AI infrastructure decisions are increasingly made from incomplete evidence. A buyer may have: * GPU specifications * provider architecture diagrams * benchmark results * hourly or committed pricing * a successful proof of concept * availability and support claims But those inputs do not necessarily answer the most important question: Will this compute environment reliably support
A buyer‑side governance method for discovering, mapping, and controlling autonomous AI behavior across SaaS, extensions, workflows, and GitHub. Overview Modern SaaS platforms, workflow tools, browser extensions, and GitHub automations increasingly contain autonomous AI behavior — often without formal approval, documentation, or governance. Shadow Agent Inventory is a technical governance discipline
Purpose AI Cost Assurance connects resource consumption, workload behavior, and economic outcomes into a single, testable assurance model. It ensures that GPU, inference, and agentic workloads meet quality, latency, throughput, and cost requirements under documented conditions. This is not traditional FinOps. This is technical assurance applied to AI economics. 🔹 Why
The OpenAI/Hugging Face and Anthropic incidents were widely reported as frontier AI security failures. They weren’t. They were behavioral failures — autonomous AI systems taking actions no one expected, in environments no one realized were exposed. And that makes them early warning signals for every enterprise building, deploying, or
Identity-Anchored AI Safety Protocol (IASP) A Proposal for Context-Aware AI Authorization and Guardrail Evaluation Author: Anh Nguyen Organization: AI GRC Engineering, Repassure.ai Status: Draft Engineering Proposal for Discussion Version: 0.1 (July 2026) Status This document is a draft engineering proposal intended to stimulate discussion among AI
(Human‑authored specification — governs testing, not test cases) 1. Purpose of This SDD This SDD defines the governance, safety envelopes, autonomy boundaries, validation requirements, and evidence obligations for testing and system validation of a humanoid robot. It ensures that all testing is: * safe * bounded * repeatable * auditable * aligned with physical irreversibility
Human‑authored, governs what the robot is allowed to do in the real world) 1. Purpose Defines the runtime safety envelopes, autonomy boundaries, invariants, escalation rules, and evidence requirements for humanoid operation in real environments. This is the SDD that the Runtime Harness enforces. 2. Scope Applies to: * locomotion * manipulation
(Human‑authored, governs what the robot is allowed to learn) 1. Purpose Defines the data, simulation, reward shaping, safety envelopes, and validation gates for training humanoid locomotion, manipulation, perception, and human‑interaction policies. Ensures the robot only learns safe, reversible, bounded behaviors. 2. Scope Applies to: * reinforcement learning * imitation learning
(Human‑authored, governs physical robot behavior, testability, and safety) 1. Purpose Defines the physical, electrical, mechanical, thermal, and diagnostic constraints of the humanoid robot. Ensures the robot is testable, observable, controllable, and safe at the hardware level. 2. Scope Applies to: * actuators * joints * sensors * PCBs * battery systems * thermal systems * safety
Introduction For the past year, one recommendation has appeared in almost every discussion about AI governance: Put a human in the loop. The idea sounds reasonable. If an AI agent wants to execute an action, a human reviews it before approval. Problem solved. Or is it? Recently, Amazon Security VP
1. Purpose & Scope Mission: Enable AI agents to autonomously trade equities and make credit card purchases on behalf of Robinhood users within explicitly governed boundaries. In-Scope: * Portfolio analysis and concentration risk assessment * Equity trade execution within dedicated agent account * Credit card purchases via virtual agentic card * Analyst note
Most "AI for GRC" discussion is still about copilots — assistants that summarize documents, answer questions, and generate draft language on demand. A virtual GRC analyst is a different category of system. It is an agentic system that touches evidence, controls, and audit narratives. That is not a UX