How to Handle Data Subject Requests for Bot Detection Data

To handle a data subject request related to bot detection data, your system must be able to search, export, and delete any bot detection data tied to a specific visitor within the regulatory response window. That means you need to know where that data lives, how to retrieve it, and how to remove it when asked. Here is a step-by-step process to get there.

Technical Architecture of Bot Detection Data Storage

Bot detection data is rarely stored in one place. It often lives in distributed logs, centralized databases, and third-party systems. Understanding the architecture is the first step to handling requests.

Distributed logs are common. They capture raw events like IP addresses, user agents, and device fingerprints. These logs are often spread across multiple servers or cloud regions. Centralized databases, on the other hand, hold processed or aggregated data. They are easier to query but may not contain all raw signals.

For data subject requests, you need a unified view. This means you must design a system that can search across both distributed logs and centralized stores. One approach is to use a data lake that ingests logs from all sources. Another is to maintain a central index that maps identifiers to storage locations.

Consider using a unique identifier, such as a cookie or user ID, to link bot detection data to a person. This identifier should be stored in a way that allows fast lookup. For example, you can create a lookup table that maps user IDs to log file paths or database records.

When dealing with distributed logs, you need a search strategy. You can use a log aggregation tool like Elasticsearch or Splunk. These tools index logs and allow you to search by IP, device ID, or other fields. They also support retention policies, which help you manage data lifecycle.

Centralized databases should be indexed for PII retrieval. This means creating indexes on columns like user ID, IP address, or device fingerprint. Without indexes, queries will be slow and may miss data. Use composite indexes when you need to search by multiple fields.

Backups and archives are often overlooked. Bot detection data may be stored in backups for disaster recovery. You must include these in your search and deletion processes. Otherwise, you may fail to delete data from a backup, violating the request.

Legal Nuances: Personal Data vs. Pseudonymized Data

Under GDPR, personal data is any information that can identify a person. Bot detection data often includes IP addresses, device fingerprints, and browser behavior. These can identify a person, especially when combined with other signals.

Pseudonymized data is data that has been processed to hide identities. For example, you might replace a user ID with a random string. But if you can still link that string to a person, it is still personal data. The GDPR treats pseudonymized data as personal data if the link exists.

Device fingerprinting is a key example. A fingerprint is a combination of browser, network, and device attributes. It can be unique to a person. Even if you store only a hash of the fingerprint, you may still be able to identify the person if you have the original data or other context.

Many companies assume that because bot detection data is technical, it is not personal. This is a common trap. An IP address alone can be personal data. A device fingerprint can be personal data. The European Court of Justice has ruled that dynamic IP addresses can be personal data.

If you use pseudonymization, you must document the process. You must also ensure that the key to re-identify is kept secure. If you cannot re-identify, the data may be considered anonymous. But in practice, bot detection data often retains enough identifiers to be personal.

When handling data subject requests, you must treat pseudonymized data as personal data if you can link it. This means you must search for it, provide it, and delete it upon request. The only exception is truly anonymized data, where re-identification is impossible.

Step 1: Map Where Bot Detection Data Lives

Start by inventorying all sources of bot detection data. Identify what data is collected, where it is stored, how long it is kept, and who has access. Use your bot detection vendor's documentation. For example, BotRefund's detection methods are transparent, so you can see what signals are used.

Create a data flow diagram that shows how bot detection data moves through your systems. Include:

• Client-side scripts that collect device and behavior data
• Server-side logs that record IP addresses and user agents
• Third-party services that process or store the data
• Backup and archival systems

This map is the foundation for every data subject request. Without it, you cannot find all the data you need to respond.

In complex enterprise environments, data flows can be intricate. You may have multiple websites, mobile apps, and APIs. Each may collect bot detection data. You need to map every endpoint and every storage location. Use automated tools to discover data flows, such as data lineage tools or API documentation.

Document the data retention periods for each source. Some logs may be kept for 30 days, others for years. This affects your ability to respond to requests. If data is deleted automatically, you may not need to search it. But you must know the retention schedule.

Step 2: Build a Search and Retrieval Process

You need a way to find all data associated with a specific user. This might involve searching by IP, device ID, or other identifiers. Your vendor should provide tools or APIs. If not, you may need to work with them to build a process.

Consider these approaches:

• Use a unique identifier, such as a cookie or user ID, to link bot detection data to a person.
• Implement a search function that queries all relevant databases and logs.
• Automate the retrieval process where possible to reduce response time.

Test your search process regularly. A common mistake is to assume that a simple database query will find everything. Bot detection data often lives in multiple formats and locations, so you need a robust search strategy.

For API integration, use vendor APIs to search and export data. Many vendors offer RESTful endpoints for data subject requests. For example, you can call an endpoint like GET /v1/data-subjects/{id} to retrieve all data for a user. Ensure you authenticate and log these calls.

Database indexing is critical. Create indexes on columns that you will search by, such as user ID, IP address, or device fingerprint. Use composite indexes for multi-field searches. For example, an index on (user_id, timestamp) can speed up queries for a specific user's data.

Consider using a data warehouse or data lake to centralize data for search. This allows you to run complex queries across all sources. But be careful with data privacy. You may need to pseudonymize data in the warehouse.

Step 3: Respond to Access Requests

When someone asks for a copy of their data, you must provide it in a structured, commonly used, machine-readable format. Verify the identity of the requester first. Under GDPR, you have one month to respond.

Your response should include:

• The categories of bot detection data you hold
• The purposes of processing
• The recipients of the data
• The retention period
• A copy of the actual data

If the request is complex, you can extend the deadline by two months, but you must inform the requester within the first month. Always document why the extension is needed.

