Build Secure and Compliant AI agents and MCP Servers. YC W23
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Updated
Jun 6, 2025 - Python
Build Secure and Compliant AI agents and MCP Servers. YC W23
Open source AI governance platform with support for ISO 42001, ISO 27001 and EU AI Act. Join our Discord channel: https://discord.com/invite/d3k3E4uEpR
Monitor, detect, and analyze security threats and Shadow AI in your LLM applications with comprehensive analytics and real-time alerting.
Governance, Risk & Compliance documentation aligned to FedRAMP Moderate, NIST SP 800-53 Rev. 5, DoD RMF, and NIST AI RMF. Includes policies, risk register, vendor assessment, continuous monitoring, vulnerability management, and AI governance materials.
Hardened Public Release of KAIROS invocation governance framework. Includes invocation terms, ethical compliance clauses, regulatory mapping, and sample outputs. Licensed under CC BY-NC-ND 4.0. License: Do not auto-generate via GitHub. Use hardened License.txt
đź”— Signet Protocol - Trust Fabric for AI-to-AI Communications. Middleware for secure, auditable, and verifiable AI interactions through Verified Exchanges (VEx), Signed Receipts, and HEL egress control.
Replication package of "Simplifying Software Compliance: AI Technologies in Drafting Technical Documentation for the AI Act".
My journey from law to code: Projects in Privacy-Preserving ML, LegalTech automation, and regulatory compliance systems.
Ethical AI Validator detects bias and assesses fairness in AI models with statistical parity analysis, real-time monitoring, and automated GDPR/AI Act compliance reporting. Python 2.7+ compatible.
ai-praxis-tool
Rule-based chatbot prototype exploring conversational AI through compliance-inspired prompts and ethical design logic.
This project delivers a prototype for AI-driven compliance and secure data sharing in cross-border media collaboration. It integrates Hyperledger Fabric for permissioned blockchain and Apache Kafka for scalable event streaming.
Lightweight AI Governance Risk Assessment tool to score AI models across key risk factors (data quality, bias, privacy, explainability, robustness) with PDF report generation using Streamlit.
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