Navigating Sensitive Data and Data Privacy: What Organizations Need to Do Now
"Data Privacy is not about locking data behind a door but understanding why certain data needs special protections."
Debbie Reynolds, "The Data Diva"
Data privacy has become a major factor as organizations navigate an increasingly complex regulatory landscape and business obligations. The growing reliance on data for business operations, decision-making, and customer interactions has amplified the need for responsible data management. Not all data carries the same level of sensitivity—some, such as biometric information and children’s data, are classified as sensitive by default, while others, such as location or behavioral data, become sensitive depending on their use and context.
Recent regulatory developments, such as the Federal Trade Commission (FTC) expanding its definition of sensitive data and the European Union’s AI Act banning AI applications deemed an unacceptable risk, underscore the heightened focus on protecting individuals' privacy. These shifts reflect an increased global effort to prevent data misuse and establish stricter compliance requirements. As a result, businesses must take proactive steps to classify, monitor, and protect sensitive data to avoid regulatory penalties and maintain consumer trust.
This essay explores the evolving nature of sensitive data, the growing scrutiny from regulators, and the key measures organizations must implement to comply with expanding business obligations. By understanding and addressing these challenges, businesses can strengthen their data privacy strategies and mitigate potential risks.
Understanding Data, Data Uses, and Sensitive Data
Sensitive data is any information that, if exposed or misused, could cause harm to individuals or organizations. Some data types are inherently sensitive, while others become sensitive based on how they are processed or combined with other data. As regulatory bodies redefine what constitutes sensitive data, organizations must carefully assess how they handle various forms of information.
Default Sensitive Data Categories
Certain data types are nearly universally recognized as sensitive due to their potential for misuse. These include:
Biometric data - For example. Fingerprints, iris scans, and other biological identifiers.
Children’s data - For example, information related to children under the age of 13, which is automatically classified as sensitive under laws such as the Children’s Online Privacy Protection Act (COPPA).
Health data - For example, medical records, genetic data, and mental health information, which are subject to strict privacy regulations under laws like the Health Insurance Portability and Accountability Act (HIPAA).
Contextually Sensitive Data
Some data becomes sensitive depending on its use, combination with other information, or potential for harm. Examples include:
Location data - For example, the FTC has classified location data as sensitive in certain contexts, such as when automakers or mobile applications collect and sell it.
Behavioral data - For example. information about user activity, purchasing patterns, and online interactions can become sensitive when used for profiling or targeted advertising.
Financial data - For example, credit scores, banking transactions, and payment details are often classified as sensitive due to their impact on financial security.
Regulators continue to refine definitions of sensitive data, and businesses must remain vigilant in tracking these developments. For instance, the U.S. government has moved to restrict the sale of Americans' location data outside of the country, treating it with the similar caution as export-controlled materials.
Increased Obligations, Scrutiny, and Penalties
With regulatory bodies tightening their oversight of data privacy, businesses that mishandle sensitive data face increased legal, financial, and reputational consequences. Organizations must stay informed about the latest compliance requirements to avoid costly penalties.
Regulatory Focus on Sensitive Data
Regulators are especially aggressive in enforcing protections for sensitive data, with key focus areas including:
Misuse of biometric data - For example, laws such as the Illinois Biometric Information Privacy Act (BIPA) and the European Union’s General Data Protection Regulation (GDPR) impose strict requirements on businesses that collect biometric data.
Protection of children’s data - For example, organizations must follow stringent guidelines when collecting data from minors, ensuring compliance with laws like COPPA and GDPR.
Restrictions on location data - For example, the FTC’s classification of location data as sensitive affects businesses that collect, store, or sell this information, requiring them to implement stronger safeguards.
AI Regulation and Data Sensitivity
The rise of artificial intelligence (AI) has introduced new concerns regarding data privacy. AI models process vast amounts of data, and regulators are increasingly scrutinizing how businesses use AI-driven technologies. Key concerns include:
AI profiling and discrimination - For example, automated decision-making systems that analyze personal data can lead to biased outcomes, necessitating stricter regulatory oversight.
Automated decision-making risks - For example, AI-powered hiring, lending, and insurance decisions require additional safeguards to prevent discriminatory practices and ensure transparency.
The AI Act, a landmark regulatory framework in the European Union, has classified certain AI uses involving sensitive data as an unacceptable risk, banning them outright. These include:
AI-based social scoring systems that evaluate individuals based on behavior, socioeconomic status, or personal characteristics.
Real-time biometric identification in public spaces for law enforcement, except under limited and strictly regulated conditions.
AI systems that exploit vulnerabilities based on an individual’s age, disability, or economic situation.
These regulations highlight the growing global effort to limit high-risk AI applications that depend on sensitive data. Businesses leveraging AI must evaluate their models against these evolving compliance requirements.
Rising Penalties for Data Misuse
Organizations that fail to comply with data privacy regulations face severe consequences, including:
Regulatory fine - Under GDPR, violations can result in fines of up to 4% of a company’s annual global revenue.
Lawsuits and class actions - For example, businesses that mishandle biometric, health, or location data have faced multimillion-dollar lawsuits.
Loss of business relationships - For example, companies that fail to meet data privacy standards may lose partnerships with entities that demand compliance.
What Organizations Should Do Now to Prepare
Businesses must implement robust data governance strategies to mitigate risks and comply with evolving data privacy regulations and expected business practices. Key actions include:
1. Classify and Monitor Sensitive Data
A thorough data inventory is essential for identifying and securing sensitive information. Organizations should:
Determine which data types are inherently sensitive and which may become sensitive based on context.
Assess data flows to understand how information moves within and outside the organization.
Implement data classification frameworks to apply appropriate security measures.
2. Strengthen Third-Party Data Oversight
Many organizations share data with vendors, service providers, and business partners. To ensure compliance, companies should:
Conduct regular reviews of third-party data handling practices.
Establish contractual agreements requiring adherence to data privacy obligations.
Limit third-party access to sensitive data through strong data controls.
3. Stay Informed on Regulatory Developments
With data privacy expectations continually evolving, businesses must:
Assign dedicated compliance teams to monitor new regulations.
Engage with industry groups and regulatory bodies to stay ahead of policy changes.
Adapt governance policies to align with emerging legal requirements.
As data privacy regulations and expectations expand and sensitive data definitions evolve, organizations must adopt proactive measures to remain compliant and protect consumer trust. The FTC’s classification of location data as sensitive and the AI Act’s bans on high-risk AI applications illustrate the rapid shift in regulatory expectations.
Businesses that handle sensitive data must implement strong data governance policies, verify third-party compliance, and stay informed about emerging regulations. By doing so, organizations can enhance data privacy protections, avoid regulatory penalties, and build long-term trust with customers and stakeholders, and make Data Privacy a Business Advantage.
