ISAF Logger
Instruction Stack Audit Framework
Automatic compliance logging for AI systems. Add 3 lines of code, get EU AI Act-ready documentation with cryptographic verification. Works with PyTorch, TensorFlow, and scikit-learn.
Why ISAF Logger?
AI regulations are here. EU AI Act, Colorado AI Act, NYC Local Law 144 all require documentation of how your AI systems were trained. ISAF automates this.
3 Lines of Code
Add compliance logging to any ML training pipeline with minimal code changes. Works with your existing workflow.
Full Stack Coverage
Automatically logs Layer 6 (Framework), Layer 7 (Data), and Layer 8 (Objectives) of the instruction stack.
Cryptographic Verification
SHA-256 hash chains prove lineage integrity. Tamper-evident audit trails that regulators trust.
Compliance Ready
Maps directly to EU AI Act Article 10 & 11, NIST AI RMF, ISO 42001, and Colorado AI Act requirements.
Framework Agnostic
Works with PyTorch, TensorFlow, JAX, and scikit-learn. Auto-detects your ML framework.
Flexible Storage
SQLite for local development, MLflow integration for production. Export to JSON anytime.
Quick Start
Get compliance logging in under 5 minutes
1. Install
2. Add to Your Code
import isaf
# Initialize ISAF (one line)
isaf.init()
# Add decorators to your training functions
@isaf.log_data(source="customer_data", version="3.2.1")
def load_training_data():
return pd.read_csv("data.csv")
@isaf.log_objective(
name="binary_crossentropy",
constraints=["fairness < 0.05"]
)
def train_model(data):
model = create_model()
model.fit(data)
return model
# Run training as normal
data = load_training_data()
model = train_model(data)
# Export compliance report (one line)
isaf.export("compliance_report.json")What Gets Logged
Layer 6: ML Framework
Framework versions, CUDA availability, default parameters, numerical precision
Layer 7: Training Data
Data source, version, shape, dtypes, missing values, preprocessing operations
Layer 8: Objective Function
Loss function, mathematical form, constraints, hyperparameters, justification
Cryptographic Hash Chain
Every layer is linked with SHA-256 hashes. Tamper-evident audit trail.
Regulatory Compliance Mappings
ISAF automatically maps your logged data to specific regulatory requirements
EU AI Act
- Article 10 - Data Governance
- Article 11 - Technical Documentation
NIST AI RMF
- MEASURE-2.2 - Evaluation metrics
- GOVERN-1.1 - AI policies
ISO 42001
- Section 8.4 - Control of externally provided AI
Colorado AI Act
- SB24-205 - Impact Assessment Documentation
CLI Tools
Inspect and verify lineage files from the command line
Inspect
View formatted report of lineage file with audit ID, timestamp, and logged layers.
Verify
Verify cryptographic integrity of hash chain. Confirms data hasn't been tampered with.
Export
Export lineage from SQLite database to ISAF-compliant JSON format.
Research Foundation
ISAF is based on peer-reviewed research published in academic literature
Published Whitepaper
The Instruction Stack Audit Framework (ISAF): A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers
KC, S. (2025). Version 1.0. Zenodo. DOI: 10.5281/zenodo.18080355
This framework addresses the fundamental traceability gap in AI governance by providing technical specifications for documenting the full instruction stack from hardware substrate to emergent behavior. Includes a 127-checkpoint audit protocol and cryptographic verification methodology.
Ready to Add Compliance Logging?
ISAF Logger is open source and free to use. Get started in minutes.
Built by HAIEC - Holistic AI Ethics & Compliance