AI-Augmented DevSecOps Framework

An AI-enhanced security automation framework for cloud-native applications, featuring real-time threat detection and automated response capabilities. This implementation accompanies the IEEE SOSE 2025 paper: "AI-Augmented DevSecOps Pipelines for Secure and Scalable Service-Oriented Architectures in Cloud-Native Systems".

🎯 Key Achievements

• 95% attack detection rate with sub-2 second latency
• 98% accuracy retention over 6 months with adaptive training
• 10,000 events/sec throughput with optimized processing
• Complete open-source implementation with reproducible results

🚀 Features

Core AI Components

• LSTM Threat Detection Engine: 64-unit architecture with sliding window analysis
• Zero-Day Behavioral Analysis: Isolation Forest with 25+ behavioral indicators
• Security Orchestrator: Confidence-weighted conflict resolution
• API Security Validator: BOLA detection and fuzzing capabilities
• Business Logic Validator: Race condition and state violation detection

Integration & Deployment

• Kubernetes Native: NetworkPolicy generation and pod isolation
• CI/CD Integration: Jenkins pipeline with security gates
• Prometheus Monitoring: Custom metrics and alerting
• Helm Charts: Production-ready deployment
• Terraform Infrastructure: Complete IaC setup

📊 Performance Benchmarks

System	Precision	Recall	Latency(s)	Throughput(eps)	Resource Efficiency
Our Framework	0.95±0.02	0.94±0.03	1.5±0.2	10K	33% less CPU
Suricata 6.0.9	0.88±0.04	0.85±0.05	2.3±0.3	8K	Baseline
Prisma Cloud 22.12	0.92±0.02	0.89±0.03	1.9±0.3	9K	16% more CPU

Statistical significance: p < 0.05, 95% confidence intervals

🏗 Architecture

System Components

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   CI/CD Pipeline │    │  LSTM Detector  │    │ Zero-Day Engine │
│   (Jenkins)     │────│   (64 units)    │────│  (Behavioral)   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Security Gates  │    │  Orchestrator   │    │ Policy Engine   │
│ (SAST/DAST)     │────│ (Conflict Res.) │────│ (NetworkPolicy) │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Kubernetes    │    │   Prometheus    │    │  Message Bus    │
│   (Runtime)     │────│  (Monitoring)   │────│ (Event-Driven)  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📋 Prerequisites

• Python 3.8+ with asyncio support
• Docker 20.10+ and Docker Compose
• Kubernetes 1.19+ cluster access
• Jenkins 2.3x+ for CI/CD integration
• Prometheus 2.30+ for monitoring
• Helm 3.x+ for deployment
• Terraform 1.0+ for infrastructure

Hardware Requirements:

• CPU: 8+ cores (Intel/AMD x64)
• RAM: 16+ GB
• Storage: 100+ GB SSD
• GPU: Optional (NVIDIA Tesla V100 recommended)

🚀 Quick Start

1. Clone and Setup

git clone https://github.com/akshaymittal143/ai-devsecops-framework.git
cd ai-devsecops-framework

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configuration

# Copy example configuration
cp config/settings.yaml.example config/settings.yaml

# Edit configuration for your environment
vim config/settings.yaml

3. Deploy Infrastructure

# Deploy with Terraform
cd terraform/
terraform init
terraform plan
terraform apply

# Deploy with Helm
cd ../helm/
helm install ai-devsecops ./ai-devsecops-framework

# Or deploy with kubectl
kubectl apply -f deploy/kubernetes/

4. Start Services

# Development environment
docker-compose -f docker-compose.dev.yml up -d

# Production environment
docker-compose up -d

# Verify deployment
kubectl get pods -l app=ai-devsecops

💻 Usage Examples

Basic Framework Usage

from src.main import AIDevSecOpsFramework
import asyncio

async def main():
    # Initialize framework
    framework = AIDevSecOpsFramework()
    
    # Start monitoring
    await framework.start_monitoring()
    
    # Process security event
    event = {
        "timestamp": time.time(),
        "event_type": "api_call",
        "source_ip": "192.168.1.100",
        "endpoint": "/api/admin/users",
        "method": "GET"
    }
    
    result = await framework.process_event(event)
    print(f"Threat detected: {result['is_threat']}")
    print(f"Confidence: {result['confidence']:.2f}")
    print(f"Processing time: {result['processing_time_ms']:.1f}ms")

asyncio.run(main())

