As companies increasingly seek AI solutions, the question of how they handle data privacy and security has become paramount.

Cognistx Data Scientist Justin Waltrip explains that the advent of language models has brought data privacy to the forefront. These models thrive on vast amounts of data, making the concept of "big data" more relevant than ever. Companies now have strong incentives to track and store user data indefinitely. The problem is some companies use that data to train or fine-tune language models for specific use cases without the customer's consent.

An essential point for businesses to understand is how AI companies use their client's data. While some companies may use that data to train models for other clients, others, like Cognistx, explicitly do not.

Businesses should ask about data privacy and understand their AI provider's policies. Some companies, for example, offer on-premises versions of their AI tools, allowing clients to host persistent data on their own systems for an additional level of control and visibility. This can be particularly important for companies with strict compliance requirements.

With AI providers hungry for data, businesses need to raise concerns about customer privacy and the protection of company secrets.

Waltrip suggests several measures to address these concerns:

• Removing personally identifiable information (PII) before using AI services, especially in sensitive domains like healthcare.
• Utilizing synthetic data that mimics real data but doesn't compromise privacy.

AI and Cybersecurity

While AI can enhance digital defenses, it also creates new vulnerabilities. Waltrip notes that AI lowers the barrier of entry for potential hackers, making it easier for less technical individuals to create malware or exploits. On the flip side, AI can be used to automate security measures and more effectively detect and stop intrusions.

When it comes to implementing data privacy measures, Waltrip advises following well-established best practices rather than unique methods such as these:

• Data isolation  
• Using local models when possible  
• Only using third-party providers with explicit client consent  
• Implementing multi-factor authentication  
• Following the principle of least privileged access  
• Encrypting data at rest

Cognistx prioritizes data privacy. For instance, its enterprise question-answering tool, SQUARE, and the AI website assistant, SQUARY, use isolated infrastructure in a virtual private cloud. Cognistx also employs a multi-tenant architecture to ensure client data is siloed.

Waltrip emphasizes the importance of empowering employees to be aware of and report potential security risks, not just technical staff. Regular meetings to discuss security concerns are crucial in maintaining a secure environment.

As AI continues to advance, the conversation around data privacy will remain crucial. Businesses must stay informed and vigilant, asking the right questions and implementing robust security measures to protect their data in this new AI-driven world.

Interested in learning more about AI and data privacy? Listen to our podcast AI-Driven.

Interested in learning about Cognistx and its products? Schedule a meeting with our team.