Protect Your Data: A list of personally identifiable information You Must Know

In digital analytics, data is the currency of growth. But hidden within your event streams, dataLayers, and marketing pixels is a significant risk: Personally Identifiable Information (PII). Accidentally sending an email address to Google Analytics or a name to a marketing pixel isn't just a mistake; it's a potential violation of GDPR, CCPA, and other global privacy laws, carrying fines that can cripple a business. The line between harmless behavioral data and regulated personal data is often blurry, and what constitutes PII can vary significantly by jurisdiction. Understanding a company's approach to data privacy is essential, often outlined in their comprehensive privacy policy.

This article provides a comprehensive list of personally identifiable information, breaking down the 10 most common—and riskiest—types of PII that analytics and marketing teams must learn to identify, monitor, and protect. We'll move beyond generic definitions to provide real-world examples of how PII leaks into analytics, from form submission events to URL query parameters. For each type, you'll find actionable strategies to prevent costly compliance failures and maintain data integrity.

To help technical teams, we'll also highlight how to leverage tools like regular expressions for proactive detection. In fact, at Trackingplan we have an Open Source project on GitHub featuring a battery of regular expressions organized by country to detect personal data, available at this link https://github.com/trackingplan/pii-regex-library. This guide will equip you with the knowledge to not only understand what PII is but to actively find and manage it across your entire data ecosystem.

1. Full Name and Contact Information

Full names combined with direct contact details like email addresses and phone numbers represent one of the most common and high-risk categories in any list of personally identifiable information (PII). This combination is a direct identifier, meaning it can pinpoint a specific individual without needing additional data. While essential for business operations like user registration, e-commerce checkouts, and CRM management, its presence in analytics data streams creates significant compliance and privacy vulnerabilities.

When a user submits a form, this data is often pushed to a dataLayer object. If not handled correctly, variables like user.name or user.email can be inadvertently picked up by analytics tags (e.g., Google Analytics, Mixpanel) and sent to third-party servers. This action often violates the terms of service of these platforms and can lead to serious breaches of data privacy regulations like GDPR and CCPA.

Practical Mitigation Strategies

To prevent accidental data leakage, analytics and development teams must implement robust safeguards. The goal is to separate operational data (like a customer's name for shipping) from analytical data (like a purchase event).

Here are several actionable tips:

• Server-Side Tagging: Implement a server-side Google Tag Manager container. This setup acts as a proxy, allowing you to control and redact sensitive information before it's forwarded to vendor endpoints like Google Analytics or Facebook Ads. For instance, you can intercept an event, remove the email_address parameter, and only pass non-PII data.
• Automated PII Detection: Continuously monitor your data streams for accidental leaks. Tools can automatically scan your analytics implementation for common PII patterns. Trackingplan even offers an open-source PII regex library on GitHub with country-specific regular expressions to help teams build their own detection systems.
• Data Layer Masking: Before data is sent, apply masking or hashing rules directly within your dataLayer implementation. Instead of sending 'John Smith', you could send a non-reversible hashed ID, preserving user-level analysis without exposing personal details.

By adopting these proactive measures, you can maintain a clean and compliant analytics environment. For a deeper dive into this topic, explore our comprehensive guide on achieving PII data compliance.

2. Social Security Numbers and Government IDs

Unique government-issued identifiers, including Social Security Numbers (SSN), passport numbers, and driver's licenses, are among the most sensitive categories on any list of personally identifiable information. These direct identifiers are often legally protected and carry severe penalties if mishandled. Their presence in analytics data streams is almost never legitimate and signals a critical compliance failure with high-stakes legal and financial consequences.

These IDs are sometimes collected for identity verification or compliance with Know Your Customer (KYC) regulations. However, if this sensitive data is captured by analytics tags during form submissions or API calls, it can be illegally transmitted to third-party platforms. For example, a dataLayer event related to a loan application could mistakenly include a user.ssn or user.driver_license_number field, sending it directly to tools like Google Analytics or marketing pixels. This action constitutes a severe data breach and violates the terms of service of virtually all analytics vendors.

