Personal data is more than just a name or social security number. With the rise of advanced technologies and artificial intelligence, even seemingly random bits of information can be used to piece together a person’s identity, turning non-specific data into personally identifiable information (PII). This presents a challenge for organizations that rely on data to improve user experiences but must now grapple with safeguarding this sensitive information.
One of the biggest hurdles for companies handling PII is that there’s no universal definition of it. The U.S. often defines PII by specific identifiers—name, address, social security number—while other regions, like the EU, adopt a broader perspective, considering nearly any information that could reveal an individual’s identity as PII. This includes indirect identifiers such as location data or online behavior, which might not qualify as PII in the U.S. but would under the GDPR.
Companies failing to properly secure PII face serious consequences. Meta faced a $400 million fine for mishandling children’s personal data on Instagram. Such incidents highlight the increasingly strict regulatory landscape around PII, where mistakes in data handling can lead to reputational damage, legal action, and hefty fines. Regulators worldwide are tightening the reins on data security, as evidenced by the FTC’s recent lawsuit against Kochava for allegedly selling personal GPS data, potentially exposing individuals to privacy violations and physical risks.
Even location data, often considered harmless on its own, can become sensitive when tied to unique identifiers like device IDs. According to the International Association of Privacy Professionals (IAPP), GPS data becomes PII when it can be linked to a specific individual or device. The Kochava case illustrates the importance of this issue, with allegations that the company’s data practices could allow third parties to pinpoint individual locations with potentially harmful consequences.
Organizations often attempt to protect user privacy through data anonymization—removing identifiable elements so data can’t be traced back to an individual. However, this process is more challenging than it sounds. Anonymized data can sometimes be re-identified if combined with other information. Advanced AI tools make this task easier, meaning organizations must go beyond simple de-identification and regularly test their systems to ensure that data remains truly anonymous.
To navigate these challenges, organizations should integrate privacy into the design of their products and systems. Techniques like encryption, role-based access controls, and identity and access management can help secure sensitive information. Additionally, companies should frequently assess their data systems to ensure that even anonymized data cannot be reverse-engineered into identifiable information.
PII management is an ongoing responsibility that requires vigilance and adaptation. As regulatory expectations grow and AI technology advances, the best approach is proactive: adopting robust privacy measures, routinely testing data anonymity, and staying ahead of evolving privacy laws.
