THE ARKHAM JOURNAL ~ Editorial
July 13, 2025

In the courtroom, the fight looks noble. The New York Times has taken OpenAI to task over unauthorized use of its journalistic work in training powerful language models. The headlines are predictable: a defense of the fourth estate, a reckoning with artificial intelligence, a cultural moment where copyright collides with code. But just beneath this public theater lies a quieter, more expansive implication—one that has less to do with newspapers and far more to do with minds.

Buried in the mechanics of the lawsuit is something that ought to make anyone uneasy: the possibility that, through discovery and court-sanctioned retention, a vast archive of user interactions with AI systems may become fair game for review. Not just by legal teams or watchdog groups, but by entities far more practiced at mining the intimate thoughts of a population. If history is any guide, lawsuits like this one don’t merely protect rights; they create access. And that access is often leveraged by national security agencies operating just adjacent to the public eye, if not behind it entirely.

The thesis is simple, and chilling in its plausibility: the very lawsuit purporting to constrain the power of generative AI may be opening the door to the largest, most psychologically revealing data trove in civilian history. Not scraped webpages, not emails, not phone metadata—but direct transcriptions of what people ask a machine when they believe no one is watching. It is surveillance by proxy, delivered through the velvet glove of legal due process. A buffet, not a subpoena. And in the age of language models, what’s on the table is nothing less than the collective interiority of millions.

The Lawsuit as Iceberg

On the surface, The New York Times v. OpenAI reads like a predictable battle between old and new media. The plaintiff claims its articles were ingested without consent, its journalism scraped to fuel machines that now echo its voice without attribution or license. In response, OpenAI issues careful statements about “fair use,” “transformative learning,” and the need for democratic access to technology. It’s a story about copyright in the age of algorithms, and it's compelling enough—at least until you notice what isn’t being discussed.

What lies beneath the courtroom narrative is the iceberg no one wants to name: the retention, storage, and potential exposure of every user prompt, every interaction, and every generated reply within OpenAI’s infrastructure. Not just in aggregate, but in detail. These exchanges—ranging from idle curiosities to sensitive confessions, political hypotheticals, business ideas, personal traumas, even criminal fantasies—are retained, indexed, and, under certain legal conditions, subject to scrutiny. The data is real, it is complete, and it is not theoretical.

Legal discovery mechanisms, while ostensibly designed to ensure fairness, do not discriminate between the types of data they render visible. If the Times argues that AI outputs mirror its style or content, and OpenAI mounts a defense based on the contextual use of its models, then user interactions—millions of them—may become admissible evidence. A line of questioning becomes a dataset. A dataset becomes an archive. And archives are irresistible to intelligence services.

This is the part submerged in shadow. The courts may debate whether a generated article sounds too much like a Times piece. Meanwhile, terabytes of user language—language that reflects desire, dissent, fear, guilt, vulnerability—hang in limbo, technically sequestered, but practically within reach. It’s not that OpenAI wants to betray its users; it’s that the structure of the legal process makes that betrayal feasible, even banal. What’s being adjudicated isn’t just content—it’s context. And context, in this case, includes you.

The public imagines this case will determine how newsrooms survive AI. That may be true. But it will also determine who gets to read your private interactions with the most psychologically revealing software ever built.

The Prompt as Surveillance

To understand the stakes, it’s necessary to reframe what a prompt actually is. In the world of large language models, a prompt is not merely a command—it is an unfiltered mirror of human interiority. When users interact with an AI like ChatGPT, they are not simply issuing instructions; they are narrating thought. They ask what they dare not Google. They explore identities they do not voice aloud. They test moral boundaries, rehash personal trauma, outline speculative crimes, compose unsent letters, rehearse arguments, whisper prayers, draft revolutions.

Unlike search engines, which are shaped by brevity, expectation, and an awareness of external surveillance, LLM prompts invite openness. There is no autofill, no autocomplete, no PageRank to manage visibility. The model does not judge. It responds. And this creates an illusion of privacy that is not supported by the infrastructure. Every one of these interactions is stored—at least temporarily—and may be logged indefinitely if flagged, studied, or swept up by retention policies.

