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Protecting client data when sharing it with any external vendor—AI or otherwise—requires a strong understanding of data confidentiality best practices. Although handling data for AI companies follows the same core principles as with any vendor, some unique aspects of AI processing add new challenges. If you’re not intimately familiar with these practices, you shouldn’t be sharing client data at all
1. Rigorous Data Justification: Avoiding the “Buffet Mentality”
One of the most common issues in data sharing is what I like to call the “buffet mentality.” This happens when data is requested broadly without a clear, specific purpose—often with vague justifications like “we might need it” or “just to be safe.” This approach might seem thorough, but it’s sloppy data governance and can expose sensitive information unnecessarily.
As a data custodian, your job is to push back on such requests. Work with the requestors to define exactly what data points are necessary to solve the problem at hand, and why they are needed. If the requestor can’t clearly articulate the purpose, the data shouldn’t be shared. Sure, there are legitimate scenarios where broader datasets might be required, such as for pattern recognition or investigative analysis, but even then, there need to be well-defined parameters in place. This ensures the data is used efficiently and stays protected.
Being precise about data requirements does more than just protect client confidentiality; it also streamlines the data-sharing process. When you focus on sharing only what’s essential, the solutions derived from the data are often more relevant and actionable.
“Work with the requestors to define exactly what data points are necessary to solve the problem at hand, and why they are needed.”
2. Sophisticated Anonymization: Go Beyond the Basics
With AI vendors, anonymization has to go beyond simply stripping out obvious identifiers like names, addresses, or email addresses. The capabilities of AI systems can expose deeper risks if data isn’t properly anonymized. The goal is to prevent the identification of individual clients not just from direct identifiers, but also from indirect data points and complex patterns.
Key Anonymization Considerations:
- Complex Identification Patterns: AI systems can identify individuals through seemingly unrelated data points by triangulating multiple pieces of information. Data that appears harmless or benign to humans, such as metadata or timestamps, could become clear identifiers to AI algorithms. For example, specific patterns in usage data or log timestamps might be enough to uniquely identify a user.
- Public Record Cross-Reference: AI systems can process vast amounts of public data almost instantaneously. Even if you think your dataset is anonymized, it can still be cross-referenced with public records for potential re-identification. For instance, anonymized location data could be matched with publicly available demographic information to narrow down identities.
- Behavioral Fingerprints: Many data types can create unique behavioral profiles. For example, transaction histories, user interactions, and other engagement logs can reveal distinct patterns that, when combined, form a kind of digital “fingerprint.” Even without traditional identifiers, this data can be used to identify individuals based on their behavior.
To address these risks, you need a deep understanding of both your data structure and AI capabilities. You may need to go as far as aggregating data, using noise injection techniques, or employing advanced de-identification strategies. It’s not just about masking a few fields; it’s about making sure the dataset can’t be reversed into revealing identities through AI’s pattern recognition power.
3. Robust Encryption: Making Data Useless to Intruders
Encryption is essential, not just as a technical requirement, but as a fundamental layer of data protection. When sharing data with AI vendors (or any vendors, for that matter), ensure that it’s encrypted during transfer and while at rest. But it’s not enough to use just any encryption; you need to use standards that can withstand potential future threats.
- Use Strong Encryption Protocols: Aim for encryption algorithms like AES-256, which are considered to be very secure. If a breach occurs, properly encrypted data will be virtually useless to an intruder because decrypting it would take decades with current computing capabilities.
- Secure Transfer Channels: Data transfer should only occur over secure channels such as SFTP, TLS, or other encrypted protocols. Sending sensitive data over unsecured channels like email is an unacceptable risk.
Remember that encryption isn’t a “set it and forget it” measure. Periodically review and update encryption methods to ensure they meet current best practices and adapt to new threats as they arise. This helps protect against the possibility of future computational advancements, such as quantum computing, which could potentially break today’s encryption standards.
“If a breach occurs, properly encrypted data will be virtually useless to an intruder because decrypting it would take decades with current computing capabilities.”
4. Vendor Due Diligence: Don’t Just Trust—Verify
Before sharing any data, it’s crucial to perform thorough due diligence on the AI vendor’s data protection measures. Don’t just take their word for it—dig into their policies, certifications, and track record. Here are some aspects to evaluate:
- Security Practices: Understand what specific security measures the vendor employs to protect data. Are they using the latest encryption techniques? Do they conduct regular security audits?
- Regulatory Compliance: Verify that the vendor is compliant with relevant regulations, such as GDPR, CCPA, or other data protection laws that apply to your industry.
- Contractual Obligations: Include strict data protection clauses in contracts, requiring the vendor to adhere to your data handling standards. Make sure there are clear stipulations on data usage, storage, and disposal, along with penalties for non-compliance.
Even with contractual protections in place, ongoing monitoring and regular audits can help ensure that the vendor continues to meet your security requirements.
5. Monitoring and Incident Response: Always Be Prepared
After data has been shared, your responsibility doesn’t end there. You need to have systems in place to monitor how the data is being used. Implement measures that track usage to ensure the data is only used for the agreed purposes and not for unauthorized activities.
Additionally, plan for worst-case scenarios by having a robust incident response plan. This plan should outline how to detect, contain, and mitigate a breach. Be ready to notify clients and authorities as required by law, and take steps to minimize any potential damage.
AI Adds Nuance, Not New Rules
It’s important to recognize that while AI presents unique challenges, the fundamental principles for protecting data remain the same. The inclusion of “AI” in the question might make it seem like there’s a different standard, but in reality, it’s about understanding how AI’s capabilities change the context of data protection.
AI systems can reveal patterns and correlations that were previously hidden, making anonymization and data protection even more critical. But with the right approach—rigorous data justification, sophisticated anonymization, robust encryption, thorough vendor vetting, and proactive monitoring—you can safeguard client data effectively, regardless of who you’re sharing it with.
The takeaway is simple: data confidentiality best practices don’t change just because you’re working with an AI company. It’s about doing the basics well and adapting those practices to account for AI’s unique abilities. By staying vigilant and ensuring you have strong safeguards in place, you can protect your clients’ data while still leveraging the power of AI.
I’d love to hear your thoughts on this! Have you ever faced challenges when sharing data with AI vendors, or do you have a unique approach to ensuring data confidentiality? Drop a comment below and share your experiences or questions about data protection. Let’s start a conversation about how we can continue to adapt our best practices to keep up with evolving technology!
“…data confidentiality best practices don’t change just because you’re working with an AI company. It’s about doing the basics well and adapting those practices to account for AI’s unique abilities.”
Note: AI tools supported the brainstorming, drafting, and refinement of this article.
A seasoned IT professional with 20+ years of experience, including 15 years in financial services at Credit Suisse and Wells Fargo, and 5 years in startups. He specializes in Business Analysis, Solution Architecture, and ITSM, with 9+ years of ServiceNow expertise and strong UX/UI design skills. Passionate about AI, he is also a certified Azure AI Engineer Associate.
