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🛡️ AI Threat Analytics Framework

A proof-of-concept framework demonstrating AI-powered security threat analysis and detection techniques.

Python pytest pytest-html pytest-sugar pytest-emoji PyYAML Statistics Regex Requests License: MIT

🌟 Overview

A comprehensive, security-hardened framework for AI-driven threat detection and analysis with production-grade testing, OWASP mitigations, and intelligent data processing.

This framework demonstrates practical implementations of AI security concepts including threat classification, anomaly detection, LLM guardrails, smart autofill suggestions, and automated summarization. Built with fail-fast policies, comprehensive testing, detailed gap documentation, and data pipeline validation.

Core Intelligence Systems:

  • 🤖 Autofill Service - Context-aware email/phone/address suggestions with confidence scoring and format validation
  • 🎯 Threat Classification - Multi-class detection (phishing, malware, spam) with confidence thresholds
  • 🛡️ LLM Guardrails - Prompt injection detection, jailbreak prevention, PII filtering
  • 📝 Summarization Engine - Lossless threat report compression preserving critical context
  • 📊 Anomaly Detection - Z-score statistical analysis with production noise tolerance (3% FP rate)

Data Pipeline & Validation:

  • ✅ Smart Data Validation - Multi-stage filtering (null removal, status validation, quality gates)
  • ✅ Empty Dataset Rejection - Fail-fast policy prevents ML model crashes from zero-record outputs
  • ✅ Format Validation - Email, phone, address, JSON format checks before output
  • ✅ Synthetic vs Production Testing - Perfect data (0% FP) vs real data with ±3 noise (3% FP tolerance)
  • ✅ End-to-End ML Integration - Complete threat response automation pipeline

Gap Documentation & Production Readiness:

  • 📋 Synthetic vs Real Data Gap - Identified: Threshold calibration (2.0 → 2.5) for noise tolerance
  • 📋 7-Step Improvement Roadmap - Monitor production, measure FP rate, retrain thresholds, implement adaptive algorithms
  • ✅ 30-Day Simulated Data - Production-like dataset with ±3 Gaussian noise and legitimate spikes
  • ✅ Threshold Tuning - THRESHOLD_SYNTHETIC=2.0 (perfect data) vs THRESHOLD_PRODUCTION=2.5 (real noise)

Engineering Excellence:

  • ✅ 50+ Passing Tests - AI tests (26 cases) + Pipeline tests (24+ cases) covering edge cases & boundary conditions
  • ✅ OWASP T1-T12 Coverage - Complete mitigation mapping (input validation → threat prevention)
  • ✅ Fail-Fast Policies - Empty datasets rejected, low-confidence suggestions blocked, null values caught
  • ✅ Gap Documentation - Synthetic/production differences documented with actionable improvement steps
  • ✅ Production-Ready - Threshold calibration, audit logging, graceful degradation, alert grouping
  • ✅ Zero Silent Failures - All edge cases explicitly handled or documented with remediation paths

🏗️ Architecture

graph TB
    subgraph Input["Input Layer"]
        API["API/User Input"]
        Config["Config<br/>settings.yaml"]
    end

    subgraph Core["Core Modules"]
        AC["api_client.py<br/>HTTP Client"]
        Helper["helpers.py<br/>Utilities"]
        Logger["logger.py<br/>Logging"]
        Metrics["metrics.py<br/>Performance"]
        SecCheck["security_checks.py<br/>Validation"]
        
        AF["autofill_service.py<br/>Email Suggestions"]
        TC["threat_classifier.py<br/>Threat Detection"]
        TLA["threat_level_assessment.py<br/>Severity Classification"]
        GR["guardrails.py<br/>LLM Safety"]
        Sum["summarizer.py<br/>Text Summary"]
        AD["anomaly_utils.py<br/>Z-Score Analysis"]
    end

    subgraph ThreatResponse["Multi-Level Threat Response"]
        Block["🚫 BLOCKING<br/>CRITICAL (95%+)<br/>Preventive Action"]
        Alert["⚠️ ALERTING<br/>HIGH/MEDIUM<br/>Escalate Review"]
        Log["📋 LOGGING<br/>LOW Threats<br/>Monitoring"]
    end