Practical Mitigation Strategies

The primary goal is to create an impenetrable barrier between systems that legitimately handle government IDs and your analytics or marketing data streams. This data should be treated as toxic and never allowed to touch third-party tracking scripts.

Here are several essential tips:

• Strict Data Governance Policies: Establish and enforce clear internal policies that explicitly prohibit the collection or transmission of any government-issued IDs to analytics or marketing platforms. This rule should be a cornerstone of your data governance framework.
• Real-Time PII Detection and Alerting: Implement automated monitoring solutions to continuously scan all data streams for government ID patterns. A tool like Trackingplan can provide real-time alerts via Slack or Teams the moment an SSN or passport number is detected, enabling immediate incident response.
• Proactive Audits and Regex Libraries: Conduct regular, scheduled audits of all tracked properties to hunt for government ID formats. To build a robust internal detection system, your team can leverage comprehensive open-source resources like Trackingplan's PII regex library on GitHub, which contains country-specific expressions for various ID types.
• Input Validation and Form Design: Ensure web forms are designed to prevent users from entering sensitive IDs in fields intended for other purposes. Implement strict client-side and server-side validation rules to block and flag submissions containing formats that match government IDs in non-essential fields.

3. Financial Account Information

Financial data, including credit card numbers, bank account details, and payment credentials, is among the most sensitive and highly regulated entries on any list of personally identifiable information. Its exposure presents severe security risks and triggers strict compliance obligations under standards like the Payment Card Industry Data Security Standard (PCI-DSS). While essential for transactions, this information should never, under any circumstances, be sent to analytics or marketing platforms.

Accidental leakage often occurs during checkout processes when form data is improperly captured. For instance, a misconfigured tag might scrape all fields from a payment form, inadvertently sending a credit_card_number or cvv to a dataLayer and subsequently to Google Analytics. Even payment tokens from services like Stripe or PayPal, if mishandled, can be exposed to third-party pixels, creating a direct violation of PCI-DSS and user trust.

Practical Mitigation Strategies

The primary goal is to completely isolate financial data from analytics streams. This requires a strict separation between payment processing environments and marketing or analytics data collection.

Here are several actionable tips:

• Isolate Payment Processing: Ensure all payment processing occurs on secure, PCI-DSS compliant servers, completely segregated from client-side tagging and analytics scripts. Never capture or store full card numbers in client-side code, cookies, or the dataLayer.
• Leverage Tokenization: Use payment gateways like Stripe, PayPal, or Square that handle sensitive data directly and provide non-sensitive tokens for transaction references. This ensures your systems never touch raw financial information.
• Implement Server-Side Tagging: Route payment-related events like purchase through a server-side GTM container. This allows you to meticulously control the data flow, ensuring that only non-sensitive transaction details (e.g., order_id, revenue) are forwarded to analytics destinations.
• Automated Financial PII Scans: Proactively monitor all data streams for financial patterns. You can build custom detection systems using libraries like Trackingplan's open-source PII regex library on GitHub, which contains expressions for identifying financial data formats specific to different countries.

4. Email Addresses and Login Credentials

Email addresses and login credentials, such as passwords and authentication tokens, are a critical category in any list of personally identifiable information. While an email address alone may seem less sensitive than a social security number, it is a direct identifier often used for authentication and account recovery. When combined with passwords or session tokens, its exposure creates severe security risks, including account takeovers and broader data breaches.

This type of data frequently appears in analytics streams through user authentication events. For example, a login event might mistakenly include a user’s email or a session token in its properties. Similarly, form submission tracking pixels can inadvertently capture login credentials, sending them directly to third-party marketing and analytics platforms. This not only violates platform terms of service but also poses a direct threat to user security and privacy.

Practical Mitigation Strategies

Protecting login credentials requires a strict separation between authentication systems and analytics instrumentation. The primary goal is to ensure that sensitive access information never leaves your secure, first-party environment.