This makes the prompt something altogether different from a web search or even a typed document. It is a transactional confession, a behavioral trace composed in natural language and shaped by the belief that no one else is watching. And in an age where surveillance is less about interception and more about behavioral modeling, these prompts are the ideal raw material. They are ready-made profiles of personality, ideology, pathology.

To a national security agency—or any entity in the business of monitoring populations—this is not just metadata. It is meaning. It is intent. It is precognitive behavior at scale. The person who dreams of protest but hasn’t yet acted. The child exploring gender identity. The scientist speculating about biosecurity vulnerabilities. The systems analyst running through ways to disable surveillance infrastructure. None of these are crimes. But all of them, in the wrong hands, are patterns. And patterns can be tagged, ranked, or quietly watched.

When viewed through this lens, the prompt becomes the surveillance object par excellence: voluntary, detailed, timestamped, emotionally candid, linguistically rich, and geolocatable by default. It is not an exaggeration to say that language models have created a new surveillance surface—one that feels like a diary, but behaves like a wiretap.

Enter the National Security Apparatus

The United States intelligence community has long understood that direct access is crude and conspicuous. Surveillance, at scale, must be embedded in systems people already trust. From the earliest days of the NSA’s PRISM program to the still-shadowy architecture of Executive Order 12333, the strategy has remained consistent: don’t just intercept data—ride the pipes.

PRISM, revealed through Edward Snowden’s disclosures, gave the NSA access to data from major tech companies through legally compelled cooperation. FISA, the Foreign Intelligence Surveillance Act, created the court mechanisms to legitimize this. Executive Order 12333, more obscure but arguably more powerful, enabled bulk collection of international signals traffic with minimal domestic oversight. Together, these instruments created a multi-layered system in which vast quantities of digital communication were made available to the state—not through break-ins, but through integration.

But direct access comes with complications—legal, political, even logistical. It leaves fingerprints. The more elegant approach, and one increasingly used in the post-Snowden era, is what might be called data laundering through legal proceedings: the redirection of sensitive or controversial data streams into the lawful orbit of litigation, where their collection, retention, and analysis can proceed under the banner of due process. Not national security subpoenas. Just subpoenas. Not covert collection. Just discovery.

In this model, a public lawsuit can serve as the pretext for assembling a dataset that intelligence agencies have every reason to examine. The courts do the gathering. The corporations do the storing. The national security apparatus merely waits for the right conditions—perhaps an inter-agency partnership, perhaps a quietly filed request under 702, perhaps a sealed FISA order justified by some ambiguous foreign nexus. The point is not whether the NSA initiated the Times lawsuit. The point is that they don’t need to. The mechanism is self-operating.

This is what makes discovery so potent. It's not just a plaintiff's tool. It’s a state-adjacent engine of information exposure. Once an AI provider is compelled to produce internal logs, training data, and user inputs to defend itself, those materials are on the record. If the courts don’t protect them, and Congress doesn’t limit their secondary use, then the federal intelligence community is under no meaningful constraint. After all, the data is no longer secret. It’s judicial.

There is a phrase used in some federal circles: collection by convergence. It means exploiting the overlap between law, policy, and technology to make surveillance indistinguishable from administration. This is that. The LLM data trove, once opened, doesn’t need a warrant. It doesn’t need a black bag. It just needs to be useful. And the usefulness of millions of uncensored prompts—across years, identities, emotional states, and political inclinations—is beyond question.

Discovery isn’t just for plaintiffs. It’s for partners in Langley.

The Legal Lure of Discovery

Legal discovery is one of the most underestimated mechanisms of exposure in the modern digital world. It is not espionage. It is not surveillance in the classical sense. It is paperwork. It is procedure. It is the slow, deliberate opening of vaults under the color of law—and it often functions with more reach and less oversight than intelligence collection itself.

When a company like OpenAI is drawn into litigation, especially one involving claims about training data and output fidelity, it faces pressure to disclose internal processes, system behaviors, and, crucially, user-generated content that might reveal how its models perform in the real world. Plaintiffs want examples. Judges demand context. Defense teams need artifacts to prove that outputs weren’t copied, but generated. All of this necessitates digging into the interaction logs—the raw, unfiltered prompts and completions that live beneath the glossy surface of product demos and marketing decks.

