Safety Envelope Testing

Description

Safety envelope testing systematically evaluates AI system performance at the boundaries of its intended operational domain to identify potential failure modes before deployment. The technique involves defining the system's operational design domain (ODD), creating test scenarios that approach or exceed these boundaries, and measuring performance degradation as conditions become more challenging. By testing edge cases, environmental extremes, and boundary conditions, it reveals where the system transitions from safe to unsafe operation, enabling the establishment of clear operational limits and safety margins for deployment.

Example Use Cases

Safety

Testing autonomous vehicle perception systems at the limits of weather conditions, lighting, and sensor coverage to establish safe operational boundaries and determine when human intervention is required.

Assessing financial trading algorithms under extreme market conditions and volatility to prevent catastrophic losses and ensure system shutdown protocols activate appropriately.

Reliability

Evaluating medical AI diagnostic systems with edge cases near decision boundaries to ensure reliable performance and identify when the system should defer to human specialists.

Limitations

Requires comprehensive domain expertise to identify relevant boundary conditions and edge cases that could affect system safety.
May be computationally expensive and time-consuming, especially for complex systems with high-dimensional operational domains.
Difficult to achieve complete coverage of all possible boundary conditions, potentially missing critical edge cases.
Results may not generalise to novel scenarios that fall outside the tested boundary conditions.
Establishing appropriate safety thresholds and performance criteria requires careful calibration based on domain-specific risk tolerance.

Resources

Research Papers

Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance

Andrew J. Lohn•Sep 2, 2020

Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. A central task of TEVV for AI is estimating brittleness, where brittleness implies that the system functions well within some bounds and poorly outside of those bounds. This paper argues that neither of those criteria are certain of Deep Neural Networks. First, highly touted AI successes (eg. image classification and speech recognition) are orders of magnitude more failure-prone than are typically certified in critical systems even within design bounds (perfectly in-distribution sampling). Second, performance falls off only gradually as inputs become further Out-Of-Distribution (OOD). Enhanced emphasis is needed on designing systems that are resilient despite failure-prone AI components as well as on evaluating and improving OOD performance in order to get AI to where it can clear the challenging hurdles of TEVV and certification.

Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review

Antonio V. Silva Neto et al.•Dec 14, 2022

System and Safety Analysis with SysAI A Statistical Learning Framework

Yuning He•Jul 12, 2022

Documentations

AMLAS - Assurance of Machine Learning in Autonomous Systems

University of York•Jan 1, 2021

Related Techniques

Name	Description	Assurance Goals
Red Teaming	Red teaming involves systematic adversarial testing of AI/ML systems by dedicated specialists who attempt to identify flaws, vulnerabilities, harmful outputs, and ways to circumvent safety measures. Drawing from cybersecurity practices, red teams employ diverse attack vectors including prompt injection, adversarial examples, edge case exploitation, social engineering scenarios, and goal misalignment probes. Unlike standard testing that validates expected behaviour, red teaming specifically seeks to break systems through creative and adversarial approaches, revealing non-obvious risks and failure modes that could be exploited maliciously or cause harm in deployment.	General
Anomaly Detection	Anomaly detection identifies unusual behaviours, inputs, or outputs that deviate significantly from established normal patterns using statistical, machine learning, or rule-based methods. Applied to AI/ML systems, it serves as a continuous monitoring mechanism that can flag unexpected model predictions, suspicious input patterns, data drift, adversarial attacks, or operational malfunctions. By establishing baselines of normal system behaviour and alerting when deviations exceed predefined thresholds, organisations can detect potential security threats, model degradation, fairness violations, or system failures before they cause significant harm.	General
Adversarial Robustness Testing	Adversarial robustness testing evaluates model resilience against intentionally crafted inputs designed to cause misclassification or malfunction. This technique generates adversarial examples through methods like FGSM, PGD, Carlini & Wagner (C&W) attacks, and AutoAttack to measure model vulnerability. Testing assesses both white-box scenarios (where attackers have full model access) and black-box scenarios (where attackers only observe inputs and outputs), measuring metrics like robust accuracy, attack success rates, and perturbation budgets required for successful attacks.	Security Reliability Safety
Out-of-Domain Detection	Out-of-domain (OOD) detection identifies user inputs that fall outside an AI system's intended domain or capabilities, enabling graceful handling rather than generating unreliable responses. This technique trains classifiers or uses embedding-based methods to recognise when queries require knowledge or capabilities the system doesn't possess. OOD detection enables systems to explicitly decline, redirect, or request clarification rather than hallucinating answers or applying inappropriate reasoning to unfamiliar domains.	Reliability Safety Transparency