Here are several actionable tips:

• Hash PII for Analytics: Before sending user identifiers like email addresses to analytics platforms, hash them using a one-way encryption algorithm like SHA-256. This allows for user-level analysis without exposing the raw email. Instead of sending user_email: 'jane.doe@example.com', send a hashed version.
• Never Track Credentials: Implement a zero-tolerance policy for tracking passwords, API keys, or authentication tokens in client-side analytics. These credentials should be handled exclusively on the server side and never be included in a dataLayer push or event property.
• Use Server-Side Tagging: A server-side container acts as a secure proxy. It allows you to receive data from your application, strip out any sensitive credentials or unhashed emails, and then forward a sanitized version of the event to third-party vendors.
• Implement Consent and Data Minimization: Only capture an email hash in analytics if it's absolutely necessary for attribution or user journey analysis, and always ensure you have explicit user consent. In many cases, a randomly generated user ID is a more privacy-conscious alternative.

5. Location Data and IP Addresses

Precise geographic information, including GPS coordinates and IP addresses, is a powerful yet sensitive category on any list of personally identifiable information. While invaluable for personalization, localized marketing, and fraud detection, this data can directly identify an individual's home, workplace, or movement patterns. Under regulations like GDPR, location data is considered PII when it can be used to single out or track a person, making its collection and processing a high-stakes compliance activity.

This data often enters analytics streams through mobile app events (current_location), form submissions (user_address), or automatically collected IP addresses. Sending precise coordinates or an unmasked IP address to analytics platforms like Google Analytics not only violates their terms of service but also creates a significant privacy risk. For example, linking a user ID to a precise latitude and longitude over time can reveal highly personal behavioral patterns.

Practical Mitigation Strategies

To leverage location insights safely, teams must implement strict controls to de-identify or aggregate data before it reaches third-party analytics vendors. The objective is to analyze regional trends without compromising individual privacy.

Here are several actionable tips:

• IP Anonymization: Enable IP address masking in your analytics tools. For Google Analytics 4, this is enabled by default, but for older implementations using Universal Analytics, you must explicitly set the anonymize_ip parameter to true. This truncates the last octet of the IP address, making it impossible to pinpoint a specific user.
• Data Generalization: Instead of collecting precise GPS coordinates, limit location tracking to a less specific level, such as the city, state, or region. This allows for geographical segmentation in your analysis without storing sensitive, individual-level data.
• Explicit Consent Mechanisms: Before collecting any precise location data, especially from mobile devices, implement a clear and explicit user consent mechanism. Configure your consent management platform (CMP) to disable location-based tracking for users in restricted regions or for those who do not opt-in.
• Automated Monitoring: Use a data governance tool to continuously scan your event payloads for accidental leaks of location data. Monitoring for parameters like latitude, longitude, or gps_coordinates ensures that no unapproved PII is sent to your analytics or marketing platforms.

6. Health and Medical Information

Health and medical data is one of the most sensitive and stringently regulated entries in any list of personally identifiable information. This category includes medical history, diagnoses, biometric data like heart rate, medication use, and even health-related search queries. Classified as "special category data" under GDPR and protected by laws like HIPAA in the US, its presence in marketing or analytics systems poses an extreme compliance risk and can lead to severe penalties.

This type of PII can leak into analytics in subtle ways. For example, a user on a health and wellness app might track their symptoms, and this data (symptom_logged: 'migraine') could be sent as an event property to an analytics platform. Similarly, a search query on a pharmacy website for a specific medication might be captured by a marketing pixel, inadvertently linking a user's advertising profile to a health condition.

Practical Mitigation Strategies

Handling health data requires a complete separation between operational, clinical systems and marketing or analytics platforms. The core principle is to prevent any health-related data point from ever reaching a non-compliant third-party tool.

