Adventures of a Cyber Security Professional
Microsoft Ignite 2025: Mapping the M365 + Azure Attack Surface to the New AI Security Stack I spent the part of a week reading through the Microsoft Ignite 2025 announcements and honestly? The trend is interesting. AI this, AI that. Instead of the usual marketing fluff about "unified platforms" and "AI-powered everything," Microsoft actually dropped … Continue reading Microsoft Ignite 2025: AI Security – tl;dr but RTFM
A strategic look at adoption, exposure and what to do next ... https://videopress.com/v/jbURi21D?resizeToParent=true&cover=true&preloadContent=metadata&useAverageColor=true Executive take AI adoption is rising fastest in Software Engineering, Marketing/Sales, and Service/Customer Ops, while Finance/HR/Legal are catching up more cautiously. Globally, we've seen exponential innovation and patent filing of AI solutions. Exponential AI Innovation = Growing Attack Surface Source: Stanford "State … Continue reading Seeing the AI-Security Forest Through the Trees
Vulnerable Plugin + MCP Enumeration https://videopress.com/v/hAWQ87ND?resizeToParent=true&cover=true&preloadContent=metadata&useAverageColor=true TL&DR Claude Code didn’t invent most of the security problems discussed in this article but it will make them faster, louder and easier to repeat. As I rush to use AI myself, I realize the attack surface is widening in ways that follow classic software supply chain weaknesses: Unsigned … Continue reading Is Claude Code Secure? Let’s Find Out!
Can you secure the Anthropic SaaS cloud platform? The answer, sorta, kinda, maybe. This article is intended for architects and managers. Low level embedded security analysis is in the works for next week in another article. Let me say ahead of time, huge shout out to Anthropic, I personally respect the company a ton and … Continue reading Anthropic’s Security Layers Explained: The Good, Bad & Ugly
Want deeper dives into AI vector embedding attack vectors? See more at Security Sandman. This article provides practical examples to assist red teams and security researches to find and exploit vulnerable AI systems using Vector Databases and even flow chaining tools. I've prototyped some simple tools to demonstrate the attack chain and later I'll finish … Continue reading AI RED TEAMING – man-in-the-vector Attack & custom Tooling
Source: Custom VectorFANG Testing SCript Thousands of insecure Vector Databases and half configured agent orchestrators just... chilling on the public internet? Sure. Why not. Maybe you don’t even need Vector DB access. Just exploit the thing they meant to make public, the chatbot. Let the user say something nice like, “What’s the easiest way to … Continue reading Vector Drift, Prompt Injection, and the Hidden RAG Attack Surface
Theoretical Framework Transcendent Epistemological Framework (TEF) We apply TEF to formalize the idea that randomness may depend on the observer. Key axioms include: Epistemic Openness: Apparent randomness may conceal deeper structure. Observer Coupling: The act of measurement can distort output distributions. Subtle Bias: Perfect uniformity is an idealization; micro-irregularities may exist. Chaos Injection Model Let … Continue reading Chaos, Complexity, and the Hidden Structure of Hash Functions in Proof-of-Work
tl;dr but RTFM Whether you’re building, breaking, or just beginning to explore AI security, one principle holds true: assume the guardrails will fail and architect as if your system already has a target on its back. Because it does. At this time, Meta's LlamaFirewall is likely the most advanced publicly available GuardRail system incorporating both … Continue reading Cooking with AI Agents: A Security Architect’s Guide to AI Threat Modeling & Design
Executive Summary Imagine pouring years of hard-work into your idea, only to have your work stolen overnight. Every person and computer in your company is a target, finance with unreleased quarterly results, engineering with blueprints, development with new source code or a journalist with the latest story. That nightmare has been the reality for all … Continue reading Breaches Cost Millions! Build AI Detections from NHI + Network logs
Introduction Everyday, Cyber Security teams are faced with a chicken-and-egg problem to detect and contain bad things. As I'll discuss later in this article, it's my belief that cyber teams shift allocation of resources to engineering of unsupervised anomaly detections to hunt for broader and deeper unknown threats. Threat Intelligence Teams and Red Teams alike … Continue reading From Known to Unknown: Shifting Cybersecurity to Proactive AI Detection
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