When addressing the challenge of sensitive data exposure with generative AI, businesses typically adopt one of two approaches: either control AI usage with an AI firewall or eliminate the risk by deploying a Private/ On-premises AI solution. As companies increasingly leverage tools like ChatGPT and Google's Gemini to drive productivity and innovation, the importance of protecting sensitive information cannot be overstated. A recent study by Harmonic Security reveals that approximately 8.5% of prompts entered into these AI models by enterprise employees contain sensitive data, predominantly customer and employee information.
This blog will explore how sensitive data can be inadvertently exposed when using AI and delve into how these two strategies—AI firewall and Private AI deployment—can safeguard your business from potential data breaches.
How Does Sensitive Data Exposure Occur?
Generative AI models are designed to process and generate human-like text based on the input they receive. When employees input sensitive information—such as personal identifiers, confidential business details, or proprietary data—into these models, there's a risk that this data could be stored, processed, or even inadvertently shared. This exposure can occur through:
- Data Retention: AI models may retain input data to improve future responses, leading to potential unauthorized access.
- Model Training: Sensitive data used during training can be embedded within the model, risking unintentional disclosure.
- Third-Party Access: Utilizing external AI services may involve sharing data with third-party providers, increasing the risk of data breaches.
A notable example is the case of Stability AI, the company behind the AI art generator Stable Diffusion. In 2023, Stability AI faced a class-action lawsuit alleging that it had trained its AI model on billions of copyrighted images without the consent of the artists. This incident underscores the risks associated with handling sensitive and proprietary data in AI applications.
Implications of Sensitive Data Being Exposed
The inadvertent exposure of sensitive data through generative AI can have several repercussions:
- Regulatory Non-Compliance: Violations of data protection regulations like GDPR or HIPAA can result in substantial fines and legal consequences.
- Reputational Damage: Loss of customer trust due to data breaches can harm a company's reputation and lead to customer attrition.
- Intellectual Property Risks: Exposure of proprietary information can erode competitive advantages and result in financial losses.
For instance, in 2017, DeepMind, an AI subsidiary of Google, faced scrutiny when it was found that the Royal Free NHS Trust had unlawfully shared patient data with the company without adequately informing patients. This incident underscored the importance of transparency and consent in AI-driven healthcare applications.
Mitigating Risks with BusinessGPT's Private AI and AI Firewall Solutions
To address these challenges, organizations can implement robust solutions like BusinessGPT's Private AI and AI Firewall:
AI Firewall:
This tool provides comprehensive governance and security by:
- Rule-Based Enforcement: Implementing predefined rules aligned with company policies to mitigate AI-related risks.
- Data Protection: Preventing the exposure of sensitive data through classification and input validation.
- Compliance Assurance: Ensuring adherence to regulations such as the EU AI Act and NIST AI RMF.
- Risk Management: Identifying and mitigating potential risks associated with AI misuse and data leakage.
- OWASP LLM Top 10 Mitigation: Addressing threats like prompt injection and insecure output handling.
Private AI Solution:
For organizations requiring stringent data privacy, this solution offers:
- Secure Deployment: On-premises or private cloud deployment ensures sensitive data remains within the company's infrastructure.
- Data Synchronization and Control: Providing answers based on users' existing access permissions, ensuring data is only accessible to authorized personnel.
- Comprehensive Data Integration: Extracting information from various company data sources, including emails, chats, CRM systems, and knowledge bases.
- Real-Time Information Retrieval: Delivering instant, contextual, and accurate answers to business queries based on internal data.
For more information on securing your organization's AI usage, book a meeting with our experts today to discover the best approach tailored to your needs. Let’s work together to protect your data and empower your team with safe, innovative AI solutions.
