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Major US banks have found AI agents operating inside customer accounts, signed in with credentials customers handed over themselves. Nothing was breached. The agents authenticated correctly, and some of them still did things nobody asked for. That gap between having access and having authority is the agentic AI security problem every enterprise running agents now owns.

Deciding to buy open source support for Apache ActiveMQ is the easy part. Working out which vendor can actually keep a broker alive at three in the morning is considerably harder, because nearly every vendor in this market is vague in exactly the same places.

This Apache ActiveMQ CVE guide is the maintained reference for every significant Apache ActiveMQ vulnerability: what it does, how it is scored, which versions it affects, which release fixes it, and whether it is being exploited.

When a CVSS 10.0 vulnerability is published, the expectation is that affected software gets patched within days. When it involves an open-source broker that processes business-critical transactions in enterprise environments, the expectation is that the security teams responsible for those brokers treat it as an emergency. “The attacks exploit a maximum-severity security flaw in Apache.

An AI agent doesn’t just answer questions—it takes actions in real systems. As teams across the enterprise build their own agents, governance can’t be an afterthought. Learn how agents work, why they create new risks, and what controls are needed before they act, not after.

McKinsey's 2026 tech report calls agentic AI the 'connective tissue of the enterprise.' But agents that act at machine speed need governance at machine speed too. Static controls won't work for dynamic agents—active, in-flow guardrails must run before the action, not after.

When every employee is building AI agents, seeing what they did afterward isn't enough. AgentIQ's in-flow governance runs inside the agent's execution flow—pausing for human approval, enforcing policies by value, masking PII, and stopping runaway agents before costs spiral.

When every employee is building AI agents, seeing what they did afterward isn't enough. AgentPatrol's in-flow governance runs inside the agent's execution flow—pausing for human approval, enforcing policies by value, masking PII, and stopping runaway agents before costs spiral.

Treehouse Dataflow Toolkit moves mainframe data from Db2, VSAM, and IMS through Apache Kafka pipelines into Snowflake and AWS targets. meshIQ keeps the streaming layer visible and stable. Together they give data science teams continuously updated enterprise data for AI and ML.

Enterprises are running AI agents nobody has counted, holding credentials nobody reviewed, taking actions nobody can audit. AgentPatrol governs what agents are allowed to do at the moment of action—inside the execution flow—not after the fact through a gateway watching from outside.