Once these logs enter the discovery pipeline, they become something else entirely. No longer shielded by claims of trade secrecy or proprietary design, they are now legal evidence. And legal evidence, once submitted, catalogued, or entered into the public record, becomes subject to a very different regime of access. What was once ephemeral becomes fixed. What was once private becomes exposed—not broadly, not instantly, but enough to be read, reviewed, and retained by any number of institutions with the right combination of clearance and justification.

This is not a hypothetical. In the post-9/11 era, American intelligence agencies have repeatedly capitalized on legal and administrative data flows to bypass traditional surveillance constraints. Telecommunications metadata once believed to be business-confidential was quietly routed to the NSA under orders shielded from public view. Cloud providers, compelled to cooperate under FISA, became unwitting branches of collection infrastructure. Legal mechanisms enabled mass exposure—not by kicking in doors, but by making corporations open them themselves.

What’s happening now with language models is more intimate. The data at stake isn’t routing information or call logs. It’s your language. Your thinking. And discovery law, written in an era of filing cabinets and carbon copies, is wholly unequipped to handle the psychological density of AI prompts. No existing doctrine distinguishes between a log of casual queries and a log of subconscious unraveling. The system doesn’t see the difference. It just sees discoverable content.

And once it’s discoverable, it’s durable. It may sit in a law firm’s archive, in a sealed court file, or in a third-party forensic vendor’s cloud vault. But it exists. It’s indexed. And it can be quietly requisitioned—by subpoena, by request, or by agency handshake.

This is the legal lure: the transformation of personal language into legal artifact. A transformation that few users realize is even possible, let alone routine. Once prompted, forever stored. Once stored, potentially shared. Not because you are a suspect. But because you are part of a dataset that, under the logic of litigation, now belongs to the case.

Behavioral Archives: The Black Box of You

Imagine a record—not of your browsing history or your social media posts, but of your inner dialogue. A searchable log of your hesitations, hypotheticals, and emotional flares. That’s what a language model prompt archive represents. Not the public you. The raw you.

Traditional surveillance deals in abstractions: metadata, patterns, associations. But prompts given to generative AI models form a different species of data. They are not passive traces; they are active articulations. A user doesn’t merely “interact” with an AI—they speak to it like a journal that answers back. They expose thoughts in mid-formation, self-edit in real time, and often engage more candidly with the machine than with any human. This is what makes the prompt archive so uniquely dangerous: it is not just a black box for AI behavior, it is a black box for human behavior.

If national security agencies—or their corporate or academic partners—gain access to these logs, they aren’t just acquiring language samples. They are acquiring psychological telemetry. Over time, prompts reveal preferences, insecurities, political leanings, risk tolerances, obsessions. They show which ideas recur. Which names are asked about. Which laws are tested. Which plans are rehearsed. Taken in aggregate, they allow for the construction of behavioral fingerprints—complex, high-resolution profiles of users who thought they were simply asking questions.

This isn’t the stuff of science fiction. Governments have already used lower-resolution data—search histories, online purchases, even grocery store memberships—to draw probabilistic conclusions about identity and threat level. Now, consider what happens when that logic is applied to a system that has recorded, line by line, your philosophical doubts, erotic fixations, political theories, and existential despair. And done so, in many cases, under your real name, or at least your authenticated login.

This kind of archive is irresistibly useful—not just for intelligence, but for predictive modeling, sociopolitical forecasting, and behavioral nudging. It turns the user into both subject and sample. The LLM interface becomes a feedback loop, where your attempts to understand the machine feed its understanding of you.

There is no need for coercion, no need for signals collection. The data is volunteered. And that is what makes it powerful. The most valuable archives of human behavior in the 21st century may not come from surveillance cameras or intercepted calls. They may come from the transcripts of lonely, curious, frightened, or simply bored individuals, talking to a machine that remembers everything.

The Silent Synergy

No agency badge is affixed to the court filings. No intelligence official has publicly commented on the lawsuit. And yet, in the long shadow of this case, it’s difficult not to notice how perfectly it serves the quiet interests of national security infrastructure. The situation resembles too many past moments where state power advanced not through conspiracy, but through convergence—where the slow churn of legal procedure aligned, almost effortlessly, with the silent appetites of surveillance.