Component-Specific Usage

# LSTM Threat Detection
from src.detection.lstm_detector import LSTMDetector

detector = LSTMDetector(units=64, window_size=10, threshold=0.95)
threat_probability = detector.predict(sequence_data)

# Zero-Day Detection
from src.detection.zero_day_detector import ZeroDayDetector

zero_day = ZeroDayDetector()
zero_day.train_baseline(benign_data)
anomaly_score = zero_day.analyze_behavior(sample)

# Security Orchestration
from src.orchestration.orchestrator import SecurityOrchestrator

orchestrator = SecurityOrchestrator(
    strategy=ConflictResolutionStrategy.CONFIDENCE_WEIGHTED
)
action = await orchestrator.process_security_signal(signal)

📁 Repository Structure

ai-devsecops-framework/
├── src/                          # Core implementation
│   ├── detection/                # AI detection engines
│   │   ├── lstm_detector.py      # LSTM threat detection
│   │   ├── zero_day_detector.py  # Zero-day behavioral analysis
│   │   ├── attack_generator.py   # Synthetic attack generation
│   │   └── zero_day/             # Zero-day specific modules
│   │       └── behavioral_analyzer.py
│   ├── orchestration/            # Security orchestration
│   │   └── orchestrator.py       # Conflict resolution & coordination
│   ├── api/                      # API security components
│   │   ├── security_validator.py # API fuzzing & validation
│   │   └── test_runner.py        # Security test execution
│   ├── validation/               # Business logic validation
│   │   └── business_logic_validator.py
│   ├── pipeline/                 # Data pipeline components
│   │   ├── message_bus.py        # Event-driven communication
│   │   ├── policy_engine.py      # Dynamic policy generation
│   │   ├── preprocessor.py       # Data preprocessing
│   │   └── k8s_integration.py    # Kubernetes integration
│   └── telemetry/                # Monitoring & telemetry
│       └── collector.py          # Metrics collection
├── config/                       # Configuration files
│   ├── settings.yaml             # Main configuration
│   ├── logging.yaml              # Logging configuration
│   └── policies/                 # Security policies
│       └── network-policies.yaml # Kubernetes NetworkPolicies
├── deploy/                       # Deployment configurations
│   ├── kubernetes/               # K8s manifests
│   ├── terraform/                # Infrastructure as Code
│   └── helm/                     # Helm charts
├── evaluation/                   # Performance evaluation
│   └── benchmarking.py           # Comprehensive benchmarking
├── examples/                     # Usage examples
│   └── basic_usage.py            # Basic framework usage
├── models/                       # Model artifacts & results
│   └── results.md                # Detailed performance results
├── docs/                         # Documentation
│   └── API_REFERENCE.md          # Complete API reference
├── tests/                        # Test suites
├── notebooks/                    # Jupyter analysis notebooks
└── data/                         # Datasets & samples

🔧 Configuration

Core Settings (config/settings.yaml)

detection:
  model:
    type: lstm
    units: 64
    window_size: 10
    dropout_rate: 0.2
  threshold:
    anomaly: 0.95
    confidence: 0.85
  hyperparameters:
    learning_rate: 0.001
    batch_size: 64
    epochs: 100

orchestration:
  strategy: confidence-weighted  # conservative, majority, confidence-weighted
  retry_attempts: 3
  timeout: 30s
  component_weights:
    lstm_detector: 0.8
    zero_day_detector: 0.9
    api_validator: 0.7

monitoring:
  prometheus:
    scrape_interval: 15s
    retention: 30d
  logging:
    level: info
    format: json
  metrics:
    - ai_devsecops_threats_detected_total
    - ai_devsecops_detection_latency_seconds
    - ai_devsecops_model_accuracy

📊 Monitoring & Metrics

Prometheus Metrics

The framework exposes comprehensive metrics for monitoring:

# Threat detection metrics
ai_devsecops_threats_detected_total{component="lstm_detector"}
ai_devsecops_detection_latency_seconds{component="zero_day_detector"}
ai_devsecops_model_accuracy{model="lstm"}
ai_devsecops_false_positives_total

# Resource utilization
ai_devsecops_cpu_usage_percent
ai_devsecops_memory_usage_bytes
ai_devsecops_network_io_bytes_total

