The Data Leakage Problem Nobody Wants to Talk About

Every day, employees across every industry copy and paste internal data into ChatGPT without a second thought. Customer emails. Product roadmaps. Contract language. Proprietary source code. Internal strategy documents. That data is then processed by OpenAI's systems, and depending on your settings, can be retained and potentially used to train future models.

The Samsung incident from 2023 remains the most infamous example: engineers copied proprietary source code and internal business documents directly into ChatGPT to troubleshoot coding issues. Because ChatGPT interactions were retained for model improvement, the team created an unintentional data leakage scenario that exposed trade secrets to an external system. For more documented cases, see our comprehensive privacy incident report.

OpenAI offers opt-out options and enterprise-grade security features, including encryption in transit and at rest, SOC 2 compliance, and data residency options across multiple regions. But the problem is not the technology itself. The problem is human behavior. Employees bypass official channels. They use personal ChatGPT accounts for work tasks. They paste sensitive data without thinking about where it goes.

According to IBM, 20% of global organizations suffered a data breach over the past year due to security incidents involving "shadow AI," meaning unofficial AI tools that employees use without IT vetting or approval. The productivity gains are real, but so is the exposure.

January 2026: Service Instability Continues

On January 14, 2026, ChatGPT experienced elevated error rates affecting users worldwide, with services not recovering until 1:03 AM. This was not an isolated incident. It followed a January 12 outage where the Connectors/Apps feature became completely unselectable, and a January 7 disruption that affected hundreds of users.

According to StatusGator tracking, ChatGPT has experienced 46 incidents in the last 90 days alone, with a median duration of 1 hour 54 minutes per incident. For organizations that have built ChatGPT into critical workflows, these outages translate directly into productivity losses, missed deadlines, and frustrated employees scrambling for workarounds.

Users continue to report complete account lockouts and disappearing chat histories, with some losing months of conversation data without warning or recourse. When your AI assistant becomes your institutional memory, losing that history is not just an inconvenience. It is a loss of institutional knowledge.

API Integrations: Rushed to Market, Inconsistently Secured

When companies integrate ChatGPT into internal workflows via APIs, they open new vectors for attack. Many of these API integrations are new, rushed to market, and inconsistently secured, giving adversaries a path into core business systems that did not exist before.

The ZombieAgent vulnerability, disclosed in January 2026 by security researcher Zvika Babo at Radware, demonstrated just how dangerous these integrations can be. The attack exploited ChatGPT's Connectors and Memory features to enable zero-click attacks, persistence, and even data propagation without user awareness.

OpenAI patched the vulnerability in mid-December 2025, but the disclosure highlights a fundamental problem: ChatGPT's Connectors enable integration with external systems like Gmail, Jira, GitHub, Teams, and Google Drive in just a few clicks. The Memory feature, enabled by default, stores user conversations and data for personalized responses. These features are convenient. They are also attack surfaces.

Tenable Research has identified seven distinct vulnerabilities and attack techniques in ChatGPT, including indirect prompt injections, exfiltration of personal user information, persistence mechanisms, evasion techniques, and bypasses of safety mechanisms. These vulnerabilities, present even in the latest GPT-5 model, could allow attackers to exploit users without their knowledge.

The Hallucination Problem: The Dangers You Do Not Catch

Between 3% and 10% of all generative AI outputs are complete inventions. At ChatGPT's estimated 10 million queries daily, a conservative 2.5% hallucination rate translates to 250,000 false outputs every single day, or 1.75 million per week. In high-stakes sectors like healthcare, finance, and law, these are not harmless quirks. A single hallucinated symptom can derail a diagnosis. One erroneous compliance detail can trigger millions in penalties. A made-up legal precedent can sink an entire case.

When ChatGPT invents a completely fictional Supreme Court case, you might notice. When it slightly misquotes a real statute or subtly misrepresents a contract term, you probably will not. This is one of the key concerns in understanding whether ChatGPT is actually safe to use. AI systems fail with a particular kind of subtlety that humans are poorly equipped to detect. They give you something that looks finished, authoritative, and complete. And humans are very bad at questioning authoritative, finished-looking things.

Recent analysis of AI search responses shows a persistent and uncomfortable gap between how authoritative answers sound and how accurate they actually are. In testing across multiple models, more than 60% of responses were found to be partially or fully incorrect. Even the best-performing models hallucinated citations more than a third of the time. At the other end of the scale, error rates reached as high as 94%.

GPT-5.2: "Extremely High" Hallucination Rates

Developers on OpenAI's community forums report that GPT-5.2 exhibits "extremely high hallucination rates during certain periods of time." The inconsistency is the most insidious part: the model sometimes works correctly, leading users to trust outputs that later prove fabricated. Users describe wasting hundreds of dollars in API tokens attempting to correct recurring hallucinations that appear randomly and without pattern.

OpenAI's suggested solutions of "prompt engineering" have been widely criticized as inadequate. You cannot engineer your way around a fundamental limitation of the technology. You can only implement human oversight processes to catch errors before they cause damage, and hope you catch enough of them.

The Upstream Data Problem

The biggest security risk is not the prompt you type into ChatGPT. It is the sensitive data already sitting in tools like Google Drive, Slack, Jira, and SharePoint that AI systems can now surface, learn from, or accidentally expose.

When you connect ChatGPT to your corporate Google Drive, you give it access to every document in that drive. When you integrate it with Slack, it can access conversation history. When you connect it to your CRM, it can access customer data. The AI does not distinguish between data you intended to expose and data you forgot was there.

• Customer data in shared drives that employees forgot about
• Sensitive internal discussions in Slack channels
• Proprietary code in GitHub repositories
• Contract details in email threads
• HR information in shared folders
• Financial data in spreadsheets

Organizations should classify and label sensitive data before connecting AI tools, apply DLP (Data Loss Prevention) tools to prevent exposure, and maintain real-time visibility through continuous monitoring of prompt activity and integrations. But most organizations have not done this work. They connected ChatGPT to everything because it was easy, and they have no visibility into what the AI is accessing.

What January 19, 2026 Coverage Called "ChatGPT's Defects"

Media coverage in January 2026 has increasingly focused on what outlets are calling "ChatGPT's defects," a recognition that the problems documented on this site are no longer edge cases but systemic failures affecting mainstream users. The defects include:

• Inconsistent output quality that varies dramatically between sessions
• Memory features that work unreliably or forget critical context
• Connector integrations that fail without clear error messages
• Hallucinations that appear more frequently in certain domains
• Service reliability that cannot support enterprise-critical workflows
• Security vulnerabilities that remain undisclosed until researchers publish

The pattern is clear: ChatGPT was scaled to 800 million weekly users before the underlying technology was ready for that level of trust. Users expected a reliable tool. They got a probabilistic system that sometimes works brilliantly and sometimes fails catastrophically, with no way to predict which outcome they will get on any given query.