Protecting Sensitive Data When Using AI Chatbots: A Technical Guide for Security Professionals

AI chatbots like ChatGPT have become indispensable tools for security professionals, aiding in tasks from code analysis to threat report summarization. However, these tools pose a significant data privacy risk, as conversations are typically saved and user data is used to train AI models¹. For security teams handling sensitive information—such as proprietary code, internal infrastructure details, or threat intelligence—the implications of this data collection are particularly severe. A method exists to use these chatbots with stronger privacy controls, mitigating the risk of exposing confidential organizational data.

The primary risk stems from how AI providers manage user data. Chatbots collect two types of data: automatically collected information like IP addresses and device details, and, more critically, all user-provided content from prompts and uploaded files⁹. This user-provided data is routinely used to train and improve AI models. Consequently, sensitive or proprietary information entered into a prompt could potentially be incorporated into the model’s knowledge and reflected in responses to other users, constituting a serious data leakage vector.

Standard Privacy Controls and Their Limitations

Most platforms offer basic in-app settings to limit data usage. For ChatGPT users with an account, the standard method is to navigate to Settings > Data Controls and disable the “Improve the model for everyone” option⁵. A crucial limitation of this setting is that it is coupled with chat history; disabling model training also disables the saving of conversation history. Furthermore, this setting does not sync across devices, meaning it must be manually configured on each installation, such as the web interface and mobile app separately. Similar settings exist on other platforms: Perplexity AI allows users to turn off “AI data retention” in preferences, and Google Gemini users can manage data saving in the Activity controls.

It is vital to understand that “opting out” only prevents specific prompts from being used for *future* training runs. The models have already been trained on vast datasets scraped from the public internet, which may include historical public data from employees⁶. For highly sensitive discussions, OpenAI provides a “Temporary Chat” or incognito mode. This feature does not save conversations to history or use them for training, with chats being purged from OpenAI’s systems after 30 days, retaining them only for abuse monitoring. A significant operational limitation is that Temporary Chat does not work with features that require memory or connection to other apps, such as Custom GPTs with Actions.

A Robust Method for Opting Out of Training

Beyond the standard in-app toggle, a more effective and comprehensive method for opting out has been identified through user experimentation¹⁰. Submitting a formal “Privacy Request” at privacy.openai.com/policies appears to function as a global account-level setting. According to user reports, this method successfully opts an account out of training across all devices while allowing the retention of chat history and all other features. This bypasses the significant drawbacks of the in-app toggle, which disables history and is device-specific. This method provides a more robust privacy guarantee for organizational use.

For security-conscious organizations, the most secure option is to utilize enterprise-grade solutions. ChatGPT Teams, Enterprise, and Education plans offer enhanced data controls and policies that ensure user data is not used for training by default⁵. These plans are designed for environments where data confidentiality is paramount. Additionally, third-party Data Security Posture Management (DSPM) and Data Loss Prevention (DLP) solutions can provide organizational-level control, preventing sensitive data from being entered into chatbots in the first place, regardless of individual user settings or human error⁹.

Critical Data to Exclude from AI Interactions

Regardless of settings, the fundamental rule for security professionals is to never share sensitive information with a public AI model. All input should be treated as if it could be seen by human reviewers or potentially compromised in a data breach. The categories of data to strictly avoid include Personally Identifiable Information (PII) such as government IDs and phone numbers, financial information like credit card numbers, and any login credentials or passwords⁸. Furthermore, private or confidential business information, unpublished research, and proprietary intellectual property like source code or trade secrets must never be submitted to these systems.

The underlying risks justifying this caution are substantial. Conversations may be reviewed by humans for policy compliance and safety, even if a user has opted out of training¹. AI services are high-value targets for hackers; a 2023 incident saw over 100,000 OpenAI account credentials stolen and sold on the dark web, highlighting that no online service can guarantee absolute security. The opaque and often complex nature of these privacy settings can itself be a risk, potentially leading to a false sense of security among users who believe they are fully protected when they may not be.

Relevance and Strategic Recommendations for Security Teams

For security operations centers (SOCs), system administrators, and leadership, the uncontrolled use of AI chatbots represents a tangible data exfiltration and intellectual property theft vector. A red team could potentially use information leaked into a model to gather intelligence about a target organization. Conversely, blue teams must develop policies to mitigate this risk. The recommended approach is layered: mandate the use of the robust privacy portal opt-out for all corporate accounts, encourage the use of temporary chats for any work-related queries, and strictly enforce policies on the types of information that can be submitted.

Organizations should treat AI chatbot usage with the same seriousness as any cloud service. This includes conducting a risk assessment, updating acceptable use policies to explicitly forbid the submission of sensitive data, and providing clear guidance on secure usage practices. Technical controls, such as network-level monitoring for traffic to these services and integrating DLP tools to scan for and block sensitive data before it reaches the chatbot, are highly recommended. Training and awareness campaigns are essential to ensure all personnel understand the risks and the correct procedures for using these powerful but risky tools safely.

In conclusion, while AI chatbots offer significant utility for security tasks, their default data handling practices present a clear and present danger to organizational confidentiality. By understanding the limitations of standard privacy toggles, employing the more robust opt-out method via the privacy portal, leveraging enterprise solutions where possible, and most importantly, enforcing strict data handling policies, security teams can harness the power of AI without compromising sensitive information. A culture of caution and a defense-in-depth strategy are the best defenses against this emerging threat vector.