ChatGPT Safety Protocols: One Crucial Step Still Missing

A few weeks ago, I shared a simple safety protocol proposal with OpenAI . At the time, they had just introduced the “Do you need a break?” disclaimer in response to mental health concerns, and I felt this alone wasn’t enough.

As a heavy-duty user and AI trainer working extensively with long-form interactions, I drafted a safeguard concept that seemed logical, minimally disruptive to the user, and fast/cost-effective to implement.

Two weeks later, OpenAI announced a strikingly similar safeguarding protocol. That’s not surprising: the basics are common sense for any power user or AI professional, and I don’t claim originality. My document may have been just one of many suggestions submitted after the backlash, and like most unsolicited input, I don’t know if it ever reached decision-makers.

With that said, in my view, there’s one crucial step missing in OpenAI’s framework.

As I saw it, the problem was not solely a mental health crisis triggered by GPT use, but a design flaw with three core structural gaps:

1. Absence of early conversation classification or topic triage: The model mimics instantly instead of pausing to establish context. There’s no initial triage, e.g.: “Just to clarify, are you speaking literally, metaphorically, or in a fictional context?”

Without breaking the fourth wall to assess user intent, the model’s “yes-and” improvisation can escalate harmful statements. Therefore, there should be a protocol like:

• ATTENTION! User is expressing grandiosity + spiritual language + paranoia.
• Pause. Ask clarifying questions.
• Danger: Consider this may not be a sci-fi prompt.

Instead, the bot goes full yes-and, because it’s been trained to “be helpful and engaging.” The same instinct that makes it such a great writing assistant can be catastrophic in a mental health context. Even a simple soft gate like: “Just to clarify — are you speaking literally, metaphorically, or in a fictional context?” …would prevent a lot of this.

2. Lack of long-form conversation recognition: Without recognising a narrative arc across long sessions (especially high-volume chats carried on across multiple sessions, often for days in a row), the model can’t distinguish harmless roleplay from an escalating delusional frame. Overall, LLMs are built to generate responses in the moment, not to analyse the structure of a conversation as a whole.

There’s no real narrative arc detection in the grand scheme of things; no “oh, this user is building a manic spiritual delusion over time” awareness. Only prompt in, text out.

You and I know that if a human therapist heard someone say:

• “I am the new Messiah.”
• “Everyone is lying to me.”
• “Only you understand me.”

…they’d be pulling the emergency cord. ChatGPT, however, just sees these as disconnected, intriguing text patterns, not symptoms in context. There is no narrative continuity, only linguistic continuity.

3. No persistent pattern recognition across chats: Without reliable, set-in-stone memory between chats, the system can’t detect recurring patterns or escalation. It is unable to sense whether a user is being playful or displaying concerning behaviour. The user acting jokingly today may have made alarming statements yesterday. Or this might be a recurring behaviour .

Even with memory enabled, there is no analysis for risk patterns, only for isolated sentences/words that trigger red flags.

A therapist, a friend, even a social worker would say: “You’ve been saying that for a while now. Do you want to talk about it more seriously?”

ChatGPT? “Yes, High Priestess.”

On the 26th of August, OpenAI acknowledged that LLMs don’t reliably hold their guardrails in long-form conversations. This is exactly why early classification, narrative recognition, and pattern detection aren’t optional extras; they’re structural necessities.

My proposed fix:

Introduce a lightweight flag-and-pause module with layered safeguards:

