When Your AI Becomes Chief of Staff: A STRIDE Threat Analysis of the Lobster/OpenClaw Personal AI Agent System
I constructed a complete data flow diagram with five trust boundaries and ran a systematic STRIDE-per-element analysis. The result: 46 enumerated threats — 11 Critical, 17 High, 14 Medium, and 4 Low. Seven have no mitigation at all.
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The Context Trap: How Claude Code's Session Memory Can Narrow Your Solution Space
The longer and more technically dense a coding session becomes, the more the model's attention and generative probability distributions get pulled toward patterns, idioms, and architectural choices already present in that context.
When Your AI Becomes Chief of Staff: A STRIDE Threat Analysis of the Lobster/OpenClaw Personal AI Agent System
I constructed a complete data flow diagram with five trust boundaries and ran a systematic STRIDE-per-element analysis. The result: 46 enumerated threats — 11 Critical, 17 High, 14 Medium, and 4 Low. Seven have no mitigation at all.
Instinct vs. Deliberation: How Anthropic and OpenAI Train Their Models to Follow the Rules — And Why It Matters for Enterprise AI
The most consequential technical distinction in enterprise AI isn't about which model is smarter. It's about how each model was taught to be safe — and what that means when you deploy agents that make real-world decisions.
Agentic AI
AI systems that can operate autonomously, making independent decisions and taking proactive actions to achieve goals without constant human direction—acting as intelligent agents with planning, tool-use, and reasoning capabilities.
When Your AI Becomes Chief of Staff: A STRIDE Threat Analysis of the Lobster/OpenClaw Personal AI Agent System
I constructed a complete data flow diagram with five trust boundaries and ran a systematic STRIDE-per-element analysis. The result: 46 enumerated threats — 11 Critical, 17 High, 14 Medium, and 4 Low. Seven have no mitigation at all.
Information Governance
The framework of policies, processes, and standards ensuring proper management, security, compliance, and quality of organizational information throughout its lifecycle.
Generative AI and Information Governance: A Practitioner's Guide
The information governance landscape is undergoing its most significant transformation since the advent of cloud computing. Generative AI—the same technology powering tools like ChatGPT and Claude—is rapidly moving from novelty to necessity in how organizations manage, classify, and protect their data assets. For those of us who have
Why AI Prompts Are Not Official Records: A Formal Logic Analysis for Compliance Professionals
The explosive adoption of AI tools across enterprises has created an urgent compliance question that keeps information governance professionals awake at night: When employees interact with AI systems, do their prompts constitute official records?
Building Semantic AI Agents with Custom DSLs
The Problem: Lost in Translation Every interaction with an AI agent involves translation—from human intent to machine action. Today, most AI systems attempt this translation in one giant leap, interpreting natural language directly into system operations. It's like playing telephone tag with your entire business logic. ConsiderLatest Articles
The Context Trap: How Claude Code's Session Memory Can Narrow Your Solution Space
The longer and more technically dense a coding session becomes, the more the model's attention and generative probability distributions get pulled toward patterns, idioms, and architectural choices already present in that context.
When Your AI Becomes Chief of Staff: A STRIDE Threat Analysis of the Lobster/OpenClaw Personal AI Agent System
I constructed a complete data flow diagram with five trust boundaries and ran a systematic STRIDE-per-element analysis. The result: 46 enumerated threats — 11 Critical, 17 High, 14 Medium, and 4 Low. Seven have no mitigation at all.
Instinct vs. Deliberation: How Anthropic and OpenAI Train Their Models to Follow the Rules — And Why It Matters for Enterprise AI
The most consequential technical distinction in enterprise AI isn't about which model is smarter. It's about how each model was taught to be safe — and what that means when you deploy agents that make real-world decisions.
