The Risk Llama AI Reference Taxonomy

Published: April 2025

Organizations are rapidly adopting artificial intelligence (AI) technologies to enhance operations, improve customer experiences, and gain competitive advantages. However, this adoption introduces a new dimension of operational risks that traditional risk frameworks struggle to capture. The distinct characteristics of AI systems – self-learning capabilities, opacity, complexity, and autonomous behavior – create unique risk profiles that require specialized risk management approaches.

At Risk Llama, we have developed an AI Risk Reference Taxonomy that complements existing operational risk frameworks while addressing these unique AI-specific challenges. This specialized taxonomy helps organizations identify, categorize, and manage risks that exist specifically because of AI's unique attributes.

Why Traditional Risk Categories Fall Short

Conventional operational risk taxonomies were developed for traditional systems with predictable behaviors, clear causality, and straightforward audit trails. AI systems, particularly those using deep learning, neural networks, and large language models (LLMs), operate quite differently. They can develop emergent behaviors, make decisions through opaque processes, and create complex feedback loops that traditional risk categories simply weren't designed to address.

For example, while a standard model risk framework might adequately cover traditional statistical models, it lacks the dimensions needed to assess risks related to neural network architecture choices, transfer learning failures, or reinforcement learning reward misalignments.

A New Dimension of Risk Management

The Risk Llama AI Risk Reference Taxonomy identifies ten distinct risk categories specifically tailored to AI systems:

AI Model Integrity addresses risks in the design, training, and deployment of complex AI models that exhibit self-learning capabilities. Unlike traditional models, AI systems can develop unexpected behaviors that conventional validation approaches may miss.

AI Algorithmic Performance focuses on how AI algorithms operate in production environments. This includes unique performance degradation patterns, emergent behaviors, and feedback loop problems that don't exist in traditional systems.

AI Data Management targets the specialized data requirements of AI systems, particularly training data bias, feature engineering, and data drift management that can significantly impact AI decision-making.

AI System Security addresses unique attack vectors like adversarial examples, prompt injection, and model theft that traditional security frameworks don't cover.

AI Decision Ethics confronts the ethical implications of autonomous or semi-autonomous AI decision-making, ensuring fairness, appropriate human oversight, and ethical boundaries.

The taxonomy also covers AI Regulatory Compliance , AI Governance, AI Third-Party Management , AI Business Continuity , and AI Explainability – all with specialized subcategories that address AI-unique challenges within these domains.

Implementation Without Duplication

A key innovation of our approach is ensuring complementarity with existing frameworks rather than duplication. Each risk category provides clear boundary guidance to help risk managers determine when to use AI-specific categories versus standard operational risk categories.

For instance, our taxonomy recommends using AI Security categories only for threats that specifically exploit AI characteristics (like adversarial examples), while directing traditional security threats that happen to affect AI systems to standard Information Security categories.

This approach allows organizations to maintain their existing risk taxonomy while adding the necessary dimensions to properly address AI-specific risks.

Putting Theory into Practice

Implementing a specialized AI Risk Taxonomy can significantly improve an organization's ability to identify and manage previously obscured AI risks. Organizations can uncover critical vulnerabilities in machine learning models that conventional risk assessments would miss and detect concerning decision patterns in AI systems before they lead to regulatory issues.

The taxonomy proves particularly valuable for organizations using multiple AI systems across different business functions. It provides a consistent language for discussing AI risks across teams and helps prioritize mitigation efforts based on a more accurate understanding of unique AI risk profiles.

The Path Forward

As AI technologies continue to evolve and regulatory frameworks mature, organizations need a structured approach to understand and manage the unique risks these systems introduce. Traditional operational risk categories remain valuable but must be supplemented with AI-specific dimensions.

The Risk Llama AI Risk Reference Taxonomy serves as both a practical tool and a strategic framework. It helps organizations meet current challenges while preparing for emerging risks as AI capabilities advance.

The full taxonomy includes detailed implementation guidance, decision frameworks for risk classification, and mapping tables to integrate with existing risk management systems. We've carefully designed it to be intuitive for risk managers while providing the technical specificity needed to address complex AI risks.

Building Your AI Risk Management Capability

At Risk Llama, we believe effective AI risk management requires both specialized frameworks and practical implementation expertise. While this article introduces our innovative taxonomy, successful implementation demands a tailored approach based on your organization's specific AI systems, risk profile, and regulatory requirements.

We work directly with clients to adapt and implement this taxonomy within their existing risk management frameworks. We provide not just the taxonomy itself, but the training, tools, and ongoing support needed to build effective AI risk management capabilities.

As AI becomes increasingly central to financial operations, the gap between traditional risk frameworks and AI-specific challenges will only grow. Organizations that develop specialized AI risk management capabilities now will be better positioned to leverage AI's benefits while avoiding its unique pitfalls.

Contact Risk Llama today at info@riskllama.com to learn how our AI Risk Reference Taxonomy can enhance your organization's ability to identify, assess, and mitigate the unique risks of artificial intelligence systems.