AI Security Lab probes how far AI work can be trusted

The series opens from a shift the author describes: AI is moving from something that answers questions to something that receives work from humans and executes it. In the older pattern, a person read the AI's text and decided what to do, so an error did not directly change a system's state. Once tools are attached, the AI can read files, run commands, search the web, call APIs, and change data, and an AI Agent can loop through judgment, tool selection, execution, and re-judgment.

The author's framing is that whether AI can be trusted is too large a question, because humans also make mistakes and no one removes people from work on that basis. What matters instead is what work is assigned, under what conditions, and to whom. This is why the Lab separates two problems: how well AI can judge, and how the Application handles that judgment. On the Application side the author lists Validation, Authorization, Access Control, Transaction, Idempotency, and Recovery, and notes that even with these controls, the question of whether the AI's original judgment was wrong still remains, leading later experiments toward Known Answer, Consistency, and Claim Verification.

The author is careful not to pre-decide that some work must never be given to AI, or that AI is safe because it is convenient, and instead repeats a cycle of hypothesis, experiment, failure, cause analysis, countermeasure, and re-experiment. Whether the Lab produces a usable boundary for delegation is likely to hinge on how well that loop surfaces failure conditions rather than one-off attack successes.