The Agentic AI Governance Assurance & Trust Engine (AAGATE) translates the high-level functions of NIST AI RMF into a living, Kubernetes-native architecture.
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Enhancing Healthcare Cybersecurity: Integrating FAIR Risk Quantification With Zero Trust Principles
The adoption of the FAIR model was pivotal in transforming our cybersecurity strategy into a measurable, business-aligned framework.
Abstract: The growing need for geospatial data analysis highlight's location privacy issues. Although existing technologies such as differential privacy and location obfuscation can be relatively ...
AI can supercharge work, but without guardrails it can mislead fast — so humans, governance and smart frameworks still need ...
As organizations accelerate the adoption of Artificial Intelligence, from deploying Large Language Models (LLMs) to integrating autonomous agents and Model Context Protocol (MCP) servers, risk ...
As AI takes center stage, the real win is making sure we can actually trust its decisions — and that’s why verifiable AI is ...
As adoption accelerates, governments and international bodies are grappling with the challenge of establishing guardrails that ensure safe, transparent and equitable AI deployment. However, in the ...
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring ...
Abstract: This research paper presents a secure IoT data management framework that integrates Tinkercad, Flask, and ThingSpeak, while adhering to the NIST guidelines for data security. The Flask ...
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