When most of us use Public AI services, like ChatGPT and Gemini, we quickly find ourselves confiding in these services, sharing details and personal information. The personal manner in which these services interact with us fosters the illusion of anonymity and confidentiality.
But Public AI services have a deep dark secret. They are not secure.
The queries and the data you provide when you use these services are used to train future versions of these LLM’s, which exposes your confidential information to prying eyes.
While these services may offer the assurance that your data is anonymized before its incorporated into their models, the process of anonymizing data is often ineffective. A skilled user can query the LLM in the future and tease out important details that could expose you to liability.
The implications of this lack of security are particularly serious when you’re using Public AI services for your business.
The Illusion of Anonymity
At first glance, removing personally identifiable information (PII) from your data before it’s used in an AI query may seem like a robust solution to privacy concerns. However, this approach provides a false sense of security.
Advancements in data analytics and the proliferation of publicly available information have made it increasingly easy to re-identify anonymized data. By cross-referencing anonymized datasets with other data sources, malicious actors can piece together the puzzle to reveal the identities of individuals or uncover sensitive business information. Studies have shown that even with names and direct identifiers removed, individuals can be re-identified with high accuracy using as few as three data points, such as zip code, gender, and date of birth.
Not all anonymization methods are created equal.
Techniques like data masking, aggregation, or pseudonymization may reduce the risk of identification but do not eliminate it entirely. These methods often leave patterns or indirect identifiers intact, which can be exploited.
For example, aggregated sales data might hide individual transactions but still reveal strategic business trends if analyzed over time. This residual information can be invaluable to competitors or malicious entities seeking to gain an advantage.
While data anonymization can help with regulatory compliance under laws like GDPR, CCPA, or HIPAA, meeting legal requirements doesn’t necessarily ensure data security. Regulators acknowledge that anonymized data can sometimes be re-identified and emphasize the importance of robust data protection measures beyond mere anonymization.
Relying solely on anonymization can leave your organization vulnerable to data breaches, legal penalties, and reputational damage.
The High Stakes of Data Exposure
The ramifications of critical information falling into the wrong hands are severe. Confidential business strategies, proprietary technologies, and sensitive customer data are valuable assets that, if exposed, can lead to:
- Competitive Disadvantage: Competitors could exploit your proprietary information to undermine your market position.
- Financial Losses: Data breaches can result in hefty fines, lawsuits, and loss of revenue.
- Reputational Damage: Erosion of customer trust can have long-term impacts on brand loyalty and public perception.
Self-Hosting AI
The solution to all of these problems is to bring the LLM in-house and self-host your AI.
In simple terms, self-hosting means that you run an LLM and your AI applications on servers your organization controls, rather than relying on external, Public AI services. This approach is increasingly important for several reasons:
- Data Security: By keeping data in-house, you minimize the risk of data breaches and unauthorized access.
- Regulatory Compliance: Industries governed by strict data protection laws—such as GDPR in Europe, CCPA in California, and HIPAA for healthcare—require tight control over how data is stored and processed.
- Control and Customization: Self-hosting allows for greater customization to meet specific business needs and integrates seamlessly with existing systems.
The Achilles Heel of Self-Hosted AI Models
While self-hosting addresses security and compliance concerns, it introduces a new challenge – keeping AI models up-to-date.
An AI model learns patterns and makes predictions based on the data used to train the LLM. When you use an open source LLM, such a Llama, the model is usually a year old by the time it’s released to the world.
In other words, any LLM that you self-host will be a year out of date. The implications of this are relatively obvious:
- Limited Knowledge: The AI won’t be aware of events, developments, or data that emerged after its last update.
- Reduced Accuracy: Predictions and insights may become less relevant over time, affecting decision-making.
- Competitive Disadvantage: Without current information, businesses may miss out on opportunities or fail to respond to emerging threats.
For example, a financial AI model trained on year old data wouldn’t account for market fluctuations, regulatory changes, or economic events that have occurred more recently.
