AI Risk
AI risk is the potential for harm arising from the design, development, or use of AI systems. It can include bias, discrimination, security vulnerabilities, limited transparency, and other unintended consequences. AI risk considers both the likelihood of an issue and the severity of its impact, helping organizations understand and manage how AI systems affect individuals, businesses, and society.
To understand how organizations manage AI risk in practice, explore the playbook below.
What AI Risk Includes
AI risk goes beyond technical failures. It spans multiple dimensions that shape how AI systems perform in real-world settings.
Common areas include:
- Bias and discrimination: Unfair outcomes caused by biased data or design
- Lack of transparency: Decisions that are difficult to explain or understand
- Privacy and data risks: Misuse or exposure of sensitive data
- Security vulnerabilities: Manipulation, adversarial attacks, or misuse
- Performance and reliability issues: Unpredictable behavior with new or changing data
- Legal and compliance risks: Failure to meet regulatory or policy requirements
- Societal and ethical impacts: Effects on employment, access to services, or public trust
These categories also connect closely to AI security risk management and broader artificial intelligence security concerns.
Why AI Risk Matters
AI systems increasingly influence decisions in areas such as hiring, credit, healthcare, and public services. When risks are not properly managed, these systems can cause harm at scale.
AI risk matters because it helps organizations:
- Identify potential issues before deployment
- Reduce legal, financial, and reputational exposure
- Build systems that are fair, reliable, and trustworthy
- Align AI use with ethical standards and regulatory expectations
A strong AI governance framework helps organizations manage these risks more consistently across teams, systems, and use cases.
Regulations and Standards Shaping AI Risk
AI risk is a central concept in emerging global AI regulations and standards.
Key guidance includes:
- NIST AI Risk Management Framework (AI RMF): Defines risk as a combination of likelihood and impact, and provides guidance for managing AI risks
- ISO/IEC 23894: Offers guidance on AI-specific risk management practices
- ISO/IEC 22989: Provides foundational AI concepts and terminology
- EU AI Act: Applies a risk-based approach with stricter requirements for high-risk systems
These sources reflect growing expectations for organizations to actively manage AI risk, with many tracking NIST AI risk management updates to stay aligned with evolving guidance.
How AI Risk Is Addressed in Practice
AI risk management is an ongoing process embedded within governance and operations. Organizations assess risks during development, monitor systems after deployment, apply controls, document decisions, and review risks whenever systems or use cases change. This helps ensure risks remain managed as AI systems evolve.
Real-World Examples of AI Risk
- Financial Services: Mastercard uses Credo AI to govern AI adoption and support responsible generative AI deployment.
- Hiring and Talent Matching: AdeptID uses Credo AI to document fairness, explainability, and bias mitigation while preparing for EU AI Act requirements.
- Defense Operations: A leading defense contractor standardized AI risk management across more than 100 AI systems to support ongoing compliance and oversight.
These examples demonstrate the value of managing AI risks throughout the AI lifecycle.
Best Practices for Managing AI Risk
- Identify risks early during design and development.
- Involve legal, technical, and business stakeholders.
- Follow established risk management frameworks.
- Continuously monitor system performance.
- Document decisions and mitigation measures.
These practices help organizations deploy AI more responsibly and consistently.
Tools and Frameworks Supporting AI Risk
- NIST AI Risk Management Framework (AI RMF): Structures risk identification, assessment, and mitigation.
- ISO/IEC 23894: Provides guidance for AI risk management processes.
- ISO/IEC 42001: Supports AI governance and management systems.
- Internal Governance Tools: Help track risks, enforce policies, and monitor AI systems.
Together, these tools enable organizations to operationalize AI risk management across teams and workflows.
Summary
AI Risk refers to the potential for harm or unintended consequences arising from AI systems. By understanding where risks can occur and how they impact individuals and organizations, businesses can take proactive steps to reduce harm, meet regulatory expectations, and build trustworthy AI systems that deliver value responsibly.
Frequently Asked Questions
Here you can find the most common questions.
What is the difference between AI risk and AI risk management?
AI risk refers to potential negative outcomes, while AI risk management is the process of identifying, assessing, and mitigating those risks.
Who is responsible for managing AI risk?
AI risk is typically managed by cross-functional teams, including data scientists, risk professionals, legal teams, and business leaders.
Is AI risk always negative?
Not necessarily. While the focus is often on potential harm, understanding risk also helps organizations balance negative outcomes with potential benefits.