Securing User Data in Large Language Models: Risks, Protections, and Enterprise Strategies

Virtual Gold
Sep 9
6 min read

Large language models (LLMs) have emerged as transformative AI systems, trained on immense text datasets that enable them to understand and generate human-like language across a wide array of tasks. Within this landscape, data security takes center stage, focusing on protecting the confidentiality, integrity, and proper use of the data users input into these models—ranging from personal details to proprietary business information. The concern arises because any data fed into an LLM could potentially be learned or revealed by the model, and improper handling of these inputs might expose private information or company secrets. This issue has gained prominence as LLM-powered tools like ChatGPT have spread rapidly into business and personal use, prompting serious privacy and security considerations.

Tracing the Evolution of Data Security Concerns

The roots of these concerns trace back to early research, which first raised red flags about LLMs memorizing sensitive training data. In 2021, studies by Carlini et al. demonstrated that a large generative model, such as GPT-2, could regurgitate portions of its private training data verbatim when queried creatively, extracting hundreds of snippets. These included personal identifiable information like names, emails, phone numbers, private chat logs, source code, and even unique encryption keys—often from single occurrences in the training set. This revealed that LLMs can unintentionally memorize and potentially disclose rare or sensitive data. Subsequent studies between 2022 and 2023 built on these findings, showing that larger models tend to memorize more and that data duplication in the training corpus significantly increases this risk.

As LLMs transitioned from research labs to widely deployed products, real-world incidents began to validate these early warnings. The public release of OpenAI’s ChatGPT in late 2022 marked a significant milestone, bringing a powerful LLM to scale within weeks. Users quickly discovered ways to manipulate it, as seen in February 2023 when Microsoft’s Bing Chat, powered by GPT-4, was coaxed through prompt injection to reveal its hidden system directives, including the internal code-name "Sydney." This breach demonstrated how an attacker could make the model divulge confidential initial prompts by asking the right trick question. A month later, in March 2023, OpenAI faced a more direct challenge when a bug in ChatGPT’s service allowed some users to accidentally see data from other accounts, exposing chat titles and even partial credit card details of ChatGPT Plus subscribers. OpenAI responded by taking the service offline to patch the issue and later acknowledged the gravity of the breach with an apology.

Regulatory bodies took notice as well. Around the same time, Italy’s data protection authority temporarily banned ChatGPT, citing the March 2023 leak and broader GDPR concerns about OpenAI processing personal data without a legal basis. This investigation concluded in late 2024 with a €15 million fine for GDPR violations, including inadequate transparency and the use of personal data in training. High-profile organizational mishaps further underscored the stakes. In April 2023, Samsung engineers inadvertently leaked proprietary source code and internal meeting notes by pasting them into ChatGPT while debugging code and transcribing meetings, unaware that such data might be retained on OpenAI’s servers. Samsung swiftly implemented a ban on employee use of external generative AI tools, capped prompt sizes to discourage large uploads, and issued an internal memo noting the difficulty of retrieving and deleting data once it’s on an external AI server.

In response, LLM providers began enhancing their data protection measures. By mid-2023, OpenAI announced that API customer data would not be used for training by default, with logs retained for only 30 days and options for even shorter retention periods. They also introduced a premium "ChatGPT Business" service, assuring users that prompts would not improve the model. Similarly, Anthropic emphasized optional data sharing but reversed course in 2025 for consumer tiers, requiring users to opt out if they didn’t want their chats used in training, while extending retention to up to five years.

A Taxonomy of Risks Across the LLM Lifecycle

The security risks associated with LLMs can be categorized across their lifecycle: training, inference, and operations. During the training phase, models risk memorizing sensitive data, leading to potential regurgitation through membership inference attacks—where attackers determine if specific data was part of the training set—or training data extraction attacks that recover verbatim snippets. Data poisoning poses another threat, where malicious data is inserted to manipulate model behavior, such as embedding triggers for later activation.

At the inference stage, prompt injection attacks allow adversaries to override system instructions. Direct injections use malicious prompts to elicit disallowed outputs, while indirect injections hide instructions within third-party content like web pages or images, as seen in the 2025 Anamorpher exploit where downscaled images revealed hidden prompts. Malicious tool use becomes a concern when LLMs with API or code access execute harmful actions due to manipulated inputs, exemplified by the self-replicating "Morris II" AI worm in 2024, which spread through prompt injection and data exfiltration across systems.

Operationally, vulnerabilities include service-level bugs, such as the ChatGPT Redis incident caused by a race condition, and insecure data handling practices like inadequate encryption or access controls. Supply chain risks also emerge from dependencies on external libraries or vendors, amplifying the potential for breaches.

Real-World Case Studies

Several incidents illustrate these risks in practice. The ChatGPT data breach in March 2023 highlighted the need for robust multi-tenant isolation after a nine-hour window exposed user data. Samsung’s leak in April 2023 emphasized the importance of employee guidelines following the unintended disclosure of sensitive materials. The Bing "Sydney" prompt leak in February 2023 showcased the fragility of relying on models to withhold secrets. Italy’s regulatory action against OpenAI, culminating in a 2024 fine, underscored compliance challenges. The Morris II experiment demonstrated the potential for chained exploits, while the Anamorpher exploit revealed evolving multi-modal vulnerabilities. Each case prompted providers to refine their safeguards.

Defensive Strategies and Mitigations

To counter these risks, a range of mitigation strategies has been developed. During training, data deduplication removes duplicates to reduce memorization, while differential privacy adds noise to limit the influence of individual data points, though it remains challenging for large models. At inference, robust prompt design with separators and rules, input sanitization to remove injection patterns, output filters to detect sensitive content, and sandboxing with least privilege and manual reviews for critical actions provide layered protection.

Operationally, limited retention periods, encryption, strict access controls, confidential computing with secure enclaves, user education, and regular red-teaming exercises enhance security. Providers like OpenAI, Anthropic, Google, Microsoft, and Apple have incorporated various combinations of these measures, from opt-out options to on-device processing.

Navigating the Regulatory and Future Landscape

The regulatory environment includes GDPR, which mandates a legal basis for personal data processing and has led to fines for non-compliance. The forthcoming EU AI Act will require risk assessments for foundation models, while NIST’s AI Risk Management Framework and Generative AI Profile offer guidance on privacy and security, recommending red-teaming and incident response. The OWASP Top 10 for LLMs lists vulnerabilities like prompt injection and insecure output handling, and MITRE ATLAS catalogs AI-specific attacks to aid threat modeling. Sector-specific regulations, such as HIPAA and SEC rules, further emphasize secure data practices.

Looking ahead, multimodal AI systems that process audio and video will expand attack surfaces, necessitating advanced filters. Longer context windows may increase the risk of persistent sensitive data, calling for session resets and provenance tracking to verify data origins. The rise of autonomous agents with tool access could heighten malicious use risks, pointing toward cryptographic methods and stronger regulatory frameworks as potential developments.

Integrating Model Selection for Enhanced Security

The choice between proprietary and open-source LLMs plays a crucial role in data security. Proprietary models require sending data to external servers, raising compliance and leak risks under regulations like GDPR, while open-source models enable on-premises deployment to keep data internal. In healthcare, open models support HIPAA compliance by avoiding external transmission, and in finance, they allow custom solutions like internal fraud detection systems. Legal and public sectors benefit from open models’ auditability and data sovereignty. A hybrid approach—using proprietary models for general tasks and open ones fine-tuned on confidential data—offers a balanced strategy.

This comprehensive exploration of LLM data security provides a foundation for organizations to assess their deployment strategies. How might these insights shape your approach to leveraging LLMs safely?