Please try govc with your implementations! I've been testing it internally and the implications are vast. https://github.com/Caia-Tech/govc -Marvin
Enterprise-grade AI deployment pipeline with forensic audit capabilities for regulated industries
This repository is a demonstration and reference implementation, not an authoritative solution. AFDP is designed to showcase concepts and architectures for forensic-grade AI deployment pipelines.
We encourage you to:
- 🔧 Build your own custom systems based on these ideas
- 🎯 Adapt the concepts to your specific needs
- 🚀 Improve upon the architecture for your use cases
- 💡 Take inspiration, not implementation
This is one approach among many possible solutions. Use it as a starting point for your own innovation.
AFDP is a comprehensive microservices ecosystem that combines forensic-grade audit trails with production intelligence learning for AI/ML deployments. The platform monitors production systems to understand real-world impact sequences, automatically generating training data from actual production patterns while maintaining complete transparency and compliance.
Key Approach: AFDP captures cause-and-effect sequences in production, creating ML training datasets that reflect real-world behavior, not just synthetic tests.
Workflow Customization: Using Temporal, organizations can define their own collection workflows - what to monitor, when to collect, how to process. Combined with Git's cryptographic properties, this creates an immutable record of not just WHAT was collected, but HOW it was collected, ensuring complete workflow integrity.
Status: Active Development with Initial Implementations
AFDP follows a modular microservices architecture, designed to capture production intelligence and convert it into actionable insights:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Production │───▶│ AFDP Services │───▶│ Training Data & │
│ Systems │ │ (Analysis) │ │ Forensic Trails│
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
└──────────────────────┴───────────────────────┘
Real-World Impact Tracking
🔐 Notary Service [Alpha]
Enterprise-grade cryptographic notarization service providing:
- Cryptographic Signing - Vault-backed signing with enterprise key management
- Transparency Logs - Immutable records via Rekor/Sigstore integration
- Audit Trails - Forensic-grade evidence for regulatory compliance
- Multi-Protocol - REST API, gRPC, and Pulsar messaging support
🔍 Repository Analysis Service [Development]
Universal forensic analysis for any repository type:
- Security Investigations - Source code, digital evidence, financial records
- Threat Detection - AI-powered anomaly identification and threat assessment
- Legal Support - Chain of custody, evidence discovery, compliance monitoring
- Distributed Intelligence - Real-time selective sharing with stakeholders
- Real-time compliance monitoring
- Regulatory reporting automation
- Audit trail visualization
- 💼 Business & Enterprise Applications - Litigation prevention, compliance monitoring, risk management
- 🧠 Production Learning: Monitors real systems to understand cause-and-effect sequences
- 📊 Training Data Generation: Automatically creates ML datasets from production patterns
- 🔒 Forensic Grade: Immutable audit trails that cannot be repudiated or backdated
- 🎯 Impact Tracking: Correlates deployments with real-world business outcomes
- 🏛️ Multi-Framework Compliance: SOX, HIPAA, FedRAMP, PCI-DSS support
- 🛡️ Memory Safe: Built with Rust for security and performance
- 🔍 Verifiable: Public cryptographic receipts for independent verification
- 🏗️ Modular: Microservices architecture for scalability
- 📊 Observable: Comprehensive metrics, logging, and tracing
- 🌐 Distributed Network Intelligence: Choose what gets logged and instantly distribute findings to your stakeholders (legal teams, insurance, law enforcement, regulatory bodies) in real-time
- Model Deployment Approval - Cryptographic proof of approved AI models
- Data Lineage Tracking - Immutable record of data transformations
- Compliance Reporting - Automated audit trails for regulatory requirements
- Patient data handling verification
- AI diagnostic system approval trails
- Treatment algorithm audit logs
- Trading algorithm deployment evidence
- Risk model validation records
- Financial reporting audit trails
- Secure AI system deployment
- National security algorithm verification
- Cross-agency audit coordination
- Production Intelligence - Learn from real deployment impacts, not just tests
- Automated Training Data - Generate ML datasets from production sequences
- CI/CD Pipeline Integrity - Tamper-proof deployment records with impact correlation
- Infrastructure Changes - Track how changes affect real-world performance
- Incident Response - Forensic evidence with full cause-and-effect analysis
- Deployment Learning - Understand patterns that lead to success or failure
AFDP revolutionizes how organizations understand their production systems by:
Example: API Deployment Impact
1. Deploy new API version (v2.1.0)
2. AFDP tracks sequence:
- Latency increases by 15ms
- Error rate spikes to 0.3%
- Specific endpoints affected
- User sessions drop by 2%
- Revenue impact: -$1,200/hour
3. Automatically generates training data:
- Pattern: "This code change → This production behavior"
- Labels: Performance degradation, Revenue impact
- Context: Load patterns, infrastructure state- Causal Sequences: What happened, in what order, with what impact
- Real-World Labels: Actual business outcomes, not synthetic metrics
- Production Context: Load, state, dependencies during events
- Behavioral Patterns: How systems actually behave under stress
- Train ML models on real production behavior
- Predict deployment impacts before rollout
- Identify patterns that lead to incidents
- Optimize for actual business metrics, not just technical ones
AI Training Data Generation
AFDP creates a framework for generating training datasets that connect code changes to whatever production outcomes matter to YOUR system.
The Opportunity
Every organization has different production realities:
- E-commerce: Code → Response time → Cart abandonment → Revenue
- SaaS: Deployment → API latency → User churn → MRR impact
- Infrastructure: Change → Resource usage → Scaling events → Cost
- Your system: Your code → Your metrics → Your outcomes
What AFDP Provides
A framework to:
- Define what production data matters to you
- Capture cause-and-effect sequences automatically
- Create training data from YOUR production patterns
- Maintain cryptographic proof of data authenticity
The Potential
This could enable AI models trained on real production behavior rather than synthetic tests. What that means depends entirely on your use case - AFDP provides the plumbing, you define the intelligence.
The key insight: connecting code changes to production outcomes, whatever those outcomes are for your system. The rest is up to you.
- 📖 Notary Service - Complete notary service documentation
- 📖 Repository Analysis Service - Universal forensic analysis capabilities
- 🚀 DevOps Integration Guide - How AFDP fits into your production workflows
- 🔄 Workflow Customization - Design your own intelligence collection workflows
AFDP is designed with security as a foundational principle:
- Zero Trust Architecture - No implicit trust between components
- End-to-End Encryption - All communications encrypted in transit
- Immutable Audit Logs - Cryptographically signed evidence chains
- Secure Key Management - Integration with enterprise HSMs and Vault
- Memory Safety - Core services built in Rust to prevent memory vulnerabilities
This project is licensed under the MIT License - see the LICENSE file for details.
- 🐛 Bug Reports: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📧 General Inquiries: [email protected]