Situation

As Viator scaled its use of Large Language Models across multiple customer-facing domains, the lack of centralized oversight posed significant brand and safety risks. Disparate teams deployed prompts without standardized safety guardrails, leading to inconsistent outputs and potential exposure to adversarial inputs.

Task

Design and implement an organization-wide AI Governance and Responsible AI Framework to standardize safety, compliance, and ethical oversight. Goal: 100% of production models pass a rigorous Safety Gate without significantly increasing deployment latency.

Action

Pillar 1: Safety Gate Implementation (“Critic-at-the-Edge”)

Architecture achieving <10ms validation:

  • PII Detection: Regex patterns for common PII (email, phone, SSN) + Presidio for entity recognition (runs in parallel)
  • Content Classification: DistilBERT fine-tuned on 10K labeled examples for prohibited content categories
  • Bias Detection: Lightweight heuristics (word lists, sentiment skew) for initial screen; heavy model for flagged cases only
  • Batching: Requests batched at 10ms intervals; amortizes model inference overhead
  • Total Latency: P50: 4ms, P95: 8ms, P99: 12ms (measured over 1M requests)

Pillar 2: Adversarial Robustness

  • Red Team Program: Quarterly adversarial testing by internal security team + external bug bounty
  • Encoding Detection: Base64, ROT13, Unicode homoglyph detection in input preprocessing
  • Multi-Turn Analysis: Conversation-level context tracking to detect jailbreak attempts across turns
  • Canary Tokens: Synthetic “honeypot” prompts in production to detect bypass attempts

Pillar 3: Source Authority Hierarchies

  • Gold Sources: Official policy documents, verified supplier data (weight: 1.0)
  • Silver Sources: Structured product metadata, curated FAQs (weight: 0.7)
  • Bronze Sources: Chat logs, user reviews (weight: 0.3)
  • Conflict Resolution: Higher-tier sources always override lower-tier when contradictions detected

Pillar 4: Observability & Audit

  • Logging: Every inference logged with input hash, output, model version, safety scores
  • Monitoring: Arize for real-time safety metric tracking and drift detection
  • Audit Trail: Immutable log storage (S3 + Athena) for compliance investigations
  • Alerting: PagerDuty integration for safety score anomalies (>2 std from baseline)

Results

Metric Before After Method
Safety Gate Coverage 34% 100% All production endpoints integrated
High-Risk Incidents ~12/month 1/month Severity-weighted incident count
False Positive Rate — 2.3% Manual review of flagged queries
Validation Latency Overhead — 8ms P95 End-to-end measurement
Compliance Audit Pass Rate — 100% External audit Q4 2024

System Design & Architecture

Modular Safety Layer integrated into LLMOps pipeline:

  • Async Validation: Non-blocking for low-risk queries; blocking for flagged content
  • Fallback Responses: Pre-approved safe responses for blocked queries
  • A/B Testing: New safety models tested in shadow mode before promotion

Risks & Mitigations

Risk Impact Mitigation Monitoring
Latency Bloat Safety checks slow UX SLMs + batching + parallel execution P95 latency dashboard
False Positives Harmless queries blocked HITL review queue; threshold tuning FP rate by category
Adversarial Bypass Safety layer circumvented Red team + encoding detection + multi-turn analysis Canary token trigger rate
False Negatives Harmful content passes Layered detection; human audit of random sample Weekly audit of 100 outputs
Model Drift Safety model degrades over time Continuous retraining on new examples Safety score distribution tracking