SECAI Core · phase 4 of 15
Privacy
An AI principle that AI systems collect, use, share, and store personal data in a way that minimizes data collection, protects individuals' identities and sensitive information, and complies with laws and user expectations
The Explain card
- Plain English
- Privacy, as an AI principle, means systems collect only the personal data they need, protect people's identities and sensitive details, and handle data in ways that match the law and what users reasonably expect.
- Example
- A voice assistant could store every recording forever "in case it helps training". A privacy-respecting design keeps audio only as long as needed, strips identifiers before any training use, and lets users delete their history.
- Why it matters
- Models remember more than people think. Over-collection creates a larger breach surface, and memorised personal data can resurface in outputs. Minimising what goes in reduces what can come out.
- Hook
- Privacy in AI is data minimisation with manners: collect less, protect more, surprise no one.
Where it sits in the deck
Phase 4: Data Integrity, Governance, and Provenance
Once you know what data is, learn the principles that keep it trustworthy, traceable, and minimized throughout its lifecycle.
