Incident Timeline

The 2025 cases show that even modern, cloud-native services (Clerk, Shopify) struggle with technical debt. However, the nature of this debt has evolved:

Different industries show distinctive patterns in their schema failures:

Based on the 2025 cases, I recommend focusing on these types of schema failures for your series:

- **Over-reliance on Caching:** Slack's outage demonstrated how caching can mask inefficient query patterns, creating hidden vulnerabilities

- **Promises vs. Betrayals:** Cases are fairly evenly split, suggesting both well-intentioned designs and negligent practices contribute to major failures

Many failures occurred during specific operational phases:

- **Transportation:** Legacy systems unable to handle modern operational complexity

The proposed action items form a valuable toolkit for preventing similar failures:

Both 2018 (Facebook Cambridge Analytica, Aadhaar, SingHealth) and 2019 (Capital One, First American, AMCA) revealed catastrophic security failures stemming from fundamental architectural decisions. A recurring pattern was **assuming security through obscurity** rather than implementing robust access controls and verification. This manifested in:

Both years featured catastrophic data migration failures with similar root causes:

Many failures arose from decisions that didn't account for how data would actually be accessed:

Most major incidents across both years classified as **Betrayals** rather than **Promises Made**:

Across both years, the **ℵ₁ (Systemic)** complexity level dominated, with fewer cases at the extremes:

A pattern emerged around when in a system's lifecycle failures occurred:

- **Financial Services:** Dominated by migration failures (TSB) and access control issues (Capital One, First American)