Three Key Steps to Securing Sensitive Data Before Using Tools like DeepSeek & ChatGPT

Large Language Models (LLMs) like DeepSeek and ChatGPT are transforming how organisations operate, unlocking new efficiencies, automating workflows, and enhancing decision-making. However, these AI-powered tools also introduce major security risks. Without the right data security controls in place, businesses risk exposing sensitive data, violating compliance regulations, and even opening the door to cyber threats.

In fact, AI models like DeepSeek have the potential to exploit and expose vulnerabilities at scale, increasing the likelihood of data leaks and unauthorised access. Data shared with an LLM—intentionally or not—can be retained, accessed by unintended parties, or even contribute to future model training, raising serious privacy concerns. The problem is exacerbated when organisations lack visibility into their SaaS and cloud environments, leaving sensitive data unprotected and susceptible to breaches.

To safely leverage the power of LLMs, organisations must first implement robust data protection measures. The key? Knowing where sensitive data is, who has access, and how it's used. Here’s how:

1. Identify Where Your Sensitive Data Lives

In modern business data is often scattered across SaaS apps and cloud platforms. Without visibility into the data you have, you can’t secure it. We found in a recent study that 65% of organisations do not have full visibility into where sensitive data is stored across their SaaS ecosystem. By using automated DLP tools, organisations can discover and classify sensitive data at scale, ensuring it isn’t unintentionally accessed or exposed.

2. Tighten Access Controls

Too many organisations operate with lax access policies. According to Metomic research, 78% of security teams find that employees have access to sensitive data they do not need for their roles. Enforce strict role-based controls (RBAC) and the principle of least privilege (PoLP) to prevent unauthorised data access. Regularly audit permissions to detect and fix security gaps.

3. Monitor and Protect in Real Time

AI threats evolve rapidly. A recent analysis that we performed on enterprise SaaS environments showed that 42% of security incidents are linked to sensitive data being unintentionally exposed or overshared. The use of LLM's can give rise to both accidental and malicious insider threats and, while they may be powerful productivity tools, they leave organisations open and exposed to data leaks that can have dire consequences. That’s why it’s vital to deploy real-time monitoring to spot unusual activity, block unauthorised access, and prevent sensitive data from being fed into LLMs. Proactive detection and automated policy enforcement are critical.

Data Security: The Foundation for Safe AI

Using LLMs safely starts with securing sensitive data. By knowing where it is, controlling access, and monitoring usage, organisations can prevent AI-driven security threats. At Metomic, we help businesses safeguard SaaS data, ensuring resilience in an evolving threat landscape. If you want to learn more about how we approach data security and how we could help your organisation, drop me a note.