Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

An AI agent with access to a customer’s brokerage account can begin executing trades. Not because the customer asked. Because someone, somewhere upstream, slipped a hidden instruction into a tool the agent loaded at startup. The agent is doing exactly what it was told. Just not by the customer. This is not a hypothetical. It is the attack class that financial security teams have exactly zero legacy tooling to catch and it is arriving precisely as banks accelerate their agentic AI ambitions.

Your pipeline has an API testing stage. Your scanner runs on every build. A finding list comes back clean. And then something gets exploited in production that your pipeline ran past 47 times without flagging. Here's what happened: endpoint validation passed. Security didn't. They are not the same thing. Here's what that box doesn't capture: APIs don't fail in clean test environments.

On June 5, 2026, ServiceNow quietly pushed a security update to hosted customer instances. The fix, described in an internal knowledge base article, addressed a flaw that let unauthenticated users gain more access to ServiceNow-hosted data than they were ever supposed to have. No password. No credentials. The remediation itself tells the whole story: ServiceNow changed an endpoint configuration to restrict access to authenticated users only. Read that again.

What this milestone means for enterprise AI security - and why we built it. AI adoption inside the enterprise didn't slow down and wait for security to catch up. It accelerated. And nowhere is that more visible than in the rapid deployment of large language models like Claude across enterprise workflows. Customer support teams use it to summarize tickets. Legal teams use it to review contracts. Engineers use it to write and review code. Finance teams use it to draft reports.

AI agents do not create risk only when they hallucinate or produce an inaccurate answer. They create risk when they take the wrong action. A single user prompt can move through an application, reach an agent runtime, call a tool, trigger an MCP server, and touch a downstream API. By the time the action happens, the original request may be several layers away from the system that actually changes data, sends information, or executes a workflow. That is the problem security teams now face.

When an enterprise deploys an MCP-powered AI agent, such as a coding assistant, a customer workflow automaton, an IT helpdesk bot, something quietly dangerous happens at startup. The agent inherits the full permission set of the application that launched it. If the orchestrating app holds write access to a production database, the MCP agent does too. If it can call financial APIs, trigger deployments, or read HR records, the agent inherits all of that, without ever explicitly being granted those rights.

On April 27, 2026, a threshold was crossed that the internet had never hit before. Cloudflare Radar data confirmed that automated systems, such as bots, crawlers, and autonomous AI agents, now generate 57.4% of all HTTP requests for web content. Human traffic accounts for just 42.6%. What is accelerating this transformation is agentic AI: autonomous systems that browse, search, authenticate, and transact on behalf of users without any human intervention mid-task.

As enterprises adopt AI agents, two control points are becoming common: AI Gateways and MCP Gateways. They sound similar, but they solve different problems. An AI Gateway controls how applications interact with AI models. An MCP Gateway controls how AI agents interact with tools, systems, and data exposed through MCP. Both are useful. Neither is enough on its own.