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Epic AUD-EVOLVE-001: Future Enhancements & AI Insights

Status: 💡 Research (15% complete)
Owner: AI Team
Target: Q3 2026

Epic Description

This epic explores next-generation enhancements for the Audit Trail Platform (ATP), focusing on the integration of AI-powered analytics and predictive intelligence. It introduces anomaly detection and intelligent audit correlation models to identify unusual behavior, forecast compliance risks, and surface actionable insights for administrators. The objective is to evolve ATP from a passive logging system into a proactive intelligence platform.

Epic Objectives

  • Develop a proof of concept (PoC) using Azure AI Inference and OpenAI models
  • Detect behavioral anomalies and compliance drift in near real time
  • Introduce predictive intelligence for risk scoring
  • Define roadmap for Audit Trail Platform v2 with AI integration
  • Ensure AI models comply with data privacy and explainability requirements

Features

Feature AUD-AI-ANOM-001: Anomaly Detection 💡

Status: Research (PoC phase)
Target: Q2 2026

Tasks: - 🔄 PoC using Azure AI Inference (70% complete) - ⏳ Integrate event stream with AI model - ⏳ Evaluate model performance & explainability

Current Work: - Training anomaly detection model on 6 months of historical data - Testing with synthetic anomaly injection - Evaluating Azure ML vs Azure AI Inference endpoints

Preliminary Results: - Anomaly detection accuracy: 89% on test dataset - False positive rate: 8% (acceptable for alerting) - Inference latency: 320ms per batch (target: <500ms)

Feature AUD-AI-INS-001: Predictive Audit Intelligence 💡

Status: Research (planning)
Target: Q3 2026

Tasks: - 💡 Roadmap for ATP v2 with AI correlation (research) - ⏳ Prototype risk scoring engine - ⏳ Integrate predictive insights into Admin UI

Research Focus: - Identify key risk factors for compliance violations - Design risk scoring algorithm (0-100 scale) - Evaluate vector databases for semantic search (pgvector, Qdrant)

Current Research Work

Phase 1: PoC Development (Current)

Timeline: October 2025 - December 2025

Activities: 1. 🔄 Train anomaly detection model on historical data 2. 🔄 Build inference pipeline consuming real-time events 3. ⏳ Validate accuracy and explainability 4. ⏳ Measure performance impact on platform

Success Criteria: - Detect 90%+ of simulated anomalies - False positive rate < 10% - Inference latency < 500ms - Explainable output (SHAP/LIME integration)

Phase 2: Production Pilot (Q1 2026)

Timeline: January 2026 - March 2026

Planned Activities: 1. Deploy AI pipeline to staging environment 2. Enable for 5 beta tenants (opt-in) 3. Collect feedback on anomaly alerts 4. Iterate on model based on real-world data

Phase 3: GA Release (Q3 2026)

Timeline: July 2026 - September 2026

Planned Activities: 1. Integrate AI insights into Admin Console 2. Add risk scoring dashboard 3. Enable automatic anomaly alerting 4. Document AI features for compliance teams

Technology Evaluation

Technology Purpose Status Decision
Azure Machine Learning Model training & deployment ✅ Evaluated Selected for PoC
Azure AI Inference Real-time inference endpoint ✅ Evaluated Selected
OpenAI GPT-4 Semantic analysis (future) 🔄 Evaluating TBD
pgvector Vector search in PostgreSQL 🔄 Evaluating Under consideration
Qdrant Dedicated vector database ⏳ Planned Future evaluation

Research Questions

Open Questions

  1. Should we use supervised or unsupervised learning for anomaly detection?
  2. What is the acceptable false positive rate for compliance alerts?
  3. How do we explain AI decisions to auditors?
  4. What are the data privacy implications of AI processing?

Answers & Decisions

  1. Decision: Start with unsupervised (no labeled data available)
    ADR: ADR-0015 (pending)

  2. Decision: Target < 10% false positive for MVP
    Rationale: Acceptable for human review workflow

  3. Decision: Implement SHAP explainability library
    Rationale: Industry standard, integrates with Azure ML

  4. Decision: Process only metadata, never raw PII
    Rationale: Protects privacy while enabling analysis

Dependencies

Upstream (Depends On)

  • ✅ AUD-QUERY-001: Query APIs for historical data
  • ✅ AUD-OTEL-001: Event streaming infrastructure
  • ⏳ AUD-COMPLIANCE-001: Compliance profiles for validation

Downstream (Enables)

  • Future: Proactive compliance monitoring
  • Future: Automated incident triage
  • Future: Cost optimization recommendations

Risks & Mitigation

Risk Severity Mitigation Status
Model accuracy insufficient High Collect more training data, try ensemble models Active
Privacy concerns with AI High Process only aggregated metadata, get legal review Mitigated
Performance impact on platform Medium Run inference async in separate service Mitigated
Explainability requirements Medium Integrate SHAP library from start Planned
Budget constraints Low Start small with PoC, scale based on value Mitigated

Budget & Resources

Allocated Budget: $15,000 (Q4 2025 - Q1 2026)

Category Cost Notes
Azure ML Compute $5,000 Training and experimentation
AI Inference Endpoint $3,000 Real-time scoring
Storage (training data) $1,000 Historical audit data lake
Engineering Time $6,000 1 ML engineer @ 40% allocation

ROI Hypothesis: - Reduce compliance incident response time by 50% - Prevent 3-5 compliance violations per year - Estimated value: \(50K-\)100K annually

Success Metrics

Metric Target Current Status
Anomaly Detection Accuracy > 90% 89% (PoC) ⚠️ Near target
False Positive Rate < 10% 8% (PoC) ✅
Inference Latency < 500ms 320ms ✅
Model Explainability SHAP scores available Not yet ⏳
Beta Tenant Feedback > ⅘ satisfaction N/A ⏳

Recent Updates

2025-10-30: - 🔄 PoC model training 70% complete - 📊 Preliminary accuracy results: 89% - 💡 Evaluating Azure OpenAI for semantic search

2025-10-15: - 🔄 Synthetic anomaly dataset generated - 📝 Data privacy review completed (approved) - 🎯 PoC demo scheduled for November 15

2025-10-01: - 💡 Epic AUD-EVOLVE-001 initiated - 📋 Research plan approved by Architecture Board - 💰 Budget allocated for Q4-Q1

Next Steps

  1. Complete PoC model training - Target 90%+ accuracy
  2. Integrate SHAP explainability - Make decisions transparent
  3. Deploy to staging - Test with synthetic traffic
  4. Beta tenant onboarding - 5 volunteers for Q1 2026
  5. Document findings - ADR for AI architecture decisions

Next Review: 2025-11-15 (PoC Demo)
Contact: #atp-ai-research on Slack