Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Employees paste code into ChatGPT. They drop customer lists into Gemini for a quick summary. They upload a contract to an AI note-taker before a meeting starts. None of it feels risky in the moment. But all of it can walk sensitive data straight out of your organization. AI DLP exists to close that gap.

Supply chain attacks are having a moment. Open-source malware detections jumped 73% in 2025. In the past year, the debug and chalk packages were backdoored, the tj-actions GitHub Action was compromised and pulled malicious code into thousands of pipelines, and the axios maintainer account was hijacked and used to distribute a RAT. Malicious releases also hit Zapier, ENS Domains, PostHog, and Bitwarden CLI. Every one of these attacks was preventable with controls that were available at the time.

Unknown block type "undefined", specify a component for it in the `components.types` option Every conversation about AI and early-career employees seems to start in the same place: will it weaken foundational skills by doing too much of the work? That's a reasonable concern. It's also not the question that ended up mattering most this summer.

Heath care organizations are under attack. That's not news. But how serious is the threat? Consider these numbers from the IBM Cost of a Data Breach Report 2025: Health care organizations need help, and managed service providers (MSPs) can drive revenue and grow their businesses by providing it. But working in health care isn't like working in other industries.

A nasty shock is hitting finance leaders across every industry right now: AI token bills that run ten, twenty, even a hundred times over what they forecasted, blowing holes straight through quarterly budgets. These leaders are all asking the same questions: How could this happen if they didn't approve it? Why didn't any of their systems alert them to the spike? And most importantly, what can they do now?

Construction projects depend on timely decisions. When a superintendent needs the latest drawing set, a project engineer must review submittals, or a project manager wants to reference lessons learned from a past project, access to accurate information directly impacts project outcomes. Yet many construction firms still struggle with information scattered across jobsite photos, RFIs, specifications, BIM models, emails, and project management systems.

An AI agent on a developer's laptop has read access to a code repository, a set of internal documents, and an external model. Nobody approved that specific combination, and nobody is watching what the agent does with it session to session. The agent is not malicious, however, it is doing exactly what it was configured to do. But, that configuration is the exposure, and most security teams do not have a way to see it, let alone stop it before sensitive data leaves the environment.