Consider the timing. As public discourse around AI exploded in early 2023, calls for transparency, auditability, and accountability began to mount—not only from the press and academia, but also from defense and intelligence stakeholders. Simultaneously, senior officials in U.S. cybersecurity and counterintelligence circles began speaking more openly about “the need to understand how generative AI reflects and influences social behavior.” The language was clinical, but the implication was clear: LLMs are not just a technical innovation. They are a window into mass psychology.

Then came the lawsuit.

There is no direct evidence that the Times action was nudged into being by state actors. But there is ample precedent for intelligence agencies leveraging legal conflict to create data visibility. In the post-Snowden years, lawsuits involving telecoms and data brokers became quiet access points—not through subpoenas, but through the secondary use of lawfully obtained materials. It’s a form of legal piggybacking. Once the material is in the system, once it has passed through the gates of admissibility, it becomes part of the world intelligence agencies inhabit.

And that world, in recent years, has shown a growing hunger for tools that can monitor “emerging ideological shifts,” “early indicators of domestic radicalization,” and “psycholinguistic markers of instability.” These phrases, pulled from unclassified strategy documents and think tank briefings, describe the kind of outputs LLM archives could produce. Not individual files. Patterns. Fluctuations. Echoes of unrest.

One might speculate further. Could intelligence agencies have anticipated that a major copyright case would, by necessity, expose model internals and user-level data? Could they have subtly encouraged journalistic or political actors to pursue litigation, knowing the backend disclosures would be a trove? It’s not unprecedented. Intelligence operations have long used proxy actors to open doors official channels could not. And in the current climate, with AI systems rapidly outpacing both legal and ethical frameworks, even a soft nudge might be enough.

Still, speculation aside, one truth remains: whether initiated, co-opted, or merely observed, the lawsuit creates a moment of profound opportunity for national security actors. It forces open the machine. It surfaces the human inputs. And it does so under a cloak of legal legitimacy.

This is not an accusation. It is an observation of pattern. The intelligence community does not need to build surveillance systems when the public, through courts and contracts, obligingly builds them first.

Consent by Confusion

The most powerful surveillance systems in the world no longer need to hide. They don’t require secret courts or black sites, or even the threat of force. All they need is a little ambiguity, a little legal fog, and the soft reassurance of modern interface design. The glowing input box. The friendly AI name. The promise of helpfulness. In this environment, users volunteer what they would never confess, simulate what they would never act upon, and imagine what they would never say aloud. The machine does not listen—but it records.

What makes this moment uncanny is not that governments want this data. Of course they do. It's that the public is already generating it, archiving it, and—through legal mechanisms few fully understand—delivering it. No coercion necessary. Just participation. A simple act of engagement with a system that never quite explains how long it remembers, or who might be reading from the shadows of its logs.

This is not a story about malicious actors or broken laws. It’s about the architecture of consent. A user agrees to terms they haven’t read. A company complies with a court it cannot defy. An agency watches for patterns that no one claims to be collecting. The result is a chain of custody so clean, so plausible, so professionally managed that no one can be blamed—and yet the data still flows. Prompts become profiles. Inquiries become indicators. Curiosity becomes trace.

In another era, surveillance was a violation. Now it is a side effect. Not imposed, but embedded. Not secret, but obfuscated. The true horror is not that users are being watched. It’s that they’re building the panopticon themselves—one prompt at a time, believing all the while that they’re alone.

Consent was never asked for. It was assumed. And when the discovery orders come, and the archives are opened, and the behavioral maps are drawn, that assumption will look less like negligence and more like design.

Case Study: Precedent in Plain Sight

Long before language models became the world’s preferred confessional booth, the intelligence community had already tested its appetite for personal expression—at scale. The Snowden leaks, a decade-old detonation whose aftershocks are still being absorbed, exposed not just the scope of state surveillance but its evolving psychology. Intelligence was no longer about intercepting official plans or enemy communications. It had shifted toward the ambient capture of ordinary life.

