Gen AI chatbots have changed how we should analyze user intent. Before AI chatbots, we relied more on structured interactions—clicks, impressions, page views. Now, we’re dealing with free-form conversations.This shift in how intent is expressed creates several challenges, outlined below: PII (Personal Identifiable Information) Everywhere: In general, a lot of financial and healthcare-related conversations with chatbots contain PII Data like SSNs and medical diagnoses. Fragmented Signals: User intent now unfolds over multi-turn conversations instead of through single events like clicks and impressions. PII (Personal Identifiable Information) Everywhere: In general, a lot of financial and healthcare-related conversations with chatbots contain PII Data like SSNs and medical diagnoses. PII (Personal Identifiable Information) Everywhere: In general, a lot of financial and healthcare-related conversations with chatbots contain PII Data like SSNs and medical diagnoses. PII (Personal Identifiable Information) Everywhere Fragmented Signals: User intent now unfolds over multi-turn conversations instead of through single events like clicks and impressions. Fragmented Signals: User intent now unfolds over multi-turn conversations instead of through single events like clicks and impressions. Fragmented Signals Previously, the recommendation systems assumed structured inputs, with LLM’s they need actual conversation signals to make them productive and for training the models. System needed for Ingesting ChatBot data A real-time PII processor using both regex rules and contextual NLP in the ingest pipeline A privacy-aware data warehouse supporting analytics and legal compliance with data encryption Conversation metrics that improve models without requiring raw data access A real-time PII processor using both regex rules and contextual NLP in the ingest pipeline A privacy-aware data warehouse supporting analytics and legal compliance with data encryption Conversation metrics that improve models without requiring raw data access Building a Better Framework Data Ingestion Our system processes incoming chat data through a high-throughput pipeline from applications: Our system processes incoming chat data through a high-throughput pipeline from applications: class SecureMessage(BaseModel): chat_id: UUID # Conversation session request_id: UUID # User question identifier response_id: UUID # LLM response identifier timestamp: datetime # Event time encrypted_pii: bytes # GPG-encrypted raw text clean_text: str # De-identified content metadata: Dict[str, float] # Non-PII features (sentiment, intent) vector_embedding: List[float] # Semantic representation (768-dim) session_context: Dict # Device, region, user segment class SecureMessage(BaseModel): chat_id: UUID # Conversation session request_id: UUID # User question identifier response_id: UUID # LLM response identifier timestamp: datetime # Event time encrypted_pii: bytes # GPG-encrypted raw text clean_text: str # De-identified content metadata: Dict[str, float] # Non-PII features (sentiment, intent) vector_embedding: List[float] # Semantic representation (768-dim) session_context: Dict # Device, region, user segment The magic below happens in the PII detection system in the ingestion pipeline: The magic below happens in the PII detection system in the ingestion pipeline: Pattern Matching: more than 150 regex patterns catch common PII formats, and this regex can be updated as per config, i.e. the list can grow as we find more PII pattern matches. Named Entity Recognition: A fine-tuned BERT model from Hugging Face to have a score on chat conversations Contextual Analysis: Identifies implicit PII by doing contextual analysis False Positive Reduction: This is very important, as we need to have a way to reduce false positives Pattern Matching: more than 150 regex patterns catch common PII formats, and this regex can be updated as per config, i.e. the list can grow as we find more PII pattern matches. Pattern Matching: more than 150 regex patterns catch common PII formats, and this regex can be updated as per config, i.e. the list can grow as we find more PII pattern matches. Pattern Matching Named Entity Recognition: A fine-tuned BERT model from Hugging Face to have a score on chat conversations Named Entity Recognition: A fine-tuned BERT model from Hugging Face to have a score on chat conversations Named Entity Recognition Contextual Analysis: Identifies implicit PII by doing contextual analysis Contextual Analysis: Identifies implicit PII by doing contextual analysis Contextual Analysis False Positive Reduction: This is very important, as we need to have a way to reduce false positives False Positive Reduction: This is very important, as we need to have a way to reduce false positives False Positive Reduction All detected PII is secured with envelope encryption using rotating AES-256 data keys, with master keys stored in GSM or some cloud secret manager with strict access controls. Multi-Temperature Storage All the data might not need the same treatment, so a tiered approach for storage is a great idea. Here’s our system: Tier Technology Retention Use Case Access Pattern Hot Redis + Elasticsearch 7 days Real-time A/B testing High-throughput, low latency Warm Parquet on Cloud Storage 90 days Model fine-tuning Batch processing, ML pipelines Cold Compressed Parquet + Glacier 5+ years Legal/regulatory audits