Runtime Monitoring and Circuit Breakers

Description

Runtime monitoring and circuit breakers establish continuous surveillance of AI/ML systems in production, tracking critical metrics such as prediction accuracy, response times, input characteristics, output distributions, and system resource usage. When monitored parameters exceed predefined safety thresholds or exhibit anomalous patterns, automated circuit breakers immediately trigger protective actions including request throttling, service degradation, system shutdown, or failover to backup mechanisms. This approach provides real-time defensive capabilities that prevent cascading failures, ensure consistent service reliability, and maintain transparent operation status for stakeholders monitoring system health.

Example Use Cases

Safety

Implementing circuit breakers in a medical AI system that automatically halt diagnosis recommendations if prediction confidence drops below 85%, error rates exceed 2%, or response times increase beyond acceptable limits, preventing potentially harmful misdiagnoses during system degradation.

Reliability

Deploying runtime monitoring for a recommendation engine that tracks recommendation diversity, click-through rates, and user engagement patterns, automatically switching to simpler algorithms when complex models show signs of performance degradation or unusual behaviour patterns.

Transparency

Establishing transparent monitoring dashboards for a loan approval system that display real-time metrics on approval rates across demographic groups, processing times, and model confidence levels, enabling stakeholders to verify consistent and fair operation.

Limitations

Threshold calibration requires extensive domain expertise and historical data analysis, as overly sensitive settings trigger excessive false alarms whilst conservative thresholds may miss genuine system failures.
False positive alerts can unnecessarily disrupt service availability and user experience, potentially causing more harm than the issues they aim to prevent, especially in time-sensitive applications.
Sophisticated attacks or gradual performance degradation may operate within normal metric ranges, evading detection by staying below established thresholds whilst still causing cumulative damage.
Monitoring infrastructure introduces additional complexity and potential failure points, requiring robust implementation to avoid situations where the monitoring system itself becomes a source of system instability.
High-frequency monitoring and circuit breaker mechanisms can add computational overhead and latency to system operations, potentially impacting performance in resource-constrained environments.

Resources

Research Papers

Improving Alignment and Robustness with Circuit Breakers

Andy Zou et al.•Jan 1, 2024

AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with “circuit breakers.” Existing techniques aimed at improving alignment, such as refusal training, are often bypassed. Techniques such as adversarial training try to plug these holes by countering specific attacks. As an alternative to refusal training and adversarial training, circuit-breaking directly controls the representations that are responsible for harmful outputs in the first place. Our technique can be applied to both text-only and multimodal language models to prevent the generation of harmful outputs without sacrificing utility—even in the presence of powerful unseen attacks. Notably, while adversarial robustness in standalone image recognition remains an open challenge, circuit breakers allow the larger multimodal system to reliably withstand image “hijacks” that aim to produce harmful content. Finally, we extend our approach to AI agents, demonstrating considerable reductions in the rate of harmful actions when they are under attack. Our approach represents a significant step forward in the development of reliable safeguards to harmful behavior and adversarial attacks. Code is available at github.com/GraySwanAI/circuit-breakers.

Software Packages

aiobreaker

Jul 27, 2018

Python implementation of the Circuit Breaker pattern.

Related Techniques

Name	Description	Assurance Goals
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
Human-in-the-Loop Safeguards	Human-in-the-loop safeguards establish systematic checkpoints where human experts review, validate, or override AI/ML system decisions before they take effect. This governance approach combines automated efficiency with human judgement by defining clear intervention criteria (such as uncertainty thresholds, risk levels, or sensitive contexts) that trigger mandatory human oversight. By incorporating domain expertise, ethical considerations, and contextual understanding that machines may lack, these safeguards help ensure that critical decisions maintain appropriate human accountability whilst preserving the benefits of automated processing for routine cases.	General
Confidence Thresholding	Confidence thresholding creates decision boundaries based on model uncertainty scores, routing predictions into different handling workflows depending on their confidence levels. High-confidence predictions (e.g., above 95%) proceed automatically, whilst medium-confidence cases (e.g., 70-95%) may trigger additional validation or human review, and low-confidence predictions (below 70%) receive extensive oversight or default to conservative fallback actions. This technique enables organisations to maintain automated efficiency for clear-cut cases whilst ensuring appropriate human intervention for uncertain decisions, balancing operational speed with risk management across safety-critical applications.	General
Safety Guardrails	Safety guardrails apply real-time content moderation and safety constraints during deployment, generating evidence of safety violations and protection effectiveness. This technique produces detailed logs of blocked content, safety incident reports, violation pattern analysis, and guardrail performance metrics. Evidence includes categorised threat taxonomies, false positive/negative rates, and temporal trends in attack patterns, supporting assurance claims about deployed system safety.	Safety Security Reliability