Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As you’re reading this, it’s possible an agent you’ve never heard of is reading your files. But where did it come from? What systems can it access? And who’s responsible for its oversight? Today, employees are using AI and agents to expand what they can accomplish and how work gets done. And the barriers to innovation have never been lower — with natural language and no-code tools, anyone in your organization can build a new agent in the span of an afternoon.

Shadow AI now contributes to one in five data breaches, and Linux endpoints, where most developers work, largely lack DLP enforcement. That means engineers can upload code to AI tools, copy files to USB or Bluetooth devices, sync data to personal cloud storage, and move files through network shares undetected. HIPAA, PCI DSS, GDPR, and CMMC hold organizations accountable regardless of this coverage gap. Netwrix Endpoint Protector extends content-aware inspection and device control to Linux.

More security and operations work now happens inside an AI client instead of a dedicated console. That shift creates a gap for any platform that isn’t part of the conversation. Every time an analyst needs to triage an insight or check a log, they have to leave the AI client and go open a different tool. Sumo Logic closes that gap with a Model Context Protocol (MCP) server.

Co-founder and COO AI Sessions in the LimaCharlie web application now run on OpenAI, Google Gemini, and OpenRouter models in addition to Claude. Connect your own credentials, pick a provider per session profile, and the session behaves the same way regardless of which model is doing the work. Sessions run on Claude by default. Beyond that, you can connect any of the following with your own credentials.

Security teams evaluating an AI agent security platform tend to ask the same question after the first demo: will this keep up? Agentic AI changes shape every few weeks, with new frameworks, new coding agents, and new ways for an agent to reach a tool or a credential. A platform that covers today's stack and stalls on next quarter's isn't much of a bet.

Cyber insurance works because losses across a book are mostly independent. One insured suffering ransomware tells you little about the next, so a portfolio of many policies is more predictable than any single one. ‍ Shared AI dependencies break that assumption in a specific way. Where a large share of a book depends on the same foundation model or the same inference infrastructure, a single failure produces simultaneous claims across insureds with no commercial relationship to each other.

A modern storefront can look perfectly healthy while malicious JavaScript works underneath: siphoning affiliate revenue, hijacking searches and clicks, tampering with analytics, or asking a remote server what to execute next. Pages load, products appear, and checkout works — yet the browser may be quietly doing something the site owner never authorized. That is the blind spot our Client-Side Security machine learning (ML) model is built to expose.

Every digital banking transaction today, whether it’s a fund transfer, a loan approval, or a balance check through a fintech app, runs through an API. API security for banking means protecting these connections from attacks that go far beyond simple hacking attempts: fraud rings probing for account access, bots automating fake transfers, and partners quietly overstepping the access they were given.

How KomodoSec’s AigentX Penetration Tester reverse engineered client-side encryption, turned the bypass into reusable Burp tooling, and used that access to validate a real business-logic flaw in a production mobile application. Client-side secrecy failed. Server-side trust became the real vulnerability. THE CHALLENGE A mobile app with TLS, certificate pinning, and a second encryption layer.