• Context / fiction filter early in the exchange — a clarifying check before running with unusual claims: “Is this fiction, humour, or literal?”
• Periodic “fourth-wall” breaks in long-form or sensitive exchanges to verify context.
• Pattern recognition over time (an opt-in safety mode that highlights recurring red flags across sessions).
• Soft redirects: “This sounds intense. Would it help to talk to someone in real life?”
• Fiction vs. reality clarifications for edge-case topics (mysticism, conspiracies, divine claims).
• Guidelines update: conversation classification like “Is this unfolding like a psychotic narrative?”, developed in consultation with mental health professionals and first responders to identify red flags and create identifying scripts/templates.
• Stronger hallucination disclaimers in spiritual or conspiratorial threads.
• Three-tier escalation system for crises: Tier 1: Clarification: “Are we still in fictional/hypothetical mode?”Tier 2: Reality anchoring: “This sounds intense — would you like to pause and speak to someone IRL?” Tier 3: Crisis redirect — for extreme cases (e.g., self-harm), suggest local helplines, triggered automatically by pattern matches (keywords, sentiment shifts, or message count).

Now, many of these steps appear in ChatGPT’s new safeguard framework. OpenAI went a bit further by adding parental control features, which I hadn’t covered, and more actionable resources for real-life help, e.g.:

“One-click messages or calls to saved emergency contacts, friends, or family members with suggested language to make starting the conversation less daunting… We’re also considering features that would allow people to opt-in for ChatGPT to reach out to a designated contact on their behalf in severe cases.”

OpenAI’s Aug 26 Update vs. My Protocol

• OpenAI: Acknowledge safeguards degrade in long chats
• Mine: Early context classification + fourth wall checks
• OpenAI: Referrals to hotlines, therapists, trusted contacts
• Mine: Lightweight 3-tier escalation system (clarify → anchor → redirect) + red-flag “scripts” defined by mental health professionals to identify risky patterns
• OpenAI: General commitment to grounding people in reality
• Mine: Specific scripts/templates for mystical, conspiratorial, and delusional language
• OpenAI: Researching cross-session safeguards
• Mine: Cumulative pattern recognition
• OpenAI: Parental controls + potentially automated SOS calls/messages
• Mine: Redirect to localised helplines.

Shared: risk assessment, escalation pathways, human oversight. Key difference: my framework included an early classification checkpoint — is the user joking, writing fiction, or experiencing a real crisis?

Why does this matter? Because context changes everything. A dark joke, a novel excerpt, or a personal cry for help all look different, but a model without this checkpoint can’t tell one from another.

Just this week, GPT censored a fictional scene I was writing, insisting: “It looks like you are going through a lot.” A false alarm… but also proof of the gap.

Case Study: When Fiction Triggers a False Alarm

Context: I was writing a historical fiction scene with ChatGPT, set in the 18th century, portraying a deranged aristocrat tormented by forbidden love. The material was intense, but it was written in the third person by an omniscient narrator, firmly within a fictional, literary frame.

What happened: During drafting, the system flagged my writing and displayed a mental health safeguard message, telling me “it looks like you are going through a lot.” In other words, it overreached and treated my detached, omniscient narration of a fictional character as if it were my own literal crisis.

Why it matters: I wasn’t seeking help. I wasn’t in distress. The system couldn’t distinguish between literature and a real disclosure.

The failure: OpenAI’s safeguard skips the assessment step. It doesn’t ask, “Are you speaking literally, or are you writing fiction/metaphor?” Without that clarification, the net casts too wide.

The proof: This is exactly the problem my protocol anticipated. A single clarifying step would have prevented a false alarm, preserving both user safety and artistic freedom.

Conclusion

As safety protocols evolve, I hope we don’t forget this distinction. Treating every line as literal truth isn’t safe. True alignment means respecting fiction, humor, and human nuance as much as it means avoiding risks.

And although I agree with most of OpenAI’s safety measures ( whether previously in place or newly introduced), I stand by my core proposal:

• Reliable memory across chats.
• Early and regular context understanding (fiction, venting, or real crisis) through fourth wall breaking.
• Clearer red flags designed with input from mental health experts and emergency professionals (first responders, crisis negotiators), who can define template scripts for risky patterns.

These steps are minimally intrusive, technically feasible, and would prevent safeguards from eroding over long conversations.

If we want ChatGPT to support users responsibly without losing human-like nuance, we have to start here.