Customized Safety Risk Taxonomies

• Customized safety risk taxonomies are systematic frameworks that define, structure, and prioritize safety hazards across various critical domains.
• They employ hierarchical, multi-dimensional models with formal quantitative methods to guide risk assessment and targeted mitigation.
• Applications span software risk-based testing, regulatory compliance, and emerging fields such as autonomous vehicles and large language models.

Customized safety risk taxonomies are systematic frameworks tailored to define, assess, and manage risk in safety-critical systems, software quality assurance processes, and emerging domains such as LLMs and autonomous vehicles. These taxonomies structure the universe of hazards and risk factors to enable the targeted assessment, mitigation, and monitoring of safety risks, thereby bridging high-level standards with domain- and application-specific safety requirements.

1. Conceptual Structure and Dimensions

Customized safety risk taxonomies are typically organized into hierarchical, multi-dimensional frameworks that reflect the domain’s specific risk drivers, quality properties, and operational contexts. An exemplary structure, as proposed for risk-based software testing, consists of three top-level classes: Context, Risk Assessment, and Risk-Based Test Strategy (Großmann et al., 2019).

• Context identifies risk motivators (such as business, safety, security, or compliance), targeted quality properties (e.g., functional suitability, reliability, performance), and the risk items (such as artifacts, modules, or requirements).
• Risk Assessment characterizes the likelihood and impact of each risk, specifies estimation techniques (quantitative vs. qualitative), scales (e.g., numerical or categorical), and degree of automation.
• Risk-Based Test Strategy maps assessed risks to test planning, design, execution, and mitigation steps.

This layered decomposition allows for systematic tailoring to specific regulatory, technical, or organizational needs.

2. Formalization and Quantitative Integration

A critical attribute of contemporary safety risk taxonomies is their capacity for formal and quantitative instantiation. Typical mathematical formulations include the classical risk formula:

$R = P \times I$

where $R$ is risk, $P$ is the likelihood (probability) of occurrence, and $I$ is the impact or severity. Advanced frameworks incorporate multidimensional scoring and aggregation, as exemplified by weighted mean calculations for risk exposure:

$$RE = \frac{\sum_{i=1}^{n} w_i \cdot x_i}{\sum_{i=1}^{n} w_i}$$

where $x_i$ are risk indicators and $w_i$ are their corresponding weights (Großmann et al., 2019). These approaches enable practitioners to move from qualitative reasoning to quantitative prioritization and automation.

3. Domain-Specific Applications and Tailoring

Customized safety risk taxonomies are practically instantiated in a range of domains:

• Software Risk-Based Testing: Approaches such as SmartTesting, RACOMAT, and PRISMA each implement the taxonomic framework differently, mapping contextual parameters and assessment techniques to testing practices—from risk-based planning to test case prioritization via fuzzy expert systems.
• Regulatory Compliance: Integration with standards such as ISO/IEC/IEEE 29119, ETSI EG, and the OWASP Security Testing Guide tailors taxonomies to regulatory requirements, explicitly mapping essential activities (like context understanding, risk identification, and mitigation) to compliance activities.
• LLM and Autonomous Vehicle Domains: Similar taxonomical structures are applied, with risk items reflecting data provenance, output safety, or adversarial environmental elements (Cui et al., 11 Jan 2024, Saffary et al., 29 Feb 2024).

By explicitly referencing both high-level requirements and operational gaps, such taxonomies support the identification of domain- or application-specific hazards—facilitating bespoke risk mitigation planning.

4. Operationalization and Comparative Analysis

One key benefit of a robust risk taxonomy is its ability to position and compare competing standards, frameworks, and methodologies. Using tabular representations (such as crosswalks between standards and approaches, where coverage of taxonomy elements is explicitly marked), practitioners can:

• Identify explicit, partial, or missing coverage of risk activities across different approaches.
• Highlight areas where automation or additional refinement is needed.
• Compare the operational effectiveness and alignment with regulatory requirements.

For instance, the taxonomy's mapping onto both standards and risk-based testing approaches distinguishes where more granular or automated risk mitigation mechanisms are required.

5. Automation, Tool Support, and Future Directions

The operational utility of safety risk taxonomies is amplified by their compatibility with automation. Automated tools can support:

• Risk assessment (via data-driven or model-based approaches)
• Test case generation and prioritization
• Continuous feedback loops where test results inform iterative reassessment of risks

Taxonomies also inform the design of modular systems, enabling parallel and incremental improvements across risk identification, measurement, and mitigation (Ekmekci et al., 2019). Future work in the field highlights the integration of natural language processing, advanced vector encoding for taxonomy expansion, and the continuous adaptation of risk indicators as system environments evolve.

6. Benefits, Flexibility, and Comparative Insight

The principal strengths of a customized safety risk taxonomy are its systematic structure, operational flexibility, and effectiveness in aligning diverse safety activities with business, compliance, or engineering objectives. Key benefits include:

• Enabling transparent, repeatable, and auditable risk assessment and mitigation processes.
• Facilitating easier communication and comparison across standards and methodologies.
• Providing a practical and scalable foundation for customizing safety frameworks to fit evolving technologies, regulatory regimes, and organizational risk appetites.

The taxonomy's explicit breakdown of context, risk assessment, and risk-based test strategy supports practitioners and researchers in both evaluating the completeness of their approaches and identifying opportunities for further refinement or automation.

7. Concluding Synthesis

Customized safety risk taxonomies form the backbone of modern risk- and evidence-based assurance in safety-critical and high-assurance domains. By structuring risk along rigorously defined and contextually adapted dimensions, these taxonomies enable practitioners to tailor safety processes to the demands of frequently changing technical, regulatory, and organizational landscapes. Their formalization with mathematical models, adaptability to diverse domains, and compatibility with emerging automation tools position them as essential instruments in the ongoing pursuit of rigorous, effective, and scalable safety assurance (Großmann et al., 2019).