Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

UPDATE: Active Exploitation CVE-2026-32996 of Veeam Agent

On September 14, 2026, public technical details and proof-of-concept (PoC) exploit code were released for CVE-2026-32996, increasing the likelihood of exploitation attempts against affected Veeam Agent for Microsoft Windows deployments. CVE-2026-32996 is a local privilege escalation vulnerability in Veeam Agent for Microsoft Windows version 13.0.1.2067 and affects all earlier version 13 builds.

Coinflow on programmable money and compliance at scale - S3E5

In this episode, we dive into the payments revolution with Daniel Lev, CEO and Co-Founder of Coinflow. Daniel brings a rare blend of expertise: he started in large-scale digital transformation at PwC, moved into product at Amount (the Chicago-based fintech unicorn), and previously co-founded and exited a Web3 sports gaming platform before launching Coinflow.

The Linux AI blind spot: 7 exfiltration points your DLP can't see

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.

Sumo Logic MCP server: bringing SIEM and log data into Claude and other AI clients

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.

Run LimaCharlie AI Sessions on the model you choose

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.

Identity Lifecycle Management: Process, Stages, Benefits, and Best Practices

Identity lifecycle management is the process of managing a user's digital identity and access from the day they join an organization to the day they leave. It covers account creation, role changes, permission updates, and deprovisioning, and it applies to employees, contractors, service accounts, and increasingly, AI agents. Get it wrong, and you end up with two failure modes: new hires waiting days for access, and former employees who still have it. Both cost money. Only one of them makes headlines.

Just-in-Time Provisioning vs. Just-in-Time Privilege: What's the Difference?

Just-in-Time Provisioning and Just-in-Time Privilege sound almost identical, but they solve different identity management challenges. Let’s say a new software engineer joins your team. They need access to Slack, Jira, GitHub, and other essential apps. Instead of having an IT admin create their account manually, the application creates the account when the employee signs in through your Identity Provider (IdP). That is JIT provisioning.

From Triage to Full Coverage: The Shift AI Agent Security Took in August

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.

When One AI Model Fails Many Companies at Once

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.