Facebook/Cambridge Analytica platform data-sharing failure
Facebook's developer platform allowed a personality-quiz app to harvest private data on roughly 87 million users, most of whom never installed the app themselves; the data reached political consulting firm Cambridge Analytica, and the scandal triggered a record $5 billion FTC penalty against Facebook.
The damage was public. The root cause was preventable.
Facebook's developer platform allowed a personality-quiz app to harvest private data on roughly 87 million users, most of whom never installed the app themselves; the data reached political consulting firm Cambridge Analytica, and the scandal triggered a record $5 billion FTC penalty against Facebook.
What the software actually got wrong
Facebook's developer API let an app that a user installed also pull data about that user's friends by default, and Facebook's platform had no effective technical controls or auditing to detect or prevent a developer from harvesting that friend data at scale and passing it on to a third party in violation of its own policies.
Facebook's developer API let an app that a user...
Facebook's developer API let an app that a user installed also pull data about that user's friends by default
Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.
Facebook's platform had no effective technical controls or auditing...
Facebook's platform had no effective technical controls or auditing to detect or prevent a developer from harvesting that friend data at scale and passing it on to a third party in violation of its own policies
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
Beyond code coverage and "shall" testing
Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.
Surfaces this before code exists
Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.
Connects the failure mode to the hazard
Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.
Find the overlooked root causes before they ship.
Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.