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Secure LLM Applications for Enterprises
Derisking GenAI adoption and building high-performance LLM apps that don’t compromise security
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The Apps of the Future are LLM-Powered
The business benefits of developing LLM applications, or adding LLM capabilities to existing products, are by now well known - and too attractive for enterprises to ignore. Microsoft’s conversational AI principal PM estimates that half of digital work will soon be automated with LLM technology, and that around 750 million new apps will need to be built by 2025.
Those apps are being built. GenAI adoption has accelerated as the barriers to developing internal bots have lowered. According to TCS research, over half of CEOs plan to build their own LLM apps. These in-house GenAI implementations offer a powerful competitive edge, and a way to accelerate innovation. Developing LLM apps independently also means that the enterprise retains full control over customization and security protocols.
But developing and deploying in-house LLM applications isn't without challenges. These models require significant investment in data infrastructure, skilled teams, and ongoing maintenance. They also introduce new security and compliance risks, especially for organizations handling sensitive data or deploying models across large, complex environments.
The time to adopt LLM technology is now: but enterprises need to go in with their eyes wide open.
Staking a Claim in the GenAI Goldrush
OpenAI
Meta’s LLaMA
Google’s Gemini
Anthropic
How Leading Companies are Successfully Harnessing LLMs
Successfull LMM Deployment | Benefits |
|---|---|
 Microsoft (Office 365) | Integrated OpenAI’s GPT-4 into Microsoft 365 apps (e.g., Word, Excel) as "Copilot" to assist with drafting documents, automating data analysis, and summarizing content. |
 Canva | Implemented GPT-powered features like Magic Write to help users generate text for presentations, marketing materials, and social media posts automatically. |
 Salesforce (Einstein GPT) | Integrated GPT into its CRM platform for generating sales emails, customer service responses, and automating workflows, improving the productivity of sales and support teams. |
 Grammarly | Uses LLMs to provide writing assistance, improving grammar, tone, and style. It also offers suggestions for clarity and engagement in real-time across apps. |
 Shopify (Shopify Magic) | Uses GPT-4 to assist merchants in generating product descriptions, marketing copy, and email content to streamline e-commerce operations and improve sales copy. |
 Zoom | Integrated GPT-4 into its platform to generate meeting summaries, help with live transcription, and create action item lists from discussions, improving meeting productivity. |
6 Powerful Benefits of LLM Applications for Enterprise
Increased Productivity
Boost efficiency by automating tasks and enhancing decision-making, driving productivity gains.
Enhanced Customer Experience
LLM apps can offer personalized, efficient customer service through 24/7 chatbot support, reducing wait times.
Cost Savings
Automating repetitive tasks reduces operational costs, allowing resources to be focused on core business areas.
Innovation & Competitive Edge
Product development and market analysis can be accelerated dramatically through LLM integration.
Improved Accuracy & Efficiency
An LLM app can analyze large datasets quickly and accurately, enhancing insights for better decisions.
Scalability & Adaptability
Apps enhanced by AI can scale easily across functions, adapting to new use cases as business needs evolve.
Keeping LLM-Based Apps Secure: Critical Risks to Consider
For any app that processes natural language, integrating LLM technology opens the door to unprecedented improvements in efficiency and output. These benefits are too good for enterprise to pass up on, but they need to proceed with a complete understanding of the risks involved.
When data leaks, privacy violations, or other attacks occur due to vulnerabilities in GenAI chatbots, organizations face unprecedented risks of falling victim to malicious threat actors or legal liability.
Best Practices for Securing LLM Applications
Restrict Plugin and API Access
Only allow explicitly necessary external plugin or API calls. Failure to restrict their access increases the attack surface of an LLM app, so every plugin integration needs strict authentication and authorization protocols. For example, in the case of multiple plugins in series, one plugin’s output should not become another plugin’s input without explicit permission.
Sanitize and Validate Inputs
Without proper Input sanitization, prompt injections can enter the model, manipulate inputs and alter LLM outputs. Strict validation techniques are necessary to protect the integrity of the application from compromised incoming data.
Treat LLM Outputs as Untrustworthy
LLMs can generate unpredictable output. This is part of their appeal and their potential, but it also means that outputs should be handled with caution, especially in high-stakes environments. Always verify and filter LLM-generated data to avoid the pitfall of overreliance.
Rate-Limit Queries
Set a threshold for query frequency to prevent abuse. Rate limiting protects against denial-of-service attacks and can control cost and resource consumption by limiting user input per time period.
Use Retrieval-Augmented Generation (RAG)
RAG enhances LLM responses by combining them with real-time data retrieval systems. A RAG architecture can help to keep LLM responses both relevant and accurate.
Log Everything
Implement comprehensive logging for both input queries and output responses to track unusual behavior. This allows for better monitoring, detection of suspicious activity, and easier debugging in case of system breaches.
Secure Your App Portfolio With Lasso Security
Lasso Security empowers enterprises to integrate LLM capabilities securely, whether through Gateway, API, or SDK.
With custom guardrails, Lasso allows the creation of contextual, app-specific security policies, protecting users and data from harmful content and ensuring safe AI usage.
The solution offers advanced access management, with context-based controls (CBAC) to mitigate oversharing risks, with full audit trails for ongoing compliance and investigation.
Lasso also provides proactive detection, response, and remediation workflows, with minimal latency and seamless integration into existing security infrastructures—all managed from a single, unified platform.
Build It Right, From the Ground Up
The LLM arms race is on. Enterprises are under increasing pressure to build in-house LLM apps, and enhance their products with cutting-edge GenAI capabilities.
But they need to do it with a security-first mindset. Lasso Security for LLM Applications provides the peace of mind (and always-on security) that enterprises need to forge ahead without jeopardizing their data.
Learn how Lasso can help you tame LLM risks and build with confidence.