Here are several actionable tips:

• Strict Data Segregation: Implement entirely separate, HIPAA-compliant infrastructure for any system that processes health data. This infrastructure must be firewalled from your standard analytics and marketing stack. Ensure any required third-party processors sign a Business Associate Agreement (BAA).
• Keyword and Regex Filtering: Proactively block health-related terms from being captured. Use server-side tagging or data validation rules to scan event properties and URL parameters for medical terminology, medication names, or disease-related keywords and redact them before they are sent to third-party vendors.
• Automated Monitoring and Audits: Continuously scan all data streams for accidental health data leaks. Specialized monitoring tools can be configured with custom validation rules and regular expressions to detect and alert on health-related PII patterns. Trackingplan offers a powerful open-source PII regex library on GitHub that teams can use to build robust, country-specific detection systems for this exact purpose.

By enforcing these strict controls, organizations can avoid severe legal repercussions and protect their users' most sensitive information. For an in-depth look at preventing such leaks, review our guide on achieving PII data compliance.

7. Biometric Data and Device Identifiers

Biometric data (fingerprints, facial scans) and unique device identifiers (IDFA, Android Advertising ID) represent a highly sensitive and regulated category on any list of personally identifiable information. Biometric data is often considered the most restrictive PII category globally, while device IDs, though pseudonymous, become PII when they can be linked back to a specific individual. These identifiers are crucial for functions like secure authentication and cross-device attribution but pose extreme privacy risks if mismanaged.

The primary risk involves the accidental collection of this data in analytics streams. For example, an app might use fingerprint data for login authentication, but a poorly configured event could inadvertently capture a related identifier and send it to platforms like Mixpanel or Amplitude. Similarly, an advertising ID like an idfa or aaid might be pushed to a dataLayer and forwarded to Google Analytics, violating its terms of service and privacy regulations like GDPR, which require explicit consent for such tracking.

Practical Mitigation Strategies

The cardinal rule is to completely isolate biometric data from any analytics or marketing data pipelines. For device IDs, the goal is to manage them in a privacy-compliant manner, respecting user consent and platform policies.

Here are several actionable tips:

• Strict Data Segregation: Architect your systems to ensure biometric processing and storage are entirely separate from your behavioral analytics infrastructure. This data should never be accessible to tagging or marketing SDKs.
• Leverage Privacy-First Frameworks: For iOS attribution, prioritize Apple's SKAdNetwork over the IDFA. SKAdNetwork provides privacy-preserving attribution data without exposing user-level device identifiers, aligning with modern privacy standards.
• Implement Device ID Resets: Ensure your systems can honor user requests for data deletion by including mechanisms to reset or nullify any stored advertising IDs associated with their profile. This is a key requirement under regulations like CCPA.
• Automated Monitoring and Validation: Continuously scan your analytics events to validate that no biometric or device identifiers are being leaked. Automated tools can alert you if a field like user.fingerprint_id or device.idfa unexpectedly appears in a payload sent to a third-party vendor.

8. Online Identifiers and Behavioral Tracking Data

Online identifiers like cookies, user IDs, and pixel tags are central to modern digital analytics and advertising, yet they occupy a complex position in any list of personally identifiable information. While often pseudonymous on their own, they become powerful direct identifiers when linked to other data points, such as an email address or a CRM profile. This linkage allows for persistent tracking of user activities across different websites, apps, and sessions.

Regulations like GDPR and CCPA explicitly classify these online identifiers as personal data because they can be used to single out and build detailed behavioral profiles of individuals. For example, a user_id from Google Analytics, when unified with a customer's account in a Customer Data Platform (CDP), transforms anonymous browsing data into a detailed log of an identified person's actions. This creates significant compliance obligations for data collection and user consent.

Practical Mitigation Strategies

Managing online identifiers requires a consent-first approach and a clear separation between anonymous and identified user data architectures. The goal is to respect user privacy choices while enabling effective analytics and personalization where consent is given.