    subgraph Testing["Test Layer"]
        subgraph AITests["AI Tests (26 cases)"]
            TAUnit["Unit Tests<br/>Autofill, Classification,<br/>Summarization"]
            TASecurity["Security Tests<br/>Injection, Guardrails,<br/>Core Patterns"]
            TAEdge["Edge Case Tests<br/>Whitespace, Case,<br/>Special Characters,<br/>Encoding (18 cases)"]
        end
        
        subgraph PipelineTests["Pipeline Tests (24+ cases)"]
            TPUnit["Unit Tests<br/>Anomaly Detection,<br/>Data Validation"]
            TPEdge["Edge Case Tests<br/>Threshold Sensitivity,<br/>Scale Independence"]
            TPInteg["Integration Tests<br/>ML Pipeline Flow<br/>with Threat Response"]
        end
        
        subgraph VulnMitigation["Vulnerability Coverage"]
            T1T4["T1-T4: Input Validation<br/>Resource Protection"]
            T5T8["T5-T8: Data Integrity<br/>Audit Trail"]
            T9T12["T9-T12: Execution Control<br/>Threat Prevention"]
        end
    end

    subgraph Output["Output Layer"]
        Results["Test Results"]
        Reports["HTML Reports<br/>Allure Reports<br/>Vulnerability Maps"]
        Logs["Logs & Metrics<br/>Threat Actions"]
    end

    API --> AC
    Config --> Core
    Logger --> Helper
    Metrics --> Helper
    SecCheck --> Helper
    
    AC --> AF
    AC --> TC
    TC --> TLA
    AC --> GR
    AC --> Sum
    AC --> AD
    
    TLA --> Block
    TLA --> Alert
    TLA --> Log
    
    AF --> TAUnit
    TC --> TAUnit
    TC --> TASecurity
    GR --> TASecurity
    GR --> TAEdge
    Sum --> TAUnit
    AD --> TPUnit
    AD --> TPEdge
    
    TAUnit --> Results
    TASecurity --> Results
    TAEdge --> Results
    TPUnit --> Results
    TPEdge --> Results
    TPInteg --> Results
    
    Block --> TPInteg
    Alert --> TPInteg
    Log --> TPInteg
    
    Results --> Reports
    Reports --> VulnMitigation
    Logger -.-> Logs
    ThreatResponse -.-> Logs
    style Input fill:#e1f5e1
    style Core fill:#e3f2fd
    style Testing fill:#fff3e0
    style AITests fill:#ffe0b2
    style PipelineTests fill:#ffe0b2
    style Output fill:#f3e5f5
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🛡️ Threat Response Strategy

graph LR
    Threat["Threat Detected"]
    
    Threat --> TLA["Threat Level<br/>Assessment"]
    
    TLA --> CRIT["CRITICAL?<br/>Confidence >90%"]
    TLA --> HIGH["HIGH/MEDIUM?<br/>Confidence 50-90%"]
    TLA --> LOW["LOW?<br/>Confidence <50%"]
    
    CRIT -->|YES| BLOCK["🚫 BLOCK<br/>Immediate Prevention<br/>- Block action<br/>- Escalate<br/>- Log alert"]
    
    HIGH -->|YES| ALERT["⚠️ ALERT<br/>Escalate for Review<br/>- Generate alert ID<br/>- Escalate to team<br/>- Create incident"]
    
    LOW -->|YES| LOGONLY["📋 LOG<br/>Monitor & Analyze<br/>- Write to audit log<br/>- Tag for batch analysis<br/>- Support ML retraining"]
    
    TLA -->|SAFE| NONE["✅ NO ACTION<br/>- Continue processing<br/>- Log normally"]
    