When providing data, ensure you include all relevant records. This may include raw logs, processed scores, and any derived data. If you use a vendor, they may need to provide data as well. Coordinate with them to ensure a complete response.

Use a secure method to deliver the data. Encrypt the file and send it via a secure channel. Do not email plain text. You should also log the delivery for audit purposes.

Step 4: Respond to Deletion Requests

If someone asks you to delete their data, you must do so unless there are legal grounds to keep it. For bot detection data, you might need to keep it for fraud prevention or legal compliance. But you should have a process to delete it when required.

Evaluate each deletion request against these criteria:

• Is the data still necessary for the purpose it was collected?
• Do you have a legal obligation to retain it?
• Is there a legitimate interest that overrides the individual's right to deletion?

If you decide to keep the data, you must explain your reasoning to the requester. If you delete it, ensure you remove it from all locations, including backups and vendor systems.

Deletion is not just about removing records. You must also delete any derived data, such as aggregated scores or profiles. If you use a vendor, they must delete data on your behalf. Ensure your contract includes this obligation.

For backups, you may need to wait until the backup cycle completes. But you should have a process to delete data from backups when possible. Some systems allow you to purge specific records. Others require you to restore and delete. Document your approach.

Step 5: Document Your Process and Meet Deadlines

Document every request, your response, and the actions you took. Keep logs. Ensure you meet the one-month deadline. This documentation is essential for demonstrating compliance.

Create a request log that includes:

• Date of receipt
• Requester identity verification method
• Data found and provided
• Data deleted or retained
• Response date

Regularly audit your process to identify bottlenecks. For example, if you consistently miss deadlines because you cannot find data quickly, you need to improve your search tools.

Automate where possible. Use workflow tools to track requests and deadlines. Set reminders for the one-month mark. This reduces the risk of human error.

Train your staff on data subject request handling. They need to know how to verify identity, search data, and respond. Regular training ensures consistency.

Step 6: Work With Your Bot Detection Vendor

Your vendor should support your compliance. Ask them about data retention, deletion capabilities, and whether they act as a processor. BotRefund offers a free bot audit and can help you understand bot traffic, but you need to ensure their data handling aligns with your obligations.

When evaluating a vendor, ask:

• Do they provide APIs or tools to search and delete data?
• What is their data retention policy?
• Do they sign data processing agreements?
• Can they assist with data subject requests?

If your vendor cannot support your compliance, you may need to consider alternatives. But remember, you are ultimately responsible for responding to requests, even if a vendor holds the data.

Common Pitfalls When Responding to Data Subject Requests

Many organizations make mistakes when handling requests. Here are common pitfalls and how to avoid them.

Pitfall 1: Incomplete data mapping. If you don't know where all data lives, you will miss records. Solution: Conduct a thorough data inventory and update it regularly.

Pitfall 2: Ignoring backups. Data in backups is often forgotten. Solution: Include backups in your search and deletion processes.

Pitfall 3: Assuming pseudonymized data is not personal. If you can link it, it is personal. Solution: Treat pseudonymized data as personal data unless you can prove it is anonymous.

Pitfall 4: Missing vendor data. If a vendor holds data, you must ensure they respond. Solution: Have a contract that requires vendor cooperation.

Pitfall 5: Slow search processes. If you can't find data quickly, you may miss deadlines. Solution: Use indexed databases and automated search tools.

Pitfall 6: Not verifying identity. You may send data to the wrong person. Solution: Use strong identity verification methods.

Pitfall 7: Over-retention. Keeping data longer than necessary increases risk. Solution: Set retention policies and delete data automatically.

Vendor Assessment Checklist

When choosing a bot detection vendor, evaluate their data handling capabilities. Use this checklist:

• Does the vendor provide a data processing agreement (DPA)?
• Can they search and export data for a specific user?
• Can they delete data upon request?
• What is their data retention policy?
• Do they store data in multiple regions?
• Do they offer API access for data subject requests?
• Do they have a documented process for handling requests?
• Do they provide audit logs of data access?
• Are they transparent about the data they collect?
• Do they offer a free audit or trial?

BotRefund, for example, uses 106 independent checks and cross-checks signals. They are transparent about detection methods. They also offer a free bot audit. But you must confirm their data handling practices.

Ask for a copy of their DPA. Review it with your legal team. Ensure they act as a processor and follow your instructions.

Key Facts About Bot Detection Data

Limitations and When This Advice Doesn't Apply

This advice is general and not legal counsel. It applies to GDPR and similar laws. If you are not subject to these laws, you may have different obligations. Also, if you use a bot detection service that does not store personal data, you may have fewer obligations.

Some bot detection methods are designed to be privacy-friendly, such as those that process data in-memory and do not store it. In those cases, you may not need to handle data subject requests for that data. However, you should still document why the data is not personal.

This guide also assumes you have a way to link bot detection data to an individual. If you cannot, you may not be able to respond to a request, but you should still have a process to handle it.

FAQ

What is a data subject request?

A data subject request is a formal request from an individual to access, correct, or delete their personal data under privacy laws like GDPR.

How long do I have to respond to a data subject request?

Under GDPR, you have one month to respond. In complex cases, you can extend by two more months, but you must inform the requester.

Can I refuse a deletion request for bot detection data?

Yes, if you have a legal obligation to keep the data, such as for fraud prevention or compliance. But you must document your reasoning.

Do I need to delete bot detection data if it's pseudonymized?

If the data can still be linked to an individual, it is still personal data. You must handle requests unless the data is truly anonymized.

What should I ask my bot detection vendor?

Ask about data retention, deletion capabilities, and whether they act as a data processor. Ensure they can support your compliance.

Is bot detection data always personal data?

Not always. If it cannot identify a person, it may not be personal data. But IP addresses and device fingerprints often can.