Device fingerprinting has evolved beyond its traditional use case of fraud prevention, expanding into security and personalization use cases such as risk-based authentication, session integrity, and seamless personalization. As regulations like GDPR and CCPA tighten expectations around data collection and retention, companies must approach fingerprinting with care to ensure privacy-first approaches. Storing raw device fingerprints can expose users to risk, so hashing the fingerprint using a stable algorithm is essential. Companies should clearly state what fingerprinting data is collected, why it's collected, and how it's used in their privacy policies, and obtain explicit user consent for non-security use cases. The next frontier of device intelligence is behavior, with high-entropy identifiers generating fingerprints that are more robust against spoofing and less likely to overlap across users. Fingerprinting has become a foundational tool for secure, seamless digital experiences, but its responsible use requires collecting what's needed, obfuscating what's stored, and being honest about what's done.