Koinic Labs: Central Compliance & Transparency Report Date: April 2026
Status: SME Provider (Research & Development Phase)
Copyright Policy (EU 2019/790) Koinic Labs respects the rights of content creators. In accordance with Article 4(3) of Directive (EU) 2019/790, we honor all machine-readable reservations of rights (TDM opt-outs). Our training pipelines are designed to exclude data from sources that have explicitly opted out of AI training.
Training Data Summary (Synthetic-First) The AXL Architecture models are trained using a Synthetic-First Methodology.
Source: Data is primarily generated through high-fidelity AI-driven instruction sets and code-generation pipelines.
Categories: Programming logic (Python, C++, Rust, Go), multi-scale reasoning, and cybersecurity defense patterns.
Curation: Automated filters and human-in-the-loop (HITL) checks are used to ensure data quality and architectural alignment.
- Intended Use & Boundaries (Liability Protection) To ensure safety and compliance, use of Koinic Labs models is subject to the following boundaries:
AXL-Secure & AXL-Debugger Series: Intended Use: Defensive cybersecurity augmentation, code auditing, and vulnerability patching assistance.
Human-in-the-Loop: These models are designed to assist human experts. They are NOT intended for autonomous deployment in critical infrastructure (e.g., power grids, healthcare, transport) without human verification.
Forbidden Use: Any offensive cyber-operations or unauthorized intrusion testing.
- Environmental Impact Koinic Labs prioritizes sustainability. By optimizing for CPU-first inference, our models significantly reduce the carbon footprint compared to standard GPU-intensive LLMs.
Training Efficiency: Typical runs average 0.0070 kg CO2.