Understanding privacy and data

Data Privacy and AI: Understanding the Risks and Rewards

Data privacy is a primary concern for many considering the adoption of AI. Hesitation regarding where data goes or what happens to sensitive information is a sign of taking responsibility seriously. In the realm of security, there is no one-size-fits-all solution; there are always trade-offs where risks must be balanced against potential wins for a team.

Privacy concerns operate primarily within the Delegation Diligence Loop. This framework teaches users to be thoughtful about which tasks to consider for AI and the responsibilities involved in owning the results.

How AI Handles Your Data

Every software tool—whether it is email, a CRM, or a spreadsheet program—has rules about data handling: how and where they store data, retention periods, and access controls. For the most part, AI tools can be evaluated similarly to regular software, with one major exception.

The Exception: Training on User Data

The primary concern unique to AI tools is whether or not they train on user data. Some terms and conditions allow AI companies to use your inputs to train their models, meaning your data could influence future outputs.

It is important to note that this rarely means someone will receive an exact copy of your uploaded data. Instead, the AI learns patterns that influence how it responds to similar questions from other users. This could potentially reveal themes or statistics from an organization's data even without reproducing exact words.

Evaluating AI Platforms and Plans

Platform Awareness teaches us that not all tools and plans are built the same.

• Some companies train on user data; others explicitly do not.
• Most delete conversations after a certain period.
• Many offer different settings and payment tiers with varying privacy protections.

This also applies to third-party tools with integrated AI features, as they may pass data back to the AI provider depending on the agreed terms of use.

Identifying Safe AI Tools

The safest AI configurations do not train on user data and have very short data retention windows, usually 30 days or less. For highly sensitive work, these protections are essential.

• Anthropic's Claude: Data policy information can be found at privacy.claude.com and trust.anthropic.com, where privacy settings can be adjusted.
• Zero Data Retention (ZDR): For maximum security, look for "immediate deletion" policies.

If a company does not provide clear resources regarding their data policy, it should be considered a red flag.

Practical Strategy: Task Delegation and Data Minimization

Problem Awareness and Task Delegation involve breaking tasks into component parts to understand what AI should handle versus what humans should handle. Often, you can gain the full benefit of AI without the risk of sharing sensitive information through advanced planning.

Scenario: Analyzing Sensitive Survey Data

Imagine a food bank conducting a client survey containing names, contact information, household income, and employment status. To analyze this safely:

1. Identify the Goal: You likely need to identify patterns across the whole client base rather than individual stories.
2. Strip Identifying Information: Recognizing that the insights do not require personal identifying information (PII) allows you to delete sensitive columns (like names or exact addresses) before uploading the data.
3. Data Minimization: You might keep only the necessary data, such as zip codes for geographic analysis, while dropping everything else.

Even when data is anonymized, it is best practice to use a model that does not train on user data.

Ensuring Diligence and Transparency

When working with AI, the diligence part of the loop is critical:

• Transparency Diligence: Determine who needs to know AI was involved in the analysis.
• Deployment Diligence: Verify AI-generated patterns against your own observations. Do not share analysis without this context.
• Ownership: If you are writing a report or grant based on these insights, you own those claims.

To better understand complex terms and conditions, you can upload the privacy policy into an AI and ask specific questions. Always ask for citations to verify the original text.

Managing Mistakes and Data Deletion

If you accidentally share sensitive information in an unsecure AI environment, take the following steps:

1. Delete the Conversation: Most platforms allow you to delete individual conversations from your history.
2. Request Data Deletion: Many providers have formal processes for this. For Claude, information is available at privacy.claude.com.
3. Follow Organizational Protocols: If your workplace has data handling guidelines, notify the appropriate person, just as you would if you sent a sensitive email to the wrong recipient.

Key Takeaways for Organizations

1. Fundamental Principles Apply: AI introduces new risks regarding model training, but basic data protection still applies. Do not share what you cannot afford to lose. Remove identifying information and match the tool's privacy protection to the data's sensitivity.
2. Different Tools, Different Rules: A free AI tool used for brainstorming is not the same as a paid enterprise account with strict retention policies. Match the tool to the task.
3. Responsible Use: Safe AI use is not about avoidance; it is about responsibility. Use problem awareness, platform awareness, and task delegation before starting a project. Responsible AI use is about knowing what to check before you start and continuing to learn along the way.