AI Risk Management: Frameworks and Strategies for the Evolving Landscape

Learn the essential AI risk management frameworks for responsible AI development. Understand regulations, mitigate risks, and build trustworthy AI systems.

Lakera Team
March 8, 2024
March 8, 2024
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As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.

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Artificial intelligence (AI) has transformative potential, but as its capabilities grow, so does the need for effective AI risk management. This field focuses on identifying and mitigating the unique risks associated with the development and use of AI systems. It's distinct from AI for risk management, which involves using AI tools to enhance risk assessment in various sectors like finance or healthcare.

Understanding AI risk management frameworks is crucial for businesses and policymakers. These frameworks provide guidance on addressing the technical, ethical, and societal challenges posed by AI, ensuring responsible and beneficial innovation. Key players like NIST, ISO/IEC, and the European Union have created comprehensive frameworks to manage these risks.

The rapid evolution of AI technologies makes AI risk management an ongoing process. New vulnerabilities and ethical concerns emerge alongside new capabilities, requiring continuous adaptation and vigilance.

In this article, we'll delve into these essential AI risk management frameworks:

  • NIST AI Risk Management Framework (AI RMF): A flexible and adaptable framework for organizations of all sizes.
  • ISO/IEC 23894:2023: An international standard for AI risk management, promoting consistency and transparency.
  • EU AI Act: A pioneering law balancing innovation with the protection of individual rights.
  • McKinsey’s Framework: Specialized approach focusing on proactive business risk management.


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Introduction to Risk Management Frameworks

What are AI Risk Management Frameworks?

AI risk management frameworks provide structured guidelines for identifying, assessing, and mitigating the diverse risks associated with AI systems. They help organizations take a systematic approach to addressing these challenges, rather than relying on ad-hoc or reactive measures.

The best AI risk management frameworks recognize that there's no one-size-fits-all solution. Different industries and applications face unique sets of risks and require tailored approaches. A framework that works well for a self-driving car company might be less applicable for a healthcare organization using AI for diagnostics.

Several key frameworks offer guidance for responsible AI development and deployment:

  • NIST AI Risk Management Framework (AI RMF): Designed for flexibility and adaptability, the NIST framework provides a comprehensive approach applicable across various industries.
  • ISO/IEC 23894:2023: An international standard for AI risk management, promoting global consistency and transparency.
  • EU AI Act: This landmark legislation classifies AI systems by risk level, establishing rules to protect fundamental rights and safety.
  • McKinsey’s Approach: This consulting firm offers a framework emphasizing proactive risk identification and collaboration between business, legal, and technical teams.

In the following sections, we'll delve deeper into each of these frameworks, exploring their core principles and practical applications.

The Frameworks for AI Risk Management

The field of AI risk management offers a diverse toolkit for organizations seeking to mitigate the potential harms associated with AI systems.

Frameworks like the NIST AI RMF provide adaptable structures, while global standards like ISO/IEC 23894 promote consistency and transparency. New legal frameworks, such as the EU AI Act, demonstrate the increasing regulatory attention focused on responsible AI use.

Understanding these frameworks is essential for developing and deploying AI in a way that benefits both businesses and society.

NIST AI Risk Management Framework (AI RMF)

The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) offers a flexible and comprehensive approach for organizations to address AI-related risks.

Developed through extensive collaboration among industry, academia, and government, the AI RMF emphasizes the importance of building trustworthiness into AI systems throughout their lifecycle.

The AI RMF Playbook

Alongside the framework itself, NIST provides a companion Playbook. This dynamic resource offers practical guidance for implementing the AI RMF's core functions, allowing for community contributions of best practices and case studies.