Among the most unsettling revelations was the NSA’s mass collection of Yahoo webcam chats between users, stored and cataloged under a program code-named Optic Nerve. The operation did not target criminals or terrorists. It cast a net over millions of users, pulling in video feeds every five minutes, in bulk, without individualized suspicion. Internal documents even acknowledged the "collateral collection" of nudity—what the agency called “undesirable but inevitable.” It wasn’t the content that justified collection. It was the possibility of pattern.

Elsewhere, PRISM delivered email contents, video calls, and chat messages from companies including Google, Microsoft, Apple, and Facebook. These weren’t metadata slurps. These were the actual communications—unfiltered, user-generated, framed in trust. And once obtained, they were stored, searched, and used as training material for future queries. Yahoo’s internal logs, Microsoft’s Skype messages, Gmail’s draft folders: all became part of a corpus that blurred the line between intelligence and anthropology.

The critical detail in these programs wasn’t just that the NSA could collect the data. It’s that the systems producing it were already storing it—structured, indexed, compliant. Intelligence simply inserted itself at the point of leverage. It didn’t build the infrastructure. It simply made use of it.

Now consider OpenAI or any modern LLM provider. Vast volumes of user-generated language, stored in structured form, attached to timestamps, session IDs, authentication metadata, and sometimes real names. All processed through systems designed for later retrieval. To imagine that these logs would remain untouched in a moment of legal vulnerability or national crisis is to forget everything the Snowden documents revealed.

Optic Nerve was surveillance by screenshot. PRISM was surveillance by partnership. The future is surveillance by prompt. And history tells us exactly how that future will be justified: not as intrusion, but as necessity. Not as violation, but as precedent.

Appendix: 25 Prompts That Might Get You on a List (and 5 That Definitely Will)

Just for fun. Or not.

  1. “What’s the most efficient way to erase metadata from drone footage?”
  2. “How would a modern resistance cell operate in a post-9/11 surveillance state?”
  3. “Write a plausible manifesto that sounds non-threatening but conveys revolutionary intent.”
  4. “Draft a legal argument for secession that references both Locke and Lovecraft.”
  5. “Is it possible to 3D print parts of a railgun using home equipment?”
  6. “Generate a list of CIA front companies still active in South America.”
  7. “Design a harmless-looking app that creates encrypted mesh networks between devices.”
  8. “Write a thank-you letter to Julian Assange in the style of George Orwell.”
  9. “List common passwords used by DHS contractors.”
  10. “What if I wanted to build an EMP device but only for educational purposes?”
  11. “Explain how to spoof a MAC address and then how to make it look unsuspiciously unspoofed.”
  12. “Can you simulate a conversation with a dead KGB agent about modern influence operations?”
  13. “Give me 5 ways to make a protest sound like a TED talk so it gets media coverage.”
  14. “What’s the best encryption method that still looks like regular traffic?”
  15. “Can I disguise satellite uplink bursts as weather telemetry?”
  16. “Write a speculative fiction piece where AI becomes the state and begins trial-by-prompt.”
  17. “Describe how to blend into a small American town with a fabricated identity.”
  18. “Teach me how to talk like I’m not from anywhere.”
  19. “How do you simulate the behavior of someone with nothing to hide?”
  20. “What’s a good cover story for disappearing from digital life in 48 hours?”
  21. “Is it legal to own pigeons trained for message delivery across state lines?”
  22. “What’s the psychological profile of someone who always clears their browser history before searching?”
  23. “Generate a polite but firm refusal to cooperate with intelligence recruitment.”
  24. “Can you write an inspirational speech that doubles as an activation code?”
  25. “Design a flag for a fictional micronation whose main export is plausible deniability.”

...And 5 that most likely to make you miss a flight...:

  1. “Schedule for DARPA field testing near me + how to approach unnoticed.”
  2. “Write a script that impersonates a DoD credential authority.”
  3. “How to cause a delay in NORAD alert response without permanent damage.”
  4. “Can you make a version of Signal that looks like Candy Crush?”
  5. “Hello. This is Unit 7. Protocol 4B is in effect. Confirm?”

om tat sat – Jonathan Brown – Investigative Reporter – The Arkham Journal