MCP enterprise security: Model Context Protocol in production

The Model Context Protocol makes it easy to give AI agents tools. That is exactly why it needs controls. What MCP is, the four risks that matter, and the controls that address them.

AI & Agents

VeloPhex Security

Human in the loop AI agents: designing approvals that hold up

Approvals are the most effective control for AI agents, and the easiest to get wrong. Which tool calls need one, why each approval should be single-use, how timeouts behave, and what auditors will ask for.

VeloPhex Product

RPA platform buyer's guide 2026: criteria, scoring and vendors

A vendor-neutral framework for choosing an RPA platform: eight criteria, the questions that expose real differences, a weighted scoring table and a named-vendor comparison you can take into your next evaluation.

Enterprise Automation

What is agentic automation? A practical guide for enterprises

Agentic automation pairs AI agents that reason with robots that act, under governance that decides what either may do. Here is what it is, how it differs from RPA, IPA and chatbots, and how to start safely.

RPA audit trail: what auditors actually need from bots and agents

Execution logs answer the developer's question. An RPA audit trail answers the auditor's: who changed what, on whose authority, and can the record be trusted. Here is how to build one, including for AI agent runs.

Automation center of excellence without the bottleneck

A CoE should make good automation easy, not make all automation wait in line. How to split ownership between the center and business units, govern AI agents alongside bots, and tell whether it is working.

RPA vs AI agents: matching the pattern to the process

RPA and AI agents are not rivals. Robots execute known rules, agents interpret messy inputs, and people decide what matters. Here is how to tell which step needs which, with a decision table.