Image source: NIST

The Four Functions of the AI RMF

The AI RMF revolves around four key functions:

  1. Govern: Establishes oversight structures and risk-aware culture for AI development and use. Example: A healthcare organization forms an AI Governance Committee to address bias, privacy, and unintended consequences.
  2. Map: Contextualizes AI risks within an organization. Example: An online retailer using a recommendation algorithm identifies potential biases and misinterpretation of user intent.
  3. Measure: Establishes meaningful metrics to quantify and track AI risks. Example: A bank implementing AI for loan approvals prioritizes fairness analyses, while a self-driving car company focuses on continuous safety testing.
  4. Manage: Guides decisive action to mitigate risks. This could involve technical adjustments, procedural changes, or even the decision not to deploy an AI system.

Adaptability and Voluntary Application

Crucially, the AI RMF is designed to evolve alongside technological advancements and best practices. Voluntary adoption promotes flexibility, allowing organizations to tailor its implementation based on their sector, risk tolerance, and goals.

ISO/IEC 23894:2023

ISO/IEC 23894:2023 is an international standard specifically designed for AI risk management.

Published in 2023, it provides detailed guidance for organizations across all sectors on identifying, assessing, and mitigating risks associated with the development and use of AI. 

The standard offers specific guidance on navigating the complexities of the AI lifecycle.

Alignment with Existing Standards

ISO/IEC 23894 builds upon the established principles of ISO 31000:2018, a broader international risk management standard. This ensures consistency with proven practices while tailoring its approach to uniquely address the challenges posed by AI technologies.

Managing Risk Throughout the AI Lifecycle

The standard recognizes the specific complexities of managing risks throughout the AI lifecycle.

Annex C of ISO/IEC 23894 provides a valuable tool with its comprehensive mapping of risk management processes across the stages of AI development and deployment. This helps organizations identify where risk mitigation strategies should be applied at each phase.

**It's worth noting that large tech companies like Google play an active role in developing and adapting AI risk management frameworks. Google's Secure AI Framework is a prime example, demonstrating how these principles can be translated into practical, company-specific guidelines for building safe and reliable AI systems.**

Key Strengths of ISO/IEC 23894

ISO/IEC 23894 emphasizes the need for ongoing risk management and continuous adaptation to the evolving landscape of AI technologies.

Its comprehensive approach makes it a valuable resource for organizations seeking to develop and deploy AI responsibly.

The EU AI Act

The EU AI Act is a landmark piece of legislation designed to regulate artificial intelligence within the European Union. With its focus on promoting safe AI, protecting fundamental rights, and providing legal certainty, the Act has the potential to shape the global AI landscape.

Harmonized Rules for a Unified Market

One of the EU AI Act's key objectives is to create harmonized rules across all member states. This aims to establish a single market for trustworthy AI applications, prevent regional differences in regulation, and avoid fragmented standards that could hinder innovation.

Risk-Based Categorization

The EU AI Act introduces a risk-based categorization system for AI applications, including:

  • Unacceptable Risk: AI systems deemed to pose a clear threat to safety or fundamental rights are prohibited.
  • High-Risk: AI systems used in critical sectors (like healthcare, transport, and law enforcement) are subject to stricter regulations, including pre-market conformity assessments and transparency requirements.
  • Limited Risk: AI systems with specific transparency obligations (such as chatbots) fall under this category.
  • Minimal Risk: Most AI systems fall under this category, where voluntary codes of conduct are encouraged.

Implications for Organizations

This risk-based approach is crucial for organizations developing or deploying AI systems. It provides a clear framework for aligning their practices with the Act's requirements, ensuring compliance and fostering a responsible approach to AI.

Global Influence

While the EU AI Act directly applies to companies and organizations within the European Union, its influence is likely to extend beyond EU borders.

Multinational companies and others seeking to access the EU market will likely need to align their AI risk management practices with these standards, potentially establishing a global benchmark for responsible AI development.

**💡 Pro Tip: Discover Lakera’s AI governance cheat sheet: EU vs US AI Regulations in a Nutshell**

McKinsey's Approach to AI Risk Management

McKinsey & Company champions a proactive and systematic approach to AI risk identification and mitigation. 