Here are several actionable tips:

• Implement Robust Consent Management: Use a Consent Management Platform (CMP) to obtain explicit user permission before deploying any tracking cookies or identifiers. Ensure your analytics tools, like Google Analytics, are configured with consent modes to adjust data collection based on user choices.
• Separate ID Architectures: Design your tracking to distinguish between anonymous identifiers (e.g., a randomly generated client_id for session analysis) and identified user IDs (e.g., a hashed customer_id post-login). Only link these identifiers after obtaining clear consent.
• Audit and Document ID Usage: Regularly audit all user ID schemes to ensure they comply with your consent policies and are accurately documented in your privacy notices. This includes first-party cookies, third-party pixel IDs, and mobile advertising IDs.
• Provide User Control Mechanisms: Implement clear and accessible mechanisms for users to opt-out of tracking or request the deletion of their data. This includes having processes to reset or disassociate identifiers linked to their profile upon request.

By carefully managing these identifiers, you can balance personalization with privacy. To learn more about building a compliant data strategy, explore our detailed guide on navigating privacy and compliance.

9. Demographic and Interest-Based Data

Demographic and interest-based data, such as age, gender, income level, and inferred user preferences, is a critical component of any comprehensive list of personally identifiable information. While often considered less sensitive than direct identifiers, this information becomes powerful PII when it can be linked back to a specific person. Its primary use in analytics is for audience segmentation, targeted advertising, and personalization, but it also carries risks of discrimination and privacy intrusion.

When a user provides their age range during sign-up or their browsing behavior suggests an interest in a particular product category, this data can be sent to analytics and advertising platforms. For example, a user_properties object might contain { "gender": "female", "age_bracket": "25-34", "income_level": "high" }. Sending this data, especially when it involves protected classes like race, religion, or political affiliation, can violate privacy regulations and lead to unfair microtargeting.

Practical Mitigation Strategies

To leverage demographic data responsibly, teams must balance marketing objectives with robust privacy and ethical safeguards. The focus should be on aggregated, anonymized insights rather than individual-level targeting with sensitive attributes.

Here are several actionable tips:

• Audit and Classify Data: Use a data governance platform to audit precisely what demographic data you collect, its source (explicitly provided vs. inferred), and where it is sent. Classify attributes based on sensitivity, paying special attention to protected classes.
• Avoid Protected Class Targeting: Establish a strict policy against collecting or using data related to race, religion, sexual orientation, or political affiliation for any targeting purposes. This minimizes the risk of discriminatory practices and regulatory penalties.
• Granular Consent and Transparency: Implement clear consent mechanisms that inform users about the collection and use of their demographic data for personalization or advertising. Your privacy policy should explicitly detail what is collected, how it is inferred, and how users can opt out.
• Anonymize for Analysis: For internal analysis, aggregate demographic data to identify trends without linking it to individuals. Instead of tracking a specific user's income level, analyze purchase behavior across different income brackets in an anonymized cohort.

10. User-Generated Content and Behavioral Signals

User-generated content (UGC) and behavioral signals, such as search queries, comments, and purchase history, represent a complex and increasingly scrutinized category in any list of personally identifiable information (PII). While a single behavioral signal like a page view might seem anonymous, it becomes potent PII when linked to a user account or device ID. This aggregation creates detailed behavioral profiles that can reveal sensitive personal attributes.

In modern analytics, events like product_review_submitted or search_performed can inadvertently capture highly personal data. For example, a search query for a specific medical condition or a product review that includes a personal story can easily be sent to analytics platforms. This not only violates platform terms of service but also poses significant privacy risks, as this data can be used to infer sensitive information about a user's health, beliefs, or financial status.

Practical Mitigation Strategies

To leverage behavioral data for insights without compromising user privacy, organizations must implement clear boundaries and technical controls. The objective is to analyze trends in aggregate while preventing the re-identification of individuals through their specific actions.

