5 Things You Should Know When Using Online/Cloud-Based LLMs

Leveraging cloud-based Large Language Models (LLMs) can transform how we analyze, process, and generate text. However, these powerful tools come with unique risks that are often overlooked, such as data exposure, insider threats, and shared infrastructure vulnerabilities. The good news? All these issues can be avoided by using our on-premise solution Brain-Bridges KnowledgeBot, which offers full control, privacy, and security for your data while still delivering the power of LLM technology. Now, should you decide to not use us, here are five things you need to know when working with online or cloud-based LLMs.

1. Your Data May Be Temporarily Unencrypted

Why it matters: LLMs require input data to be in cleartext for processing. Even if your data is encrypted during transit and storage, it must be decrypted while being analyzed by the LLM.

Risk: This temporary unencrypted state creates a potential exposure point for sensitive data.

Mitigation:

• Avoid sharing sensitive data unless absolutely necessary.
• Preprocess data to anonymize or obfuscate sensitive details before sending it to the cloud.

2. Data Handling Practices Depend on Vendor Policies

Why it matters: Cloud providers often self-regulate their data handling policies. You’re relying on their assurance that your data won’t be logged, stored, or reused.

Risk: These policies might not be independently verified, leaving gaps for potential misuse.

Mitigation:

• Review the vendor’s terms of service and data policies carefully.
• Look for commitments such as “no data retention” or “data deleted post-processing.”
• Insist on contractual guarantees with penalties for data misuse or breaches.

3. No Model Is Fully Isolated from Insider Risks

Why it matters: Cloud-based LLMs are managed by human administrators who, despite access controls, could intentionally or accidentally access your data.

Risk: Even reputable vendors cannot completely eliminate insider threats or operational errors.

Mitigation:

• Explore services offering confidential computing or secure enclaves, where even administrators cannot access decrypted data.

4. Embedding Creation May Leak Sensitive Context

Why it matters: LLMs often generate embeddings—vectorized representations of text—for tasks like search or clustering. These embeddings can potentially be reverse-engineered to extract the original input.

Risk: Proprietary or personal information may inadvertently be exposed.

Mitigation:

• Assess the embedding mechanism to ensure it uses secure, privacy-preserving methods.
• Use open-source frameworks or thoroughly vetted vendor tools to reduce risks.
• Minimize sensitive data included in the text used to generate embeddings.

5. Shared Cloud Environments Pose Privacy Risks

Why it matters: Many cloud-based LLMs operate in shared environments where multiple customers’ data is processed on the same hardware. Misconfigurations or vulnerabilities could lead to data leakage.

Risk: Your data might inadvertently be exposed to other customers or external attackers.

Mitigation:

• Request a dedicated instance for LLM processing, if available.
• Ensure the vendor employs strong multi-tenancy isolation measures.

Final Thoughts: Balancing Power and Privacy

Using cloud-based LLMs effectively requires balancing their transformative potential with the risks of data exposure. However, for organizations prioritizing data security and privacy, on-premise solutions like Brain-Bridges KnowledgeBot provide the best of both worlds—delivering cutting-edge AI capabilities without compromising control or security.

To maximize security while leveraging LLMs:

1. Preprocess Data: Anonymize or obfuscate sensitive information before sending it to the LLM.
2. Choose Trusted Vendors: Work with reputable providers that adhere to global compliance standards, such as ISO certifications and GDPR.
3. Leverage Confidential Computing: Opt for services that support secure enclaves or similar privacy-preserving technologies.
4. Consider On-Premise or Hybrid Models: For maximum security and control, deploy an LLM within your private infrastructure using solutions like Brain-Bridges KnowledgeBot.

By staying informed about these challenges and implementing proper safeguards, you can confidently integrate LLMs into your workflows without compromising data security. If some of these ‘policies’ and other organizational guard-rails do not sound like they are working for you and your company, let’s have a chat and talk about how Brain-Bridges can help you leverage AI/LLMs in a safe and secure way.