Their framework emphasizes integrating legal, risk management, and data science teams from the earliest stages of AI development, fostering an environment where AI models align with both business goals and ethical and regulatory requirements.

Systematic Risk Identification & Prioritization

McKinsey's approach centers around a structured plan for identifying and prioritizing AI-related risks.

This involves integrating risk management into every stage of the AI lifecycle, ensuring proactive consideration of potential harms and mitigation strategies.

Interdisciplinary Teams for Success

Crucially, McKinsey highlights the importance of collaboration between business, legal, and technical teams. This "tech trust team" approach ensures a comprehensive understanding of potential risks, from legal and ethical implications to technical vulnerabilities.

Image source: McKinsey

Six Types of AI Risk

McKinsey's framework outlines six major categories of AI risk:

  1. Privacy
  2. Security
  3. Fairness
  4. Transparency and Explainability
  5. Safety and Performance
  6. Third-Party Risks

Each risk type carries specific considerations. For example, a healthcare AI system handling sensitive patient data will have heightened privacy and security concerns compared to an AI-powered chatbot.

Cataloging, Assessing, and Mitigating Risk

McKinsey guides organizations in creating a detailed catalog of AI risks relevant to their specific applications.

This catalog informs impact assessments, allowing for the prioritization of risks based on their potential for harm. Mitigation strategies can then be tailored to address the most significant issues.  Continuous reassessment is essential to account for evolving technologies and new risks that may emerge.

Lakera: AI Security with an LLM Focus

Lakera takes a specialized approach to AI risk management, focusing on the unique security challenges posed by Large Language Model (LLM) based systems. This approach aligns with broader AI security frameworks while offering tailored solutions for a rapidly evolving technological landscape.

Alignment with Established Frameworks

We understand the importance of grounding our approach in recognized AI risk categories.

This is why our solutions address key areas outlined in the OWASP Top 10 for LLM Applications, ensuring a comprehensive approach to security threats. Additionally, by proactively targeting risks highlighted by the ATLAS framework, we commit to staying ahead of emerging adversarial AI techniques.

Lakera's Solutions: Guard and Red

Lakera offers a powerful suite of tools specifically designed to secure AI systems:

Lakera Guard: This specialized tool fortifies AI applications leveraging LLMs. It defends against a wide range of current and future cyber threats, providing organizations with a robust layer of protection.

Lakera Red: This AI red-teaming solution stress-tests LLM-based applications, exposing vulnerabilities before they can be exploited. The adversarial approach proactively identifies potential attack vectors, driving continuous improvement in AI system security.

The Lakera Advantage

By focusing on the specific challenges of LLMs, Lakera provides a level of specialized expertise that complements broader AI risk management frameworks.

Our commitment to both defense (Lakera Guard) and proactive testing (Lakera Red) is key to building secure and trustworthy AI systems.

Key Takeaways

Responsible AI development requires addressing technical risks alongside ethical and societal concerns. Frameworks like the NIST AI RMF, ISO/IEC 23894:2023, the EU AI Act, McKinsey, and Lakera's approach all address this complexity in different but complementary ways.

Framework Highlights:

  • NIST AI RMF: Emphasizes adaptability and community engagement for practical risk management throughout the AI lifecycle.
  • ISO/IEC 23894:2023: Promotes global consistency in AI risk management, focusing on assessment, treatment, and transparency.
  • EU AI Act: Pioneering law classifying AI systems to balance innovation with safeguarding individual rights.
  • McKinsey Framework: Prioritizes collaboration among business, legal, and technical teams for proactive risk management.
  • Lakera’s Approach: We focus on AI security, particularly for LLM-based systems, offering tools to combat cyber threats.

AI risk management is an ongoing process. Frameworks must evolve as technologies advance, and organizations must constantly adapt their practices.

Key challenges ahead include addressing increasingly complex AI systems, managing algorithmic biases, and ensuring the security of AI applications across industries. Proactive risk management, collaboration, and a commitment to ethical AI development will be essential to unlocking the full potential of AI while mitigating its risks.

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