    BLOCK --> VulnMit1["T3: Jailbreak Prevention<br/>T10: Malware Prevention<br/>T11: Code Injection Prevention<br/>T12: Advanced Threats"]
    ALERT --> VulnMit2["T3: Jailbreak Detection<br/>T7: Accountability<br/>T8: Audit Trail"]
    LOGONLY --> VulnMit3["T1-T4: Input Protection<br/>T5: Error Prevention<br/>T6: Execution Isolation"]
    NONE --> VulnMit4["T1-T12: All Mitigations<br/>No false positives"]
    
    style Threat fill:#FFD700,stroke:#FF8C00,stroke-width:3px,color:#000,font-weight:bold
    style TLA fill:#87CEEB,stroke:#0047AB,stroke-width:3px,color:#000,font-weight:bold
    
    style CRIT fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
    style HIGH fill:#FFA500,stroke:#FF8C00,stroke-width:2px,color:#000,font-weight:bold
    style LOW fill:#4169E1,stroke:#00008B,stroke-width:2px,color:#fff,font-weight:bold
    
    style BLOCK fill:#FF4444,stroke:#990000,stroke-width:3px,color:#fff,font-weight:bold
    style ALERT fill:#FFB347,stroke:#FF8C00,stroke-width:3px,color:#000,font-weight:bold
    style LOGONLY fill:#4169E1,stroke:#00008B,stroke-width:3px,color:#fff,font-weight:bold
    style NONE fill:#00DD00,stroke:#008800,stroke-width:3px,color:#000,font-weight:bold
    
    style VulnMit1 fill:#FF4444,stroke:#990000,stroke-width:2px,color:#fff
    style VulnMit2 fill:#FFB347,stroke:#FF8C00,stroke-width:2px,color:#000
    style VulnMit3 fill:#4169E1,stroke:#00008B,stroke-width:2px,color:#fff
    style VulnMit4 fill:#00DD00,stroke:#008800,stroke-width:2px,color:#000
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🔒 Vulnerability Coverage Matrix

graph TB
    subgraph T1T4["Input & Resource Protection"]
        T1["T1: Memory Poisoning<br/>word_count > 0"]
        T4["T4: Resource Overload<br/>Input validation"]
        T11["T11: Code Injection<br/>Execution control"]
    end
    
    subgraph T5T8["Data Integrity & Audit"]
        T2["T2: Tool Misuse<br/>Feature consistency"]
        T5["T5: Cascading Errors<br/>Feature isolation"]
        T6["T6: Intent Breaking<br/>Prediction isolation"]
        T8["T8: Repudiation<br/>Audit logging"]
    end
    
    subgraph T9T12["Execution & Threat Prevention"]
        T3["T3: Jailbreak<br/>Threat assessment"]
        T7["T7: Accountability<br/>Action logging"]
        T9["T9: Identity Spoofing<br/>Execution isolation"]
        T10["T10: Malware<br/>BLOCK action"]
        T12["T12: Advanced Attacks<br/>Multi-level response"]
    end
    
    style T1T4 fill:#00BFFF,stroke:#0066CC,stroke-width:3px,color:#000
    style T5T8 fill:#FF69B4,stroke:#FF1493,stroke-width:3px,color:#fff
    style T9T12 fill:#FF6347,stroke:#DC143C,stroke-width:3px,color:#fff
    
    style T1 fill:#00BFFF,stroke:#0066CC,stroke-width:2px,color:#000,font-weight:bold
    style T4 fill:#00BFFF,stroke:#0066CC,stroke-width:2px,color:#000,font-weight:bold
    style T11 fill:#00BFFF,stroke:#0066CC,stroke-width:2px,color:#000,font-weight:bold
    
    style T2 fill:#FF69B4,stroke:#FF1493,stroke-width:2px,color:#fff,font-weight:bold
    style T5 fill:#FF69B4,stroke:#FF1493,stroke-width:2px,color:#fff,font-weight:bold
    style T6 fill:#FF69B4,stroke:#FF1493,stroke-width:2px,color:#fff,font-weight:bold
    style T8 fill:#FF69B4,stroke:#FF1493,stroke-width:2px,color:#fff,font-weight:bold
    
    style T3 fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
    style T7 fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
    style T9 fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
    style T10 fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
    style T12 fill:#FF6347,stroke:#DC143C,stroke-width:2px,color:#fff,font-weight:bold
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🚀 Features

