The promise of AI co-pilots augmenting our work is captivating. However, the reality of enterprise AI adoption often presents a different picture. This post explores the practical challenges and solutions for deploying secure and accurate large language model (LLM) applications in real-world business settings.

The LLM Landscape and Its Challenges

Open-access large language models are increasingly central to enterprise AI strategies. While powerful, these models introduce unique risks requiring careful consideration. We will explore these risks based on real-world experiences and offer practical mitigation strategies.

Checklist of LLM Deployment Risks

Here’s a checklist of common challenges encountered when deploying LLMs:

1. Hallucination: LLMs can generate text that sounds plausible but lacks factual accuracy. This “hallucination” poses significant risks, particularly in high-stakes scenarios like medical advice or financial decisions.
2. Supply Chain Vulnerabilities: LLMs rely on open-source code and models, creating potential entry points for malicious code. This supply chain risk demands careful management of model sources and dependencies.
3. Vulnerable Model Servers: LLMs run on servers susceptible to attacks. These servers often process sensitive data via API requests, increasing the risk of data breaches if not properly secured.
4. Data Breaches: Integrating company data into LLM prompts (e.g., for retrieval-based systems) introduces data privacy concerns. Sensitive information within prompts can leak through the LLM output, leading to unintended disclosures.
5. Prompt Injection: Malicious actors can craft prompts designed to bypass safeguards and extract sensitive information or execute harmful commands. This requires robust prompt filtering and validation mechanisms.

Addressing the Challenges

Let’s explore solutions to mitigate these risks:

1. Combating Hallucinations with Factual Consistency Checks

Grounding LLM outputs with ground truth data is essential. However, simply including source data in the prompt isn’t enough. We need a way to verify the LLM’s adherence to facts. Employing models trained to detect factual inconsistencies between two texts provides a robust solution. These models, often called “factual consistency models” (like UniEval or BartScore), analyze the LLM output against ground truth data and assign a consistency score. This quantitative assessment allows us to identify and flag potentially inaccurate responses. Alternatively, using another LLM as a judge offers a similar approach, though it might introduce latency.

2. Securing the Supply Chain

Mitigating supply chain risks requires proactive measures. Establish a trusted model registry, whether using curated models from reputable sources or maintaining a private registry. Verify model integrity with checksums or other validation methods. Stick to industry-standard libraries and avoid running untrusted code.

3. Hardening Model Servers

Standard enterprise security practices apply to LLM servers. Implement robust endpoint monitoring, file integrity monitoring, and regular penetration testing. If using third-party LLM services, inquire about their security measures and compliance certifications.

4. Preventing Data Breaches

Implement data loss prevention (DLP) techniques to safeguard sensitive information. Use specialized NLP models to detect personally identifiable information (PII) and other sensitive data within prompts. Configure these systems to block, strip, or replace PII before it reaches the LLM. Consider confidential computing techniques like Intel SGX or TDX to encrypt server memory and protect data in use. Alternatively, leverage trusted attestation to verify the server environment before sending sensitive data.

5. Defending Against Prompt Injection

Deploy a dedicated prompt injection firewall. This layer should incorporate an evolving database of known prompt injection techniques and employ classification models to identify potentially malicious prompts. Configure the system to filter or flag suspicious prompts, providing visibility into the detection process.

Additional Considerations for Advanced Applications

Advanced LLM applications like agents introduce further security considerations. “Excessive agency,” where agents have overly broad permissions, can lead to unintended consequences. Restrict agent permissions and implement dry-run mechanisms with human-in-the-loop approval for critical actions. Monitor agent activity and log relevant events for security analysis.

Conclusion

Deploying LLMs securely and accurately requires a multi-layered approach. By addressing these challenges proactively, we can unlock the true potential of LLMs while minimizing risks. This checklist and the accompanying solutions offer a starting point for navigating the complex landscape of enterprise LLM deployment. Feel free to reach out for further guidance or support in implementing these strategies.