Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Data leakage risks with DBHub MCP servers

Organizations keep their databases behind firewalls for a reason: the data inside is the data they can least afford to lose. A new class of AI middleware–Model Context Protocol (MCP) servers–exists specifically to reach into those protected systems on an AI model's behalf. One of them, DBHub, connects directly to SQL databases.

Coding Agents Are Moving Faster Than Security. Here's What CISOs Need to Know.

Coding agents have become one of the fastest-adopted AI technologies in the enterprise. They help developers write code, debug applications, automate repetitive tasks, and ship software faster than ever before. They also introduce a security challenge unlike anything most organizations have faced. Unlike traditional AI assistants that generate content, coding agents take action.

AI Control Platform vs. AI Firewall vs. AI Gateway: Clearing Up The Terminology

Editor's note: This article was originally published by Tim Erlin on LinkedIn. It has been republished here with the author's permission. It seems like every security vendor now sells "AI security." The WAF companies, the API gateway companies, the cloud platforms, the proxy startups: all of them have an AI story, and most of them have attached one of three labels to it. AI gateway. AI firewall. AI control platform. The terms often get used as if they're interchangeable, but they are not.

What Is Firewall Configuration and Why Is It So Important?

Firewall configuration is the set of rules, policies, and settings that define how a firewall behaves. Without configuration, a firewall is hardware and software waiting for instructions. With configuration, it becomes a control that determines what traffic is permitted, what is blocked, and what gets inspected before a decision is made. Get the configuration right, and the firewall does its job. Let it drift, and you have the appearance of protection without the substance of it. This is not an edge case.

AI Governance on AWS: Discover, Observe, and Control AI in Production

AI adoption within AWS environments is accelerating faster than most security and governance programs. AI agents, APIs, MCP servers, and model integrations are entering production across cloud environments, often without centralized visibility or runtime controls. In this webinar, you’ll see how teams can discover AI workloads across AWS accounts, understand what AI systems are actually doing at runtime, enforce policy in real time, and generate continuous governance evidence without slowing engineering teams down. The session focuses on practical operational capabilities for AI systems already running in production.

Why You Must Still Review AI Code

In this video, we break down why skipping code reviews is a massive mistake that will ultimately slow you down, leave you vulnerable, and compromise your system's accountability. We dive into three concrete reasons why reviewing AI-generated pull requests actually makes you a faster, safer developer, including a real-world story of a production bug caught in under 90 seconds. Resources Chapters.

How Organizations Can Assess and Manage AI-Related Risks

Organizations assess and manage AI-related risks by establishing a cross-functional governance framework, mapping risks based on impact and financial likelihood, and instituting continuous monitoring that connects AI asset discovery to risk quantification, compliance, and enforcement. The most effective programs treat AI risk management not as a one-time assessment but as a continuous, data-driven discipline that evolves alongside the AI systems it governs. ‍

What Server Do You Need to Run an AI Language Model?

Running your own AI model sounds exciting, but one question appears almost immediately: what kind of server do you actually need? A small language model can run on a personal workstation, while a large 70B parameter model may require enterprise-grade GPUs and expensive infrastructure. Choosing the wrong hardware can lead to wasted money, unnecessary complexity, or disappointing performance.

What AI Governance Tools Exist in the Market Today

‍AI governance tools are software platforms designed to help organizations manage AI risks, ensure regulatory compliance, and enforce responsible AI use across the machine learning lifecycle. The market has expanded rapidly, and in 2026 it includes tools spanning compliance automation, model observability, data governance, infrastructure security, and integrated risk quantification.