VANE-SPACE-SLA

VANE-SPACE-SLA (v1.0): A Deterministic Zero-Trust Anomaly Detection and Telemetry Engine for Mission-Critical Spacecraft Operations Published by: MD ABUL HOSSAIN Category: Artificial Intelligence / Space Systems / Telemetry & SLA Security Repository: https://github.com/AnticipatedD/VANE-SPACE-SLA Modern spacecraft operations depend on reliable telemetry and consistent data validation. From autonomous satellite constellations to deep-space communication pipelines, incoming data must be evaluated carefully before it informs operational decisions. Changing operating conditions, noisy sensor readings, and telemetry drift can complicate anomaly detection, contributing to false alerts or overlooked deviations. VANE-SPACE-SLA (v1.0) is an open-source framework designed to address these challenges through deterministic telemetry evaluation, configurable validation gates, and structured prompt grounding for AI-assisted workflows. It is intended to support payload verification before data informs downstream state changes, with applications spanning orbital telemetry, thermal monitoring, and LLM-assisted ground control reporting. The framework uses a multi-gate validation architecture to accommodate different telemetry requirements and levels of operational sensitivity. Incoming payloads are assessed by the zero-trust telemetry engine using configurable Strict, Moderate, or Soft gate rules. Evaluation results can then support SLA monitoring records and ground control logs. The Strict Gate requires full schema compliance, precise range validation, and cryptographic signature verification. It is intended for sensitive telemetry associated with systems such as attitude control and propulsion. The Moderate Gate evaluates telemetry against statistical moving averages and configured tolerance limits, allowing bounded variation under defined operating conditions. The Soft Gate provides a more permissive evaluation path for legacy sensors or noisy, low-frequency telemetry feeds. Gate selection and acceptance thresholds should reflect the requirements of the monitored subsystem. To support the integration of generative AI into ground control workflows, VANE-SPACE-SLA includes a dedicated prompt builder, strict_prompt_builder.py. This component is designed to constrain prompts to supplied context and apply explicit rules when preparing AI-assisted operational reports. These controls aim to reduce unsupported outputs and improve consistency, while generated content still requires appropriate verification. The project also includes pytest test suites and continuous integration through GitHub Actions. Environment-based configuration through .env support and deterministic unit tests support repeatable checks across deployment environments. These practices contribute to maintainability and reproducibility, while operational readiness depends on validation against the requirements of the intended mission environment. To get started, clone the repository using “git clone https://github.com/AnticipatedD/VANE-SPACE-SLA.git”, navigate to the project directory using “cd VANE-SPACE-SLA”, and install the dependencies using “pip install -r requirements.txt”. Run the test suite with coverage reporting using “pytest --cov=. --cov-report=term”. Additional configuration and deployment guidance is available in the project documentation. VANE-SPACE-SLA offers three pricing tiers for researchers, developers, and commercial operators. The Community tier is free and includes open-source code, the full test suite, the local telemetry engine, and the standard prompt builder. The Pro Developer tier is priced at $2,999 per month and includes multi-gate validation presets, extended logging, priority updates, and custom hooks. The Enterprise SLA tier offers custom pricing for tailored SLA compliance monitoring, custom gate rules, and dedicated 24/7 mission support. Applicable licensing terms, availability, and support scope are provided in the pricing documentation. Documentation and guides are available at https://github.com/AnticipatedD/VANE-SPACE-SLA/tree/main/docs/additional_informations. Pricing and licensing information can be found at https://github.com/AnticipatedD/VANE-SPACE-SLA/tree/main/pricing. The live framework portal is available at https://anticipatedd.github.io/VANE-SPACE-SLA, and the source code is hosted at https://github.com/AnticipatedD/VANE-SPACE-SLA. As spacecraft operations become increasingly autonomous, consistent telemetry validation and traceable evaluation rules become more valuable. VANE-SPACE-SLA brings deterministic validation, configurable telemetry gates, and AI prompt grounding into a shared framework for development and evaluation. Future development can extend this foundation through broader test coverage, subsystem-specific validation rules, and additional telemetry integrations. For questions, contributions, or collaboration on aerospace telemetry and AI-assisted operational tools, connect through GitHub: https://github.com/AnticipatedD.