Technical debt
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Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk

Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt.

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Intent-based testing

Intent-based chaos testing is designed for when AI behaves confidently — and wrongly

Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomalies and trigger the appropriate response. Late one night, it flags an elevated anomaly score across a production cluster, 0.87, above its defined threshold of 0.75. The agent is within its permission boundaries. It has access to the rollback service. So it uses it.

AI drift

Five signs data drift is already undermining your security models

Data drift happens when the statistical properties of a machine learning (ML) model's input data change over time, eventually rendering its predictions less accurate. Cybersecurity professionals who rely on ML for tasks like malware detection and network threat analysis find that undetected data drift can create vulnerabilities. A model trained on old attack patterns may fail to see today's sophisticated threats. Recognizing the early signs of data drift is the first step in maintaining reliable and efficient security systems.