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6 articles from Crelis on Large Language Models. Most recent: “An AI Agent Security Policy: What Belongs In One”.
A practical policy separates agent proposals from permission, names approval states, tests material changes, and keeps evidence for disputed actions.
OWASP gives teams a dated field reference for agentic AI risk, not proof that a live action was authorised.
OWASP maps agentic AI risks; it does not prove who authorised a payment, record change, or refund.
An LLM judge can return a different verdict on the same facts tomorrow; a deterministic policy engine returns the same decision each time and can name the rule that made it — that difference decides w
Autonomous agents can make decisions that a legal team may struggle to defend. Speed is a poor substitute for security. Non-deterministic systems carry inherent risk.
The gap between an AI's proposal and an enterprise's permission is where catastrophic liability lives. Trust is a systemic vulnerability. You recognize that LLM agents exhibit unpredictable emergent b