AI Policing AI: OpenAI Deploys Custom ChatGPT to Hunt Down Internal Leakers

In a recursive twist that feels ripped from the pages of a cyberpunk novel, OpenAI has reportedly turned its own creation against its workforce. The artificial intelligence giant is now utilizing a specialized, custom version of ChatGPT to investigate and identify employees responsible for leaking sensitive internal information to the press. This development marks a significant escalation in Silicon Valley's war on secrecy, fundamentally altering the dynamic between AI creators and the systems they build.

For a company whose mission is to "ensure that artificial general intelligence benefits all of humanity," the internal atmosphere appears increasingly focused on ensuring that information about that intelligence remains strictly confined. As reported by The Information, this new tool allows security personnel to feed external news articles—such as those detailing unreleased models or internal strife—into the system, which then cross-references the public text against vast archives of internal communications.

The "Leak Catcher": How the Tool Works

The mechanism behind this digital detective is as potent as it is dystopian. According to sources familiar with the process, when a leak surfaces in media outlets like The New York Times or The Information, OpenAI’s security team inputs the article into this purpose-built ChatGPT instance.

Unlike the consumer version of ChatGPT, which is walled off from private data, this internal variant has privileged access to the company’s deepest communication logs. It can scan:

• Slack Messages: Years of casual conversations, project updates, and direct messages.
• Email Archives: Formal correspondence and external communications.
• Document Access Logs: Records of who opened specific technical briefs or strategy documents.

The AI analyzes the leaked article for specific phrasing, unique data points, or obscure project codenames that would only be known to a select few. It then correlates this "fingerprint" with internal records to flag employees who had access to that specific information or who used similar language in private chats.

This automated forensic analysis dramatically reduces the time required to trace a leak. What once took weeks of manual log review by human investigators can now be narrowed down to a shortlist of suspects in minutes. It transforms the vague suspicion of "someone talked" into a probabilistic ranking of "who most likely talked."

A Legacy of Leaks and Paranoia

The deployment of this tool is not an isolated measure but a reaction to a tumultuous period in OpenAI's history. The company has been besieged by high-profile leaks that have not only embarrassed leadership but arguably shifted the trajectory of the entire industry.

The most infamous of these was the revelation of Q* (pronounced Q-Star), a mysterious model capable of solving novel mathematical problems, which leaked just days before CEO Sam Altman’s shock firing—and subsequent rehiring—in November 2023. More recently, details regarding "Project Strawberry" (later released as the o1 model) trickled out to the press, undermining the company's carefully orchestrated launch schedules.

These incidents have hardened OpenAI’s internal culture. The open academic spirit that defined its early non-profit days has largely evaporated, replaced by the rigid information silos typical of a defense contractor.

Table 1: Timeline of Major OpenAI Leaks and Security Responses

The Panopticon Effect: Surveillance by Syntax

The psychological impact of this tool on OpenAI’s workforce cannot be overstated. Employees are now working in an environment where their syntax, word choice, and casual digital footprint are constantly liable to be weaponized against them by the very tools they help build.

This creates a "panopticon" effect—the feeling of being constantly watched, even if the watcher is an algorithm. It raises profound questions about the nature of work in the AI era. If an AI can analyze semantic drift to identify who spoke to a reporter, can it also predict who might leak based on sentiment analysis of their Slack messages?

The irony is palpable: the company effectively trains its models on the open internet (often scraping data without explicit consent) but employs draconian, AI-powered surveillance to prevent its own data from returning to that same public sphere.

The Aschenbrenner Case and Whistleblower Rights

The aggressive hunt for leakers also intersects with complex legal and ethical issues regarding whistleblowing. In April 2024, researchers Leopold Aschenbrenner and Pavel Izmailov were terminated for alleged leaking. Aschenbrenner, a member of the "Superalignment" team, later publicly stated that his dismissal was politically motivated and filed a complaint with the U.S. Securities and Exchange Commission (SEC).

His complaint alleged that OpenAI’s non-disclosure agreements (NDAs) were illegally restrictive, potentially preventing employees from reporting safety concerns to regulators. If the "Leak Catcher" tool is used to identify employees who are communicating with federal regulators or exposing safety violations—rather than just selling trade secrets—OpenAI could face significant legal headwinds.

Broader Industry Trends: The Fortress Mentality

OpenAI is not alone in this fortress mentality, though it is perhaps the most aggressive in automating it. As the stakes of the "AI Arms Race" escalate, with trillions of dollars in market value on the line, leading labs like Google DeepMind and Anthropic are also tightening their security perimeters.

However, the use of a Large Language Model (LLM) to police human employees introduces a new variable. Traditional Data Loss Prevention (DLP) software looks for specific file transfers or keywords. An LLM-based security tool understands context. It can detect a leak even if the employee paraphrased the information to avoid keyword filters. This represents a quantum leap in corporate counter-intelligence capabilities.

Implications for the Future of Work

The precedent set here is unsettling for the broader technology sector. As AI tools become more integrated into enterprise software, the capability to conduct deep, semantic surveillance of employees will become commoditized.

• Semantic Analysis: Employers could track "alignment" with company values by analyzing tone in emails.
• Pre-crime Detection: AI could flag employees exhibiting signs of burnout or dissent before they resign or speak out.
• Automated Investigations: HR investigations could be conducted primarily by AI agents reviewing communication logs.

Conclusion: The Silencing of the Labs

OpenAI’s use of a custom ChatGPT to catch leakers is a technological marvel and a cultural warning shot. It demonstrates the raw power of the technology to parse vast amounts of unstructured data to find a "needle in a haystack." Yet, it also signals the end of the era of openness in AI research.

As these companies race toward Artificial General Intelligence (AGI), the walls are closing in. The researchers building the future are doing so under the watchful eye of the very intelligence they are creating. For Creati.ai, this development underscores a critical tension: as AI systems become more capable, they will inevitably be used to enforce the power structures of the organizations that control them, turning the "black box" of AI into a tool for keeping the organization itself a black box.

The message to OpenAI employees is clear: The AI is listening, and it knows your writing style better than you do.