The AI security signal, not the noise.
Concise field notes on emerging threats, defensive patterns, and what's actually working — written for teams shipping AI in production.
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Quantum-Safe Cryptography Meets AI: The Migration Nobody Budgeted For
Post-quantum migration was already a decade-long programme. AI systems add model weights, signing keys and agent identities to the inventory — and the deadline pressure is real.
Your AI Vendor's Vendors: The Fourth-Party Problem Nobody Contracted For
Your AI provider fine-tunes on someone else's model, hosts on someone else's cloud and labels data through someone else's workforce. Your risk assessment covered none of them.
Shadow MCP: The Connector Sprawl Nobody Is Inventorying
Model Context Protocol servers are multiplying across enterprises faster than anyone can catalog them. Each one is an unmonitored bridge between an AI model and a production system.
Harvest Now, Decrypt Later: The AI Data Gold Rush Has a Quantum Deadline
Adversaries are stockpiling encrypted AI training data and model traffic today, betting on quantum decryption tomorrow. Data with a long secrecy life is already at risk.
The AI Coding Assistant Is Now Part of Your Supply Chain
Attackers have noticed that the fastest way into a thousand codebases is through the tools that write them. Poisoned suggestions, compromised extensions and typosquatted models are the new dependency confusion.
Polymorphic AI Malware Is Learning to Evade EDR in Real Time
Malware that rewrites itself per host used to be slow and sloppy. Generative models made it fast, targeted and quiet — and signature-based detection is falling behind.
Your Vector Database Is Leaking More Than You Think
Embedding stores were deployed fast and secured late. Inversion attacks, over-broad retrieval scopes and missing tenant isolation are turning RAG pipelines into data-disclosure engines.
Synthetic Identities Are Passing Remote Onboarding at Scale
Fraud rings now combine generated faces, stolen data fragments and real-time deepfake video to defeat Know-Your-Customer checks that were designed for a pre-generative era.
On-Device Small Models Move the Data Problem, Not the Risk
Running the model on the phone solves egress and creates a new one: sensitive inference on unmanaged hardware, outside every existing control.