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Nick edited this page Nov 18, 2025 · 2 revisions

PATAS: Comparison & Positioning

This document provides a detailed comparison of PATAS with traditional approaches and existing solutions in the anti-spam/abuse detection market.

PATAS vs Traditional Approaches

PATAS vs Classic ML Classifiers

Aspect Classic ML Classifier PATAS
Pattern Discovery Requires labeled training data, manual feature engineering Automatic discovery from historical logs
Transparency Black-box model (neural network, ensemble) Transparent SQL/regex rules
Explainability Hard to explain why a message was flagged Clear: shows exact rule and why it matched
Metrics Global model metrics (accuracy, F1) Per-rule metrics (precision, recall, coverage)
Rule Management Retrain entire model Individual rule lifecycle (promote/deprecate)
Safety Control Single threshold for all decisions Tiered safety profiles (Conservative/Balanced/Aggressive)
On-Premise Often requires cloud ML services Fully on-premise, no external dependencies

PATAS vs Classic LLM Filters

Aspect Classic LLM Filter PATAS
Pattern Discovery LLM analyzes individual messages Automatic clustering of similar messages
Rule Generation LLM suggests rules (may be inconsistent) Deterministic SQL/regex generation
Cost Per-message API calls (expensive at scale) One-time pattern discovery, then rule-based
Latency API call per message (100-500ms) Rule evaluation (1-10ms)
Transparency LLM reasoning may be opaque Transparent SQL rules
Consistency LLM may give different answers Deterministic rule matching
Privacy Messages sent to external API All processing on-premise

PATAS vs Manual Rule Writing

Aspect Manual Rule Writing PATAS
Discovery Human analysts identify patterns Automatic discovery from data
Coverage Limited by analyst time/expertise Discovers patterns at scale
Maintenance Manual updates when spam evolves Automatic re-discovery
Metrics Often no per-rule metrics Built-in precision/recall/coverage
Safety Ad-hoc safety decisions Systematic safety profiles
Scalability Doesn't scale with data volume Scales automatically

PATAS vs Market Solutions

Comparison Table

Feature Sardine Abnormal Feedzai Stream PATAS
Auto pattern discovery ❌ ✅ (LLM) ❌ ❌ ✅ (data-driven)
Transparent rules ✅ ~ ✅ ✅ ✅
Per-rule metrics ✅ ❌ ~ ❌ ✅
Safety profiles ❌ ❌ ❌ ❌ ✅
On-premise ~ ❌ ~ ❌ ✅
General-purpose ❌ (fintech) ❌ (email) ❌ (fintech) ✅ ✅
Developer API-first ✅ ~ ~ ❌ ✅

Detailed Comparison

Sardine

  • Focus: Fintech fraud detection
  • Strengths: Transparent rules, per-rule metrics
  • Limitations: No automatic pattern discovery, fintech-specific, limited on-premise
  • PATAS Advantage: Automatic discovery, general-purpose, full on-premise

Abnormal Security

  • Focus: Email security
  • Strengths: LLM-based pattern discovery
  • Limitations: Email-specific, SaaS-only, no per-rule metrics, no safety profiles
  • PATAS Advantage: Data-driven discovery (not LLM-dependent), general-purpose, on-premise, per-rule metrics

Feedzai

  • Focus: Fintech fraud prevention
  • Strengths: Transparent rules, some on-premise options
  • Limitations: Fintech-specific, no automatic discovery, limited per-rule metrics
  • PATAS Advantage: Automatic discovery, general-purpose, comprehensive per-rule metrics

Stream

  • Focus: General-purpose content moderation
  • Strengths: General-purpose, transparent rules
  • Limitations: No automatic discovery, SaaS-only, no per-rule metrics, no safety profiles
  • PATAS Advantage: Automatic discovery, on-premise, per-rule metrics, safety profiles

What Makes PATAS Unique

No other product combines all of these features:

  1. ✅ Automatic pattern discovery from historical logs (not manual rule writing)
  2. ✅ Transparent rule generation (SQL/regex-like, not black-box models)
  3. ✅ Per-rule metrics (precision, recall, coverage for each rule)
  4. ✅ Safety profiles (Conservative/Balanced/Aggressive tiers)
  5. ✅ On-premise / self-hosted option (not SaaS-only)
  6. ✅ General-purpose (not limited to email or fintech)
  7. ✅ Developer API-first (not just UI for non-technical teams)

When to Use PATAS

PATAS is ideal when you need:

  • Transparency: You need to explain why messages were flagged
  • Control: You want per-rule metrics and safety profiles
  • Privacy: You need on-premise processing
  • Scale: You have large volumes of historical logs to analyze
  • Flexibility: You need a general-purpose solution (not domain-specific)
  • Developer-friendly: You prefer API-first over UI-only tools

PATAS may not be ideal if:

  • You need real-time per-message LLM analysis (use LLM filters)
  • You prefer black-box ML models (use traditional ML classifiers)
  • You only need simple keyword blocking (use basic regex rules)
  • You require a SaaS-only solution (PATAS is on-premise-first)

Integration with Existing Systems

PATAS is designed to complement, not replace, existing anti-spam systems:

  • Use PATAS for pattern discovery and rule generation
  • Use your existing ML/LLM for real-time classification
  • Combine signals from both systems for final decisions
  • Use PATAS metrics to validate and improve your ML models

This hybrid approach gives you the best of both worlds: automatic discovery (PATAS) + real-time classification (ML/LLM).

Last Updated: 2025-01-15

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