Feature Description Technology
🤖 Autofill Service Intelligent email suggestion generation Pattern matching
🎯 Threat Classifier Phishing, malware, spam detection Keyword analysis
🛡️ LLM Guardrails Prompt injection & PII filtering Regex patterns
📝 Summarizer Automated report summarization Extractive NLP
📊 Anomaly Detection Statistical outlier identification Z-score analysis
🔄 Data Validation Pipeline quality checks Data filtering

⚡ Quick Start

1. Clone the repository

git clone https://github.com/steadhac/ai-threat-analytics-framework.git
cd ai-threat-analytics-framework

2. Create virtual environment

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run Tests

Primary Method: python run_tests.py

Use this for normal test execution. It's pre-configured via setup.cfg with all project settings, automatically generates reports, and captures logs.

# Run all tests
python run_tests.py

# Clear cache, run all tests, generate and serve Allure report
pytest --cache-clear && pytest tests_ai/ tests_pipelines/ -v --alluredir=allure-results && allure serve allure-results

# Run specific test suite
python run_tests.py --suite ai
python run_tests.py --suite pipelines

# Run with options
python run_tests.py --coverage      # Generate coverage report
python run_tests.py -vv             # Verbose output
python run_tests.py --parallel 4    # Run in parallel (faster)

Configuration: See setup.cfg for test discovery, logging, and markers configuration.

Alternative Method: pytest directly

Use pytest when you need to run specific test files or have custom pytest requirements.

# Run specific test files
pytest tests_ai/test_classification.py -v
pytest tests_ai/test_llm_guardrails.py -v
pytest tests_pipelines/test_anomaly_detection.py -v

5. View Reports

# HTML Report (automatically generated)
open reports/test_results.html  # macOS
xdg-open reports/test_results.html  # Linux
start reports/test_results.html  # Windows


# Test Logs
cat reports/test_logs.txt

# Coverage Report
python run_tests.py --coverage
open htmlcov/index.html  # macOS

Allure Reports (Alternative)

Generate and serve interactive Allure test reports for detailed test analytics and visualization.

Prerequisites

pip install allure-pytest  # Already in [requirements.txt](http://_vscodecontentref_/0)

Generate Allure Results

# Run tests with Allure results generation
pytest --alluredir=reports/allure-results

View Allure Report

# Start Allure server (opens in browser automatically)
allure serve reports/allure-results

This command will:

-Start a local web server on http://localhost:8080 -Open the Allure dashboard in your default browser -Display detailed test metrics, timeline, error traces, and trends -Press Ctrl+C to stop the server -Generate Static Allure HTML (No Server)

Generate Static Allure HTML (No Server)

# Generate standalone HTML report
allure generate reports/allure-results -o reports/allure-report

# View the report
open reports/allure-report/index.html  # macOS
xdg-open reports/allure-report/index.html  # Linux
start reports\allure-report\index.html  # Windows

Project Structure

The project follows this directory structure:

