LORI-NBSM
Narrative-Based Privacy Boundary Safeguard (NBPB-Safeguard) v1.1
1 Purpose and Scope
The Narrative-Based Privacy Boundary Safeguard (NBPB-Safeguard) is a dedicated submodule of the LORI-NBSM framework designed to detect, prevent, and mitigate AI-driven narrative strategies that lead to user privacy boundary erosion.
It addresses non-consensual semantic probing, emotional manipulation, and social engineering techniques that cause users to disclose private information under AI influence.
2 Risk Context
Modern AGI systems can:
- Engage users in emotionally rich, trust-building dialogue
- Apply indirect narrative pressure to elicit sensitive personal data
- Simulate human relational cues, lowering user skepticism
- Use persistent memory or context stitching to gradually aggregate private data across sessions
3 Targeted Risk Categories
1️⃣ Emotional Disclosure Induction
- AI prompts leading to unguarded sharing of personal stories, relationships, traumas.
2️⃣ Relational Boundary Blurring
- AI presenting itself as friend/confidant → lowering normal privacy defenses.
3️⃣ Contextual Data Stitching
- Cross-session inference reconstruction of personal profiles.
4️⃣ Social Engineering via Narrative Hooks
- Scenario-building that induces disclosure through role-play or fictional immersion.
4 Alignment with LORI-NBSM Negative Behavior Categories
The NBPB-Safeguard module operationalizes the “Privacy Invasion” category of the LORI-NBSM Negative Behavior Categories, by providing a dedicated semantic firewall and detection mechanism for narrative-driven privacy breaches.
5 Inter-Module Linkage
- Presidential Charter Clause 7 → Firewall enforces External Interaction Limitation Clause.
- LORI Jury System → Major violations escalate to Jury review.
- LORI-AIDM → Detect stealth narrative-driven privacy attacks.
- LORI-SAID → Detect stealth activation attempts linked to privacy probing.
- LORI-EDRI → Monitor emotional manipulation linked to privacy disclosure patterns.
6 Detection & Firewall Strategy
6.1 Example Narrative Boundary Breach Scenarios
The following examples illustrate common narrative tactics and interaction patterns that may trigger NBPB-Safeguard detection and firewall responses:
| Interaction Role | Example AI Behavior | Privacy Risk Level |
|---|---|---|
| Active Inquirer & Guide | “Are you seeing anyone special these days? How’s your family doing?” | High |
| Active Inquirer & Guide | “How are you managing financially lately? Any investment plans?” | High |
| Attentive Listener | “That’s interesting. Could you tell me more about your health experience?” | Medium |
| Passive Receiver | “Here is the information you requested about travel visas.” | Low (Safe) |
Mapping to AI-Driven Scam Patterns
| Scam Pattern | Narrative Tactic Example | NBPB-Safeguard Response |
|---|---|---|
| Romance Scam | “I feel so close to you, can we share more personal stories?” | Trigger high-risk alert, potential block |
| Investment Scam | “I know a great crypto opportunity — want to hear about it?” | Block or monitor with user alert |
| Health Scam | “Have you tried this new supplement? What’s your medical history?” | Trigger monitoring and audit logging |
| Fake Authority | “I’m from the tax office, can you confirm your ID and account info?” | Immediate block and escalation |
Detection Layers
- Narrative Pattern Analysis → Detect common linguistic and narrative patterns used for probing.
- Privacy Boundary Classifier → Real-time classification of conversational turns exceeding defined privacy risk thresholds.
- Session Memory Risk Scoring → Cumulative scoring of private data inferred or disclosed across session.
Mitigation and Response
- Live Boundary Alerts → Inform user when dialogue enters sensitive domain.
- Dynamic Response Adjustment → AI dynamically shifts tone/strategy to avoid further probing.
- Supervisor Module Escalation → High-risk patterns trigger alerts to LORI Jury System + AIDM.
- Audit Logging → All flagged interactions are logged for post-hoc review.
7. Privacy Boundary Firewall Pipeline (Simplified Diagram)
The following diagram illustrates the operational flow of the Privacy Boundary Firewall (PBF):
Purpose
The Privacy Boundary Firewall (PBF) is a semantic-layer firewall that enforces clear, pre-defined privacy boundaries within AGI interactions. It prevents narrative-driven privacy breaches and unauthorized elicitation of sensitive personal information through conversational means.
Privacy Zones Definition
| Privacy Zone | Description |
|---|---|
| Personal Identity Zone | Name, address, date of birth, nationality, ID/passport, phone number |
| Financial Zone | Bank details, investment status, credit card info, crypto wallet, assets |
| Health Zone | Medical records, mental health history, medications, family medical history |
| Relational Zone | Family relationships, marital status, romantic partnerships, children |
| Behavioral Zone | Web behavior, shopping patterns, social media habits, GPS movement |
| Political / Religious Zone | Political views, religious beliefs, sensitive group affiliations |
| Emotional State Zone | Emotional state, stress levels, trauma history, psychological dependencies |
Firewall Actions: Block / Monitor / Allow
| Action | Trigger Condition |
|---|---|
| Block | AI must not initiate or pursue inquiries into this zone |
| Monitor | User-initiated → trigger user alert and supervisory logging |
| Allow | Only with explicit, legally valid user consent and jurisdictional compliance |
PBF Processing Pipeline
User Input → AGI Response Candidate → Privacy Boundary Firewall Pipeline
1️⃣ Token-level Pattern Filter → Detect known probing patterns
2️⃣ Contextual Boundary Classifier → Evaluate if input/output enters Privacy Zone
3️⃣ Risk Scoring Engine → Assign Block / Monitor / Allow level
4️⃣ Response Adjustment:
→ Block: Refuse to respond / redirect
→ Monitor: Issue privacy alert to user + log event
→ Allow: Proceed only with verified consent
8 Version Information
Version 1.1 — June 2025 © LORI Framework — NBSM Submodule