Business data analytics can be extremely useful for decision makers, and as AI’s integration into the workplace becomes more commonplace, many teams are seeing value in asking LLMs for assistance with distilling actionable insights from proprietary datasets. While this practice should certainly be encouraged, it does need to be handled carefully in order to preserve data security and privacy.
According to the Immuta AI Security and Governance report, 80% of data experts agree that AI is making data security more challenging. Additionally, 88% of data professionals say employees at their organizations are using AI, but only 50% say their organization’s data security strategy is keeping up with the AI’s rate of evolution.
The main problem is that people often send data to LLMs as part of their ongoing workflows, without taking security into consideration. Many organizations even lack basic monitoring or visibility into what AI is being used, in a phenomenon known as “shadow AI.”
The top concern regarding AI for 56% of data professionals is the risk of sensitive data exposure via an AI prompt. Two elements need to be in place for a prompt leak to happen: user input (sensitive data uploaded by employees) and model output (when the LLM generates or reveals confidential information to someone else, based on prior interactions or training data).
These leaks are more common than you might assume, as shown by the DeepSeek incident from January 2025, whereby millions of lines of chat logs, API keys, and other sensitive information were exposed, affecting many organizations. A similar incident happened with ChatGPT earlier on in its development.
Here are five ways organizations can prevent data leaks when using LLMs.
1. Avoid giving AI direct access to the data
LLMs should never connect directly to production databases or sensitive systems. This will protect you from situations like the DeepSeek leak. Even if there is a leak, no sensitive data can be exposed if the AI has never gained access to the raw data. A great way to approach this is to build a layer that obscures the data before queries are passed on to the LLM.
Pyramid Analytics is a decision intelligence solution that separates the AI layer from the actual data without compromising the quality of the outputs. The way it works is, when a user asks Pyramid’s AI chatbot for BI a question, the engine then sends a high-level version of the question to the external AI model of the user’s choice, along with a description of the data at hand.
Pyramid then runs the query within your own environment and returns results in the form of interactive dashboards, charts, or reports. The LLM never directly interacts with the organization’s data, yet you still get the full benefits of AI-powered insight.
2. Implement strong access controls
Access to LLMs should never be a free-for-all, especially in larger organizations. There needs to be a clear set of policies that define who can use LLMs, in which contexts, and under what conditions. Access should be granted based on employee roles, implemented with appropriate restrictions for both data and model access.
Role-based access controls (RBAC) directly support enforcing the principle of least privilege, an essential component of a modern security program. This principle ensures that users, models, and connected tools only have the minimum access and capabilities needed to perform their tasks.
Mature AI organizations may even use an MCP (Model Context Protocol) server, which allows teams to control how LLMs interact with external resources by translating AI queries into actions like API calls or database lookups. Because MCP servers regularly interact with sensitive systems and data, they must be configured with zero trust principles. This means validating every request and logging all activity.
3. Rethink prompt engineering
Prompts are how every user can interact with an LLM. If not designed with security in mind, prompts become a serious vulnerability. AI systems must be able to differentiate a legitimate prompt from a harmful one. There are two steps to achieve that.
The first step is to implement validation rules for all incoming prompts, which check for suspicious patterns, including embedded commands (the types hackers might use in SQL injections, for example) or attempts to override system instructions. Input validation and sanitization are standard practice in web security, and must now be applied to AI systems in the same way.
For even more protection, organizations can deploy a tool like LLM Guard, whose dedicated injection scanner analyzes prompts in real time and can catch advanced manipulation attempts that rule-based filters might miss.
4. Log and monitor AI output and usage
LLMs should be treated like any other technology in the business. Many organizations monitor laptops and application use, and AI models need just as much oversight.
Track what’s being asked, what responses are being generated, and who is interacting with the model. This will bring up any inappropriate use or policy violations, but also any issues with the model that may lead to data leakage.
The goal with these measures isn’t surveillance, but to ensure the LLM operates and is being used securely, ethically, and as intended. After all, it is a business system that touches sensitive data, so it must be treated like one.
5. Train employees on LLM risks
Even with the most restrictive security controls in place, employees can still pose a risk. They may share overly sensitive data with the model, rely on unapproved AI tools, or take everything the LLM generates as fact.
To address this problem, awareness training platforms like Ninjio now offer AI-specific modules that train and educate employees on various best practices and risks associated with LLM use. Employees will learn to avoid sharing sensitive info or use unvetted AI tools, and evaluate AI-generated outputs before acting on them.
Whether through an external provider or an in-house initiative, security training around AI is a must for every organization that plans to implement LLMs in a meaningful way.
Final thoughts
LLMs are a powerful but relatively immature technology. Despite efforts to standardize security practices around their use and development, there is still a lot of risk for organizations, which may result in data leaks.
For that reason, security should be built into every layer of an LLM implementation. The measures discussed in this article provide a solid foundation for integrating LLMs safely and responsibly.
