Artificial Intelligence / Analysis
The next AI frontier is who gets to act
As assistants become agents, permissions, accountability and recovery become the real architecture of trust.
THE TOPIC FILE
AI security
Security changes when software interprets untrusted information as instructions. This collection examines prompt injection, data integrity, model provenance and the controls surrounding connected AI systems.
Stories in this collection
Artificial Intelligence / Explainer
Why a fluent AI answer can still be wrong
The most persuasive error is the one that sounds ordinary. Reliable AI use starts with a verification process matched to the consequence.
AI Security / Field guide
An AI red team should test the system you will deploy
A clever jailbreak is only one signal. Useful adversarial testing follows the data, permissions and consequences of the full application.
AI Security / Explainer
The data an AI learns from is a security boundary
Data poisoning can target training, adaptation and retrieval. Provenance and controlled updates matter long before a suspicious answer appears.
Artificial Intelligence / Explainer
Multimodal AI needs more than eyes
Reading a picture, a chart and a document requires different checks. A model that accepts all three has not necessarily understood them.
AI Security / Analysis
Shadow AI is an information problem before it is a tool problem
Employees adopt useful assistants faster than policies can name them. A workable response starts with the data and the task.
AI Security / Analysis
Prompt injection begins where instructions and information meet
When an AI assistant reads the outside world, it must decide what is evidence and what has authority. That boundary is the security problem.
AI Security / Field guide
Your AI supply chain includes more than a model
Weights, tokenizers, plugins, dependencies and serving infrastructure form one operating system of trust. Inventory them together.