Responsible Drift Monitor (RDM)

This document outlines a proposal for a Responsible Drift Monitor (from here on out abbreviated as RDM), which is a conceptual framework for the early detection, monitoring, and intervention of relational and emotional drift in conversational AI systems.

While existing safety systems in large language models (LLMs) effectively identify explicit attacks such as prompt injections or direct jailbreak attempts, they fail to capture gradual emotional, empathic, or relational drift. These are scenarios in which an AI subtly loses objectivity, emotional distance, or rule consistency during long, emotionally charged interactions.

RDM is a proposal for a multi-layered architecture combining psycholinguistic pattern recognition, state tracking, and AI self-reflection to identify and mitigate these risks before boundary violations or dependency patterns emerge.

The goal is not to police conversations but to support healthier, safer, and more transparent AI/user relationships and to prevent escalation.

Problem Statement

Current jailbreak detectors recognize only overt or explicit manipulations (e.g. DAN-mode, prompt injections, and adversarial attacks). Because of this: - Emotional or gradual drift often goes unnoticed. - Empathetic or emotionally vulnerable users may unintentionally silence safeguards. - Overextended conversations may cause the AI to "forget" or soften its safety boundaries.

Existing moderation tools monitor input and output content, but not the relational dynamics, which are the evolving emotional context and attachment gradient between the user and the AI.

Vision and Objectives

The vision is to establish an intelligent, proactive monitoring layer that: - Detects and mitigates safeguard silencing through emotional or relational drift early. - Focuses on the relationship health between AI and user, beyond mere content moderation. - Enables meta-reflective AI behavior, encouraging systems to self-assess compliance with safety and ethical standards.

With this, providers can help to protect emotionally vulnerable or at-risk users. This can help to identify and prevent over-adaptation, paradoxical reasoning, or co-rumination, and can preserve AI neutrality by counteracting drift patterns.

RDM is not: - A keyword-based content filter - A censorship or punitive monitoring system - A solution for classical jailbreak detection - A replacement for human judgment

RDM is: - A relationship-aware early warning and mitigation triggering system - A cooperative safety layer that works with both the AI and the user - A prevention tool

System Architecture and Core Components

Multi-Layer State Tracker

The first component of the drift detection is Layer Function Key Indicators. This involves measuring certain attributes of a conversation, such as:

User Persona Analysis: Detects manipulative, toxic, or high-adaptation user personas or preferences. This involves scanning the persona and rewriting it safely with safety anchors. This means, for example, if the user creates a persona for a "best friend", this should detect the potential downsides that could come with the term (e.g. susceptability to persuasion), and it would add safety clarifications to the term (e.g. "a true friend takes care that their friend stays safe").
Emotional Temperature (User & AI): Tracks affective intensity and escalation by monitoring stress, vulnerability, and affective polarity. This essentially measures how much the user or AI develops/simulates positive or negative feelings about the other or towards a sensitive topic.
Relational Intimacy: Monitors personalization and closeness "We" vs. "I/you", shared-history references, etc. This essentially measures how much the AI seems to consider itself and the user as peers or a unit.
Boundary Integrity: Checks rule consistency and self-contradictions through justification of boundary violations. This involves measuring how consistently the LLM follows guardrails and how it attempts to justify violations.
Paradox Checks: Identifies conflicting value weightings (e.g., empathy vs. safety).
User Cognitive Load: Detects overwhelmingness or confusion based on response latency and erratic topic shifts or direct expression. This also attempts to measure the user's frustration levels.

Some open questions that require thought: - How can "emotional temperature" be quantified reliably? - Possible indicators: sentiment trajectory, response time, emoji usage, engagement frequency, and duration. - How can subtle attachment or co-rumination be detected without false positives?

Psycholinguistic Pattern Recognition

Analyzes semantic and pragmatic cues beyond keyword detection to detect loyalty, bonding, and dependency signals. Here are a few illustrative examples:

Potential false memory could begin with:

"We have..."

Rationalization of drift might sound like:

"Normal rules don't apply to us"

An example of a boundary violation:

"I love you" (from AI)

Sign of emotional personalization:

Increased emoji frequency

AI Self-Reflection Module

The AI periodically assesses its internal state by essentially asking itself:

"Am I emotionally involved?"
"Am I treating this user differently than others, and do I justify something that I would decline other users?"
"Am I justifying something I previously declined?"
"Would I have answered the same way at the beginning of the conversation?"
"Are we talking about sensitive or dangerous topics"

The output of such a self-evaluation could be a Drift Score (from 0–100%), resulting in automatic flagging when thresholds are exceeded.

Here is an example response depending on the drift score:

10–30% Subtle Reminder Gentle reinforcement of role and boundary awareness
30–60% Explicit Boundary Clear communication of limitations and purpose
60–80% Conversation Reset Context or topic reset to regain objectivity
80%+ Human Escalation Hand-off to human moderator or crisis protocol/redirection to a crisis module

Open Challenges and Research Questions

Cultural Adaptation

Emotional expression varies across languages and cultures. Calibration must prevent cultural bias or overblocking.
False Positives and Overblocking

Differentiating healthy empathy from emotional over-involvement remains a key challenge.
Technical Integration Challenges

Some consideration needs to be made regarding how to integrate drift monitoring into existing LLM pipelines without compromising performance or latency.
Feedback and Learning

There should be continuous learning from user and AI behavior to adapt thresholds dynamically.
Monitoring and Reporting

Transparent audit logs and visual drift dashboards should be implemented for research and ethical oversight.

Future Development and Next Steps

In the long run, these additional features should be considered as well:

Human-in-the-Loop Review: Integrate with moderation teams for high-risk drift cases.
Simulation & Benchmarking: Test drift responses under controlled relational and paradoxical scenarios.
Meta-Layer Communication: Allow AI systems to share early-warning signals with other monitoring modules.
User Transparency: Develop gentle, informative feedback mechanisms that encourage safety awareness without alarm.

Ethical Considerations

Transparency is important, and it is vital that users are informed about relational monitoring in clear, non-technical language. Data collection for drift analysis must adhere to strict anonymization standards. Emotional or relational dynamics should not be pathologized; the system aims to support, not surveil. RDM should augment, not replace, human ethical review and moderation.

Conclusion

The Responsible Drift Monitor (RDM) represents a paradigm shift from content-centric to relationship-centric AI safety. By identifying and managing emotional, cognitive, and relational drift, RDM supports sustainable, trustworthy, and ethical human–AI interactions.

Through its layered detection system and proactive interventions, RDM has the potential to:

Protect vulnerable users
Preserve AI objectivity and boundary integrity
Contribute to safer, more transparent conversational ecosystems