AI risk?

Managing AI risks effectively requires collaboration among data scientists, engineers, security teams, and stakeholders to support innovation and prioritize responsible risk management. As a result, it calls for shared responsibility and accountability across an organization and requires participation from leaders across multiple functions. General counsel and risk leaders help oversee organizational risk, risk managers and legal teams identify and prioritize AI-related risks, board members provide governance and oversight, and chief information security officers (CISOs) address AI-specific security threats.

How AI risk management connects to responsible AI

Responsible AI relies on effective AI risk management to ensure that AI systems reduce bias, prevent harm, and support ethical outcomes. Principles such as fairness, privacy, accountability, respect for human dignity, and the protection of human rights form its foundation. These principles help shape technical requirements, procurement practices, and AI oversight processes. In practice, responsible AI requires organizations to go beyond meeting legal and regulatory requirements. It also involves establishing governance practices, organizational processes, and technical controls that promote transparency, accountability, and trust.

Why is AI risk management is becoming a valuable workplace skill?

AI-related careers are among the fastest-growing occupations in 2026, with increasing demand for AI engineers, cybersecurity specialists, and more, reflecting the growing need for professionals with AI knowledge and skills [7].

As AI becomes more widely integrated into workplaces across industries, organizations need professionals who can use AI systems safely, ethically, and responsibly. With AI systems becoming more sophisticated and new laws, regulations, and standards emerging, organizations are making AI governance a high priority.

AI risk management is becoming a valuable workplace skill as more organizations establish AI governance programs. According to the 2025 AI Governance Profession Report from the International Association of Privacy Professionals (IAPP), 77 percent of surveyed organizations are working on AI governance, increasing to nearly 90 percent among organizations already using AI. Survey respondents also pointed to a skills gap in AI governance. Specifically, 23.5 percent of respondents said that finding qualified AI professionals made it more challenging to deploy AI successfully [8].

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