Here are several actionable tips:

• Audit Behavioral Data Collection: Use a platform like Trackingplan to continuously audit all behavioral signals being collected. This allows you to identify exactly which user actions are being tracked, where they originate (e.g., search bars, comment fields), and where they are being sent, preventing sensitive data from reaching unintended destinations.
• Implement Granular Consent: Before tracking potentially sensitive behavioral signals, such as video watch history or detailed browsing patterns, implement clear consent mechanisms. Allow users to opt-in to specific types of tracking rather than using a single, all-encompassing consent banner.
• Anonymize and Aggregate: For trend analysis, aggregate behavioral data to remove individual identifiers. Instead of tracking an individual user's purchase history, analyze product purchase frequency across all users. This approach provides valuable business intelligence without creating high-risk individual profiles.
• Provide User Control: Empower users with control over their data by implementing features for data access, download, and deletion. This is a core requirement of regulations like GDPR and CCPA and builds trust by giving users transparency and agency over their personal information.

Comparison of 10 PII Categories

From Detection to Prevention: Automating Your PII Compliance

Navigating the extensive list of personally identifiable information we've detailed is more than an academic exercise; it's a fundamental requirement for responsible data management in the digital age. We've journeyed through the clear-cut direct identifiers like names and Social Security numbers, explored the nuanced world of indirect identifiers such as IP addresses and device IDs, and underscored the critical sensitivity of health and biometric data. The key takeaway is that PII is not a static concept. It's a dynamic and context-dependent category of data that can surface anywhere, from URL parameters and form fields to seemingly innocuous event properties in your analytics implementation.

The risks of mishandling this information are substantial, extending beyond hefty regulatory fines from frameworks like GDPR and CCPA. A single PII leak can irrevocably damage user trust, tarnish your brand's reputation, and corrupt the integrity of your analytics data, leading to flawed business decisions. Manual audits and periodic spot-checks, while well-intentioned, are simply no match for the speed and complexity of modern development cycles and the sprawling web of third-party marketing and analytics tools. A PII-laden value can be introduced in a single line of code and go undetected for months, silently propagating to downstream systems.

Shifting from Reactive Audits to Proactive Governance

The only viable, long-term solution is to move from a reactive, manual posture to a proactive, automated one. The goal is to build a system of continuous observability that acts as a perpetual guardian of your data flows. This involves embedding PII detection directly into your development and QA workflows, not treating it as an afterthought.

Effective data governance in this context relies on two core principles:

• Comprehensive Discovery: You cannot protect what you cannot see. The first step is gaining a complete, real-time inventory of every single data point being collected across all your digital properties. This includes every dataLayer.push, every event, and every parameter being sent to vendors like Google Analytics, Mixpanel, or Facebook Ads.
• Continuous Monitoring: With a complete picture in place, the next step is continuous, automated scanning. This system must be capable of validating every piece of data against a robust set of rules to flag potential PII leaks the moment they occur, long before they reach your analytics platforms or become a compliance crisis.

Key Insight: True data privacy isn't achieved through a one-time audit. It is the result of an automated, always-on system that makes compliance an integral part of your data operations, not a separate, periodic task.

Empowering Your Team with Open-Source Tools

To aid in this mission, fostering a culture of privacy-by-design is crucial. One practical way to empower your development and QA teams is by providing them with the right tools. At Trackingplan, we are committed to supporting the data community in this effort.

We have developed and maintain an Open Source project on GitHub: the PII Regex Library. This repository contains a comprehensive collection of regular expressions specifically designed to detect various types of personal data, conveniently organized by country.

Link to the repository: https://github.com/trackingplan/pii-regex-library

Integrating these regex patterns into your CI/CD pipelines, testing scripts, or internal validation tools can serve as a powerful first line of defense. It equips your team to catch PII leaks at the source, making data privacy a shared responsibility across the entire organization. By leveraging community-driven resources like this, you can significantly enhance your ability to identify and mitigate risks before they escalate. Ultimately, mastering the list of personally identifiable information means operationalizing its detection and prevention, transforming knowledge into automated, protective action.

Ready to move beyond manual checks and automate your PII compliance? Trackingplan offers a complete data observability platform that automatically discovers 100% of your tracking and continuously scans for PII leaks in real time, ensuring your analytics data is both powerful and private. See how Trackingplan can protect your data today.