A forecasting platform may need customer data to improve its predictions, but customers may hesitate to use it unless they know how that data is stored, shared, and protected. Privacy is therefore both a governance obligation and a condition of trust and adoption. section 12.3.3 section 11.0
Privacy is broader than confidentiality. Confidentiality protects sensitive information from unauthorized disclosure; privacy also gives individuals and businesses control over how their data is collected, used, and shared. It calls for fair, transparent, and lawful processing. GDPR and CCPA reinforce these expectations through requirements concerning transparency, consent, retention, and processing. section 11.2
Match the safeguards to the product and the data:
- For consumer products, examine whether tracking, profiling, or personalization uses personal data without consent; make the privacy policy visible and offer appropriate controls, such as opting out of data collection. section 11.2 section 12.3.1
- For enterprise products, account for intellectual property, trade secrets, and data sovereignty. Self-hosting a model can alleviate concerns about sending sensitive customer data to the cloud, but privacy constraints may also limit training data and model performance. section 11.2 section 1.3.2
- Explain what data is used for and how access is controlled. Specific measures such as encryption, restricted access, and regular audits make assurances more credible than a vague promise of safety. section 12.3.3
Why a privacy assurance needs detail
After incidents involving an Alexa conversation sent to an unintended contact and exposed ChatGPT account information, users have reason to ask precise questions. A forecasting-platform team can address that concern by stating the intended use of customer data, who can access it, and what safeguards and sharing controls are in place. section 12.3.3