# Business metrics
ai_devsecops_events_processed_total
ai_devsecops_policy_actions_total{action="isolate_pod"}
ai_devsecops_api_vulnerabilities_detected{type="bola"}

Grafana Dashboards

Pre-configured dashboards available in monitoring/grafana/:

• Security Overview: High-level threat landscape
• Performance Metrics: Latency, throughput, resource usage
• Model Performance: Accuracy, drift, retraining status
• Incident Response: Policy actions, remediation status

🧪 Evaluation & Benchmarking

Run Comprehensive Evaluation

cd evaluation/
python benchmarking.py --full-evaluation --output results.csv

# Statistical validation
python statistical_validation.py --confidence 0.95

# Attack-specific evaluation
python attack_evaluation.py --attack-types container,api,privilege

Performance Testing

# Load testing
python load_test.py --events 10000 --rate 1000

# Latency benchmarking
python latency_benchmark.py --duration 300

# Resource profiling
python resource_profiler.py --profile-duration 600

🔬 Research & Validation

Statistical Rigor

• Cross-Validation: 10-fold stratified validation
• Confidence Intervals: 95% CI for all metrics
• Significance Testing: Paired t-tests (p < 0.05)
• Bootstrap Sampling: 1000 iterations for robustness

Datasets Used

• CloudStrike 2024: 2.1M flows (Container, API attacks)
• MITRE Cloud: 1.5M events (Privilege, Lateral attacks)
• Synthetic K8s: 500K traces (Pod, Mesh attacks)
• CICIDS2017: Network intrusion detection baseline

Hyperparameter Optimization

• Method: Bayesian optimization with TPE
• Trials: 100 optimization trials
• Search Space: LSTM units, learning rate, batch size, dropout
• Validation: 5-fold cross-validation

🛡️ Security Features

Attack Detection Capabilities

Attack Type	Detection Rate	False Positive Rate	Avg. Detection Time
Container Escape	96%	3%	1.2s
API Abuse (BOLA)	94%	4%	0.8s
Privilege Escalation	95%	5%	1.5s
Service Mesh Hijack	93%	6%	2.1s
Supply Chain Attacks	97%	4%	1.7s
Zero-Day Variants	85%	3%	1.8s

Automated Response Actions

• Pod Isolation: Immediate network quarantine
• Network Restriction: Granular traffic control
• Enhanced Monitoring: Increased telemetry collection
• Alert Generation: Multi-channel notifications
• Policy Enforcement: Dynamic rule application

🔄 Concept Drift & Adaptation

Adaptive Learning

• Sliding Window Retraining: Weekly model updates
• Accuracy Retention: 98% over 6 months
• Drift Detection: Statistical change point analysis
• Incremental Learning: Online adaptation capabilities

Adversarial Robustness

• Adversarial Training: ε-bounded perturbations
• Ensemble Methods: Multi-model voting
• Robustness Testing: FGSM, PGD, C&W attacks
• Improvement: 18% reduction in misclassification

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/ -v

# Code formatting
black src/ tests/
flake8 src/ tests/

# Type checking
mypy src/

Research Collaboration

This framework supports ongoing research in:

• Federated Learning: Cross-organization threat intelligence
• Service Mesh Security: Istio/Linkerd integration
• Explainable AI: Interpretable security decisions
• Edge Computing: Distributed deployment optimization

📄 Citation

If you use this framework in your research, please cite our paper:

@inproceedings{mittal2025ai,
  title={AI-Augmented DevSecOps Pipelines for Secure and Scalable Service-Oriented Architectures in Cloud-Native Systems},
  author={Mittal, Akshay},
  booktitle={IEEE International Conference on Service-Oriented System Engineering (SOSE)},
  year={2025},
  organization={IEEE}
}

📞 Support

• GitHub Issues: Report bugs and feature requests
• Documentation: Complete API reference
• Performance Results: Detailed benchmarks
• Email: amittal18886@ucumberlands.edu

📜 License

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

🙏 Acknowledgments

• University of the Cumberlands - Research support
• IEEE SOSE 2025 - Conference publication
• Open Source Community - Tool and library contributions
• CICIDS2017 Dataset - Evaluation baseline data

---

⭐ Star this repository if you find it useful!

Built with ❤️ for the cloud-native security community