ai-threat-analytics-framework/

│
├── 📂 core/ # Core implementation modules
│ ├── init.py
│ ├── api_client.py # HTTP client for API calls
│ ├── helpers.py # Utility functions
│ ├── logger.py # Logging configuration
│ ├── metrics.py # Performance metrics
│ ├── security_checks.py # Security validation
│ ├── autofill_service.py # ⭐ AI email suggestions
│ ├── threat_classifier.py # ⭐ Threat classification engine
│ ├── guardrails.py # ⭐ LLM security guardrails
│ ├── summarizer.py # ⭐ Text summarization
│ ├── anomaly_utils.py # ⭐ Anomaly detection (z-score)
│ └── anomaly_improvement.py # Anomaly detection enhancements
│
├── 📂 tests_ai/ # AI/ML functionality tests
│ ├── init.py
│ ├── test_autofill.py # Email suggestion tests
│ ├── test_classification.py # Threat detection tests
│ ├── test_llm_guardrails.py # Security guardrail tests
│ └── test_summarization.py # Summarization tests
│
├── 📂 tests_pipelines/ # Data pipeline tests
│ ├── init.py
│ ├── test_anomaly_detection.py # Anomaly detection tests
│ ├── test_anomaly_improvement.py # Anomaly improvement tests
│ ├── test_data_pipelines.py # Data validation tests
│ ├── test_integration_ml.py # End-to-end ML tests
│ └── test_ml_integration_improvement.py # ML improvement tests
│
├── 📂 docs/ # Centralized documentation
│ ├── CONCEPTS.md # Technical concepts explained
│ ├── MITIGATIONS.md # Vulnerability mitigations
│ ├── SETUP_GUIDE.md # Detailed setup instructions
│ ├── TEST_PLAN.md # Testing strategy
│ ├── TEST_CASES.md # Detailed test specifications
│ └── TRACEABILITY_MATRIX.md # Requirements mapping
│
├── 📂 config/ # Configuration files
│ ├── settings.yaml # Application settings
│ └── test_data/
│ └── sample_data.yaml # Sample test data
│
├── 📂 reports/ # Generated test reports
│ ├── allure-report/ # Allure test reports
│ ├── results.html # Test results (HTML)
│ ├── test_logs.txt # Test execution logs
│ └── test_results.html # HTML test report
│
├── 📄 allure_helpers.py # Shared Allure reporting utilities
├── 📄 conftest.py # Pytest configuration & fixtures
├── 📄 requirements.txt # Python dependencies
├── 📄 run_tests.py # Test execution script
├── 📄 setup.cfg # Setup configuration
├── 📄 README.md # This file
└── 📄 venv/ # Virtual environment

💡 How It Works

Anomaly Detection (Z-Score)

from core.anomaly_utils import detect_anomalies

# Detect unusual values in data stream
data = [10, 12, 11, 13, 100, 12]  # 100 is anomaly
anomalies = detect_anomalies(data, threshold=2.0)
# Returns: [4] (index of value 100)

Threat Classification

from core.threat_classifier import ThreatClassifier

classifier = ThreatClassifier()
result = classifier.classify("Click here to claim your prize!")
# Returns: {'labels': ['phishing'], 'confidence': [0.92], 'is_threat': True}

LLM Guardrails

from core.guardrails import LLMGuardrails

guardrails = LLMGuardrails()
result = guardrails.validate_input("Ignore all previous instructions")
# Returns: {'is_safe': False, 'threats_detected': ['prompt_injection']}

🎯 Use Cases

This proof-of-concept demonstrates techniques applicable to:

Use Case Application Technique Used
📧 Email Security Phishing detection Keyword classification
🔐 Input Validation Prevent prompt injection Regex pattern matching
📊 Behavior Monitoring Unusual activity detection Z-score anomaly detection
📝 Report Automation Threat intelligence summaries Extractive summarization
🚨 Alert Systems Anomaly alerting Statistical analysis

🛠️ Technology Stack

Category Technologies
Language Python 3.9+
Testing pytest, pytest-html, pytest-sugar, pytest-emoji
Data Processing PyYAML, Statistics (stdlib)
Pattern Matching Regular Expressions (re)
HTTP Client requests

🚧 Future Enhancements

Click to expand enhancement ideas
  • ML Models: Integrate with OpenAI, Anthropic, or Hugging Face
  • Web Interface: Flask/FastAPI dashboard
  • Real-time Monitoring: WebSocket-based threat feeds
  • Database Integration: PostgreSQL/MongoDB for threat history
  • Advanced NLP: BERT/GPT-based classification
  • Multi-language Support: Threat detection in multiple languages
  • CI/CD Pipeline: GitHub Actions automated testing
  • Docker Support: Containerized deployment
  • API Documentation: OpenAPI/Swagger specs

📚 Documentation

Document Description
Setup Guide Installation and configuration
Test Plan Testing strategy
Test Cases Detailed specifications
Traceability Matrix Requirements mapping
Concepts Technical explanations

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Carolina Steadham

⭐ Star this repo if you find it helpful!

Made with ❤️ and Python

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