Critical Approaches to AI Failure Analysis

February 2, 2026

Authors

Senior Managing Scientist

Key Takeaways
AI system failures can have far-reaching consequences, requiring structured forensic analysis.
Applying proven failure analysis methods from traditional engineering practices helps address the unique complexities and risks posed by AI technologies and bridges the gap between algorithmic failures and real-world impacts.
Effective investigation demands integrated AI, cybersecurity, engineering, and domain expertise and a deep understanding of legal and regulatory risks.

As AI systems transition from digital models and test labs into vehicles, hospitals, factories, and critical infrastructure, AI failures increasingly resemble major system incidents rather than simple software bugs. In a commercial landscape where high-profile AI failures can lead to lost revenue, product recalls, regulatory action, public safety concerns, and costly litigation, organizations seeking to leverage AI want better failure responses than ad hoc debugging.

Instead, industry stakeholders can adopt robust, integrated, and proven failure analysis practices like those long used in aircraft, power systems, and medical device investigations. To address complex failure events with confidence, it is essential to synthesize deep expertise in AI and ML applications and cybersecurity with practical, hands-on engineering and failure analysis experience — all informed by a clear understanding of legal and regulatory risks. The consequences of falling short in any of these areas can be severe, impacting public safety, organizational reputation, and legal liability.

What is failure analysis for AI?

Traditional failure analysis is a systematic forensic process for identifying the specific mechanisms that led to a failure. This process involves collecting data, developing and testing hypotheses, and conducting rigorous analyses. High-quality insights can then inform changes that improve performance, reliability, and safety.

Applying failure analysis to complex, rapidly evolving AI applications means adapting proven, hard‑won methods from traditional engineering and software investigations to the AI lifecycle. Traditional methods must also expand to account for conditions that are specific to AI models and applications, including randomized and non-deterministic behavior; data bias, gaps, and drift that can cause and amplify errors in algorithmic systems; and autonomous integration with vehicles, robotics, medical devices, grid control, and other physical, real-world systems.

AI failures increasingly resemble major system incidents rather than simple software bugs.

Incident response, scoping, and reconstruction

Just as well-established protocols guide engineers when responding to a fractured industrial component or corrosion in complex electronics, adhering to a structured approach in the wake of an AI algorithm failure supports clarity and consistency, drawing on established practices while addressing the unique complexities of AI.

Capture and contain the incident: Stabilize the system to prevent further harm, preserve all relevant system and current-state data, and establish a unified incident timeline across AI, software, infrastructure, and physical components.
- Define the problem and its scope: Define the failure clearly and identify all affected stakeholders. Account for impacts to safety, security, performance, fairness, compliance, and operations, distinguishing between isolated incidents and systemic issues to pinpoint whether conditions are local or global.
- Reconstruct and replicate: Re-create the failure in a controlled setting using preserved data, configurations, and model artifacts. For stochastic systems — models that generate probabilistic outputs rather than fixed, deterministic results — use seed control to limit randomness in the results, aiding reproducibility and analysis, and employ high-fidelity simulation and hardware-in-the-loop testing to safely reenact failure scenarios in cyber-physical systems.

Systematic AI failure mode examinations

In traditional failure analysis, engineers can trace failure mechanisms from broken manufacturing equipment or a defective medical device component back through loading conditions, manufacturing variability, and design assumptions. Similarly, failure analysis for AI systems works backward from an incident through model behavior, feature generation, data pipelines, software infrastructure, and operational context.

Using a combination of traditional engineering failure analysis, software root cause analysis, and AI-specific analysis, data scientists can follow these disciplined processes to investigate real-world AI failures across applications and industries. Unlike approaches that focus solely on data science or software, Ģ��tv's methodology integrates physics-based failure analysis and deep domain expertise — enabling us to trace AI failures not just through code and data but through the physical systems and real-world environments where AI operates.

- Examine decision boundaries, feature attributions, and internal activations relevant to the incident and correlate these findings with physical system behaviors, leveraging domain-specific models to understand how algorithmic decisions manifest in the real world
  Test for brittleness — excellent performance in training environments, but failure when subjected to small perturbations or distribution shifts
  Compare behavior across model versions to identify regressions or emergent patterns
- Audit training, validation, and operational data for missing modes, bias, label noise, corruption, or drift
  Audit data in the context of physical processes and operational environments, ensuring that data-driven insights are grounded in domain realities
  Identify data transformations (pre- and post-processing) that may have amplified or masked issues
  Check for unexpected data sources introduced via integrations or third-party feeds
- Apply software engineering RCA methods (e.g., log analysis, dependency tracing, change review) in tandem with hardware and system-level diagnostics, bridging the gap between digital and physical failure modes
  Investigate deployment processes (CI/CD, over-the-air updates, configuration management) for incorrect or partially rolled out changes
- Assess whether behavior is consistent with prompt injection, data poisoning, model theft, or adversarial examples
  Correlate AI anomalies with security events (e.g., unusual access patterns, configuration changes, or network anomalies)
  In critical infrastructure contexts, evaluate potential cross-system impacts (e.g., from IT compromise into OT control layers)
- Analyze how human operators understood, trusted, or over-relied on the AI output
  Determine the degree of alignment between operator expectations and developer intentions
  Analyze interactions with both AI systems and physical equipment, considering the interplay between user actions, AI outputs, and system safety
  Examine interface design, alerts, documentation, and training that may have conditioned behavior
  Explore organizational practices — such as overrides, escalation paths, and incentives — that shape how AI outputs are used
- Use tools like fault tree analysis and cause-and-effect diagrams adapted for AI to structure root-cause hypotheses across data, models, software, and physical systems to trace failures from physical phenomena through to digital decision-making
  Differentiate between 1) proximate causes (e.g., misclassification of an object, incorrect recommendation) and 2) underlying systemic causes (e.g., under-representation of conditions in the training set, lack of defense-in-depth in security architecture, insufficient operator training)
  Quantify the relative contribution of each cause where possible, particularly for high‑dollar or contested investigations

Real-world failures, real-world stakes

By applying mature engineering failure analysis methods to AI — and adapting them to the realities of data‑driven, cyber-physical, and adversarial environments — organizations can systematically determine what went wrong, why it happened, and implement robust strategies to help prevent recurrence. This rigorous approach directly enhances real-world safety and reliability, particularly in high-stakes sectors such as healthcare, transportation, energy, and finance, where AI-driven errors can have profound consequences.

As industries increasingly rely on sophisticated AI systems, a deeper, more nuanced understanding of underlying causes — whether rooted in data, models, or other factors — empowers teams to build resilient solutions that can withstand both expected and unforeseen challenges, fostering increasing confidence and trust in the deployment of AI technologies.

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For more than 50 years, Ģ��tv has applied rigorous failure analysis to physical systems to determine not just what failed but why — and how to improve. Ģ��tv's expert AI/ML consultants leverage deep industry and scientific domain expertise to help clients bridge the gap between theoretical performance and real-world success.

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