Bridging the Gap with Real-Time Data Integration
To overcome the limitation of outdated open source models when you self-host an LLM, you need a strategy for integrating real-time, industry-specific data into your implementation.
What Is Real-Time Data Integration?
Real-time data integration involves continuously updating your AI model with the latest information gathered from various sources, such as websites, databases, industry reports and your own corporate data. This ensures your AI remains current and effective.
How It Works
- Data Collection: Automated tools, like web crawlers, continuously scrape relevant information from selected sources.
- Data Processing: Data is collected from your files, cleaned and formatted to be usable by your AI models.
- Model Updating: The AI models are updated with the new data, ensuring their outputs reflect the most recent information.
Industry-Specific Examples
For example, imagine a finance company conducting analysis of its customer’s portfolios. A company with this kind of data is mandated to keep customer data private. But it also doesn’t want to divulge its methods and strategies to a Public AI.
In order to use a Self-Hosted AI effectively, the company would require real-time stock prices, the latest news regarding economic indicators and news relevant to the stock and the industry serviced by the stock. It would also need access to the client’s portfolio, before it could produce a useful analysis.
Real-time data can take many forms. In some cases, the information you need for an effective analysis is hard to predict. Real world events can sometimes conspire to become relevant to a subject in unexpected ways.
For example, a potential real estate investment might be impacted by events happening in the community or by macroeconomic factors.
Implementing Real-Time Data in Self-Hosted AI
Implementing real-time data in a Self-Hosted environment is a complex process. It’s wise to engage experts who specialize in AI and data integration to streamline implementation. These are some of the steps the experts will take:
Step 1: Identify Data Sources
- Internal Data: Company databases, CRM systems, and transaction records.
- External Data: Industry websites, government databases, news outlets, etc.
Step 2: Set Up Data Collection Tools
- Web Scraping: Implement automated tools to collect data from websites. This can often be very complex, because aggressive web crawling and scraping requires a lot of infrastructure.
- APIs: Leverage Application Programming Interfaces and webhooks. Work closely with the client’s IT department to gather the needed data in an efficient manner.
Step 3: Ensure Data Compliance
- Legal Considerations: Obtain necessary permissions and comply with terms of service.
- Privacy Regulations: Ensure data collection methods comply with GDPR, CCPA, HIPAA, etc.
Step 4: Integrate with AI Models
- Data Processing: Clean and format data for compatibility with AI models.
- Model Updating: Establish protocols for regular updates to the AI models.
Step 5: Monitor and Optimize
- Performance Tracking: Measure the effectiveness of the AI outputs.
- Continuous Improvement: Refine data sources and update models to improve accuracy.
Whenever real-time data is integrated with Self-Hosted AI, decisions need to be made about the risk/benefit of using the information. And protocols need to be implemented to ensure that abuses don’t occur. You need to consider the ROI of enhancing the system’s capabilities, as well as the system’s accuracy and reliability.
Maximizing the Value of Self-Hosted AI
By understanding the necessity of self-hosting for security and compliance, and addressing the challenge of outdated models through real-time data integration, businesses can fully leverage AI’s potential.
Key Takeaways:
- Security and Compliance: Self-hosted AI protects your data and meets regulatory requirements.
- Current and Relevant: Integrating real-time data keeps your AI models accurate and effective.
- Strategic Advantage: Enhanced AI capabilities drive better decision-making and maintain your competitive edge.
Embracing this approach enables organizations to innovate securely and confidently in an ever-changing business landscape.
Next Steps: Empower Your Business with Verlicity AI
At Verlicity, we specialize in helping businesses integrate real-time, industry-specific data into Self-Hosted AI systems. Our team of experts can guide you through the process, ensuring that your AI initiatives are both secure and cutting-edge.
Contact us today to schedule a consultation and discover how we can tailor solutions to meet your unique needs.