Infrequent, compliance-driven Tier Technology Retention Use Case Access Pattern Hot Redis + Elasticsearch 7 days Real-time A/B testing High-throughput, low latency Warm Parquet on Cloud Storage 90 days Model fine-tuning Batch processing, ML pipelines Cold Compressed Parquet + Glacier 5+ years Legal/regulatory audits Infrequent, compliance-driven Tier Technology Retention Use Case Access Pattern Tier Tier Technology Technology Retention Retention Use Case Use Case Access Pattern Access Pattern Hot Redis + Elasticsearch 7 days Real-time A/B testing High-throughput, low latency Hot Hot Redis + Elasticsearch Redis + Elasticsearch 7 days 7 days Real-time A/B testing Real-time A/B testing High-throughput, low latency High-throughput, low latency Warm Parquet on Cloud Storage 90 days Model fine-tuning Batch processing, ML pipelines Warm Warm Parquet on Cloud Storage Parquet on Cloud Storage 90 days 90 days Model fine-tuning Model fine-tuning Batch processing, ML pipelines Batch processing, ML pipelines Cold Compressed Parquet + Glacier 5+ years Legal/regulatory audits Infrequent, compliance-driven Cold Cold Compressed Parquet + Glacier Compressed Parquet + Glacier 5+ years 5+ years Legal/regulatory audits Legal/regulatory audits Infrequent, compliance-driven Infrequent, compliance-driven Data should be partitioned by time, geography, and conversation topic—optimized for both analytical queries and targeted lookups. Access controls enforce least privilege principles with just-in-time access provisioning and full audit logging. Overcoming Technical Hurdles Building this system has its challenges: Scaling Throughput: Scaling Kafka consumers to achieve 100ms end-to-end latency to power models with real-time data Accurate PII Detection: Our use of NLP and Regex Regex-based PII system helped us ensure privacy Maintaining Data Utility: Semantic preservation techniques (replacing real addresses with similar fictional ones) retained 95% analytical utility with zero PII exposure Scaling Throughput: Scaling Kafka consumers to achieve 100ms end-to-end latency to power models with real-time data Scaling Throughput Accurate PII Detection: Our use of NLP and Regex Regex-based PII system helped us ensure privacy Accurate PII Detection Maintaining Data Utility: Semantic preservation techniques (replacing real addresses with similar fictional ones) retained 95% analytical utility with zero PII exposure Maintaining Data Utility Measuring What Matters Hallucination Detection That Actually Works Actually We calculate a Hallucination Score (H) as: H = 1 - (sim(R, S) / max(sim(R, D))) Where: R = LLM response S = Source documents/knowledge D = Knowledge base sim() = Cosine similarity between embeddings R = LLM response S = Source documents/knowledge D = Knowledge base sim() = Cosine similarity between embeddings Conversation Quality Metrics Our framework tracks: Engagement Depth: Turn count vs. benchmark for intent type Resolution Efficiency: Path length to successful resolution User Satisfaction: Both explicit feedback and implicit signals (repeats, abandonment) Response Relevance: Coherence between turns and contextual adherence Engagement Depth: Turn count vs. benchmark for intent type Engagement Depth Resolution Efficiency: Path length to successful resolution Resolution Efficiency User Satisfaction: Both explicit feedback and implicit signals (repeats, abandonment) User Satisfaction Response Relevance: Coherence between turns and contextual adherence Response Relevance Compliance on Autopilot Privacy regulations shouldn't require manual processes. Our system automates: GDPR Workflow: From user request to crypto-shredding across all storage tiers CCPA Handling: Automated inventory and report generation Retention Policies: Time-based purging with justification workflows GDPR Workflow: From user request to crypto-shredding across all storage tiers GDPR Workflow CCPA Handling: Automated inventory and report generation CCPA Handling Retention Policies: Time-based purging with justification workflows Retention Policies Making AI/ML Better The framework generates de-identified features: Conversation-level aggregates (length, topic shifts, sentiment) Turn-level metrics (response time, token efficiency) User satisfaction correlates without the need for individual identification Conversation-level aggregates (length, topic shifts, sentiment) Turn-level metrics (response time, token efficiency) User satisfaction correlates without the need for individual identification Privacy You Can Count On Our framework delivers both cryptographic and statistical privacy guarantees: Cryptographic: AES-256 encryption with 30-day key rotation Statistical: (ε,δ)-differential privacy with ε=2.1 and δ=10^-5 Anonymity: k-anonymity with k≥10 for all demographic aggregates Cryptographic: AES-256 encryption with 30-day key rotation Cryptographic Statistical: (ε,δ)-differential privacy with ε=2.1 and δ=10^-5 Statistical Anonymity: k-anonymity with k≥10 for all demographic aggregates Anonymity The Road Ahead We're continuing to improve the framework with: Support for multimodal conversations (text, voice, image) Integration with homomorphic encryption Federated fine-tuning capabilities Enhanced PII detection for specialized domains Support for multimodal conversations (text, voice, image) Integration with homomorphic encryption Federated fine-tuning capabilities Enhanced PII detection for specialized domains
