AI Security . AI Architect · Cloud Native Engineer

Most teams scope an AI agent by its job title: “Invoice processing agent.” “Customer support agent.” “Research agent.” That’s too broad. The right question is: what is the most damaging action this agent could take, and what must be true before it can take it? Consider a hypothetical invoice agent. Its task is to read invoices, match them to purchase orders, and prepare payments. It has access to the procurement system, vendor records, and payment workflow. Then an invoice contains hidden instructions telling the agent to update the vendor’s bank details before processing the payment. The agent isn’t “hacked” in the cinematic sense. It is following text that entered its working context. The failure is more basic: a low-trust input was allowed to influence a high-consequence action. The agent’s task was legitimate. Its authority was not properly bounded. Scope authority by consequence Reading an invoice and changing a bank account are different classes of action. They shouldn...

🤖 Top Agentic AI, AI Agents & Governance Articles Your twice-weekly roundup of the latest in Agentic AI , AI agents , and agent access, permission, governance & audit — covering agent frameworks, MCP/tool-use, agent identity, non-human access control, AI governance and agent security. How we detect frontier model misalignment Source : OpenAI News OpenAI uses layered monitoring to detect frontier model misalignment and unauthorized agent behavior so responders can intervene before incidents escalate. OpenAI and ServiceNow expand partnership across enterprise workflows Source : OpenAI News OpenAI and ServiceNow expand their partnership to bring OpenAI models and agents into ServiceNow and ServiceNow workflows into ChatGPT and Codex. Legora is building the agentic operating system for legal work Source : OpenA...

🤖 Top Agentic AI, AI Agents & Governance Articles Your twice-weekly roundup of the latest in Agentic AI , AI agents , and agent access, permission, governance & audit — covering agent frameworks, MCP/tool-use, agent identity, non-human access control, AI governance and agent security. How we detect frontier model misalignment Source : OpenAI News OpenAI uses layered monitoring to detect frontier model misalignment and unauthorized agent behavior so responders can intervene before incidents escalate. OpenAI and ServiceNow expand partnership across enterprise workflows Source : OpenAI News OpenAI and ServiceNow expand their partnership to bring OpenAI models and agents into ServiceNow and ServiceNow workflows into ChatGPT and Codex. Legora is building the agentic operating system for legal work Source : OpenA...

Human approval fails when it arrives too early, too late, or without enough context. A person clicks “Approve” on a request they cannot properly inspect. The agent then acts on changing information. The control exists on paper, but it does not meaningfully control the action. A better design is simple: let the agent prepare the work, but pause before it creates a commitment the business cannot easily reverse. Consider an AI agent preparing a supplier payment. It can collect the invoice, match it to a purchase order, check the requested amount, and prepare the transaction. That work is low-risk and mostly reversible. The payment itself is different. It moves money and creates an external commitment. That is where the agent should stop and show: What it plans to do Which systems and records it used Why the action crossed the approval threshold What could change if the action is delayed Who is authorized to approve it The approval should be attached to that exact proposed acti...