Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Artificial intelligence is changing how a business handles its operations, and that too very rapidly. AI agents can easily read, analyze, and act on enterprise data in real time. This ease also brings serious risk. If not managed well, these systems can expose sensitive information, break compliance rules, or even make harmful decisions. Did you know that on average, the overall cost of a data breach reached $4.45 million in 2023?

Let’s be real. Shadow AI is already reshaping Shadow IT Security, whether organizations are ready or not. Chances are that your developers aren’t waiting for a formal RFP to start using AI. They’re already deep in the trenches, using Open WebUI to manage models or shipping entire projects through platforms like Lovable at a velocity that makes traditional AppSec look like it’s standing still.

Let's address the elephant in the room, or I should say … the AI in the security operations center (SOC). If you're an SOC analyst, you've probably heard the doom and gloom predictions — AI is coming for your job! AI will replace you! Start updating your resumes now! In all honesty, that probably is not the case. AI isn't going to take your job, but it will change how you do it — and that's brilliant news.

On 7th April 2026, Anthropic published a system card for an AI model we may never be allowed to use: Claude Mythos. This preview demonstrated a significant leap in capability over Anthropic’s previous Claude Model (Opus 4.6), and their Responsible Scaling Policy (RSP) v3.1 led to them making the decision to withhold it from general availability, serving as a "defensive only" asset.

AI agents need to authenticate with numerous systems, making AI authentication a crucial security boundary that determines blast radius, revocability, and long-term governance risk.

Sophos analysts are investigating the active abuse of QEMU, an “open-source machine emulator and virtualizer,” by threat actors seeking to hide malicious activity within virtualized environments. Attackers are drawn to QEMU and more common hypervisor-based virtualization tools like Hyper-V, VirtualBox, and VMware because malicious activity within a virtual machine (VM) is essentially invisible to endpoint security controls and leaves little forensic evidence on the host itself.

For years, conversations about AI security risks were framed as forward-looking. Organizations were told to prepare for a future where autonomous agents would act on their behalf, access sensitive systems, and make consequential decisions without human intervention at every step. That future, it turns out, is now.

For many engineering and security teams, NIST SP 800-218 (Secure Software Development Framework, or SSDF) compliance feels like a hurdle that is too difficult to overcome. To meet these and other emerging regulations and be effective in today’s DevSecOps environment, organizations are moving toward codifying these standards into machine-readable rules, also known as Policy as Code (PaC).

Proofpoint DLP and Trellix DLP are two notable data loss prevention solutions. In this blog, we’ll analyze both platforms in depth and see how they compare. We’ll also introduce Teramind as a compelling alternative that combines the best aspects of Proofpoint and Trellix, while offering additional tools that could increase your workforce’s safety and productivity.

SAST, or Static Application Security Testing, is a method of analyzing source code to find vulnerabilities before the application is deployed. It's a type of white box testing that scans the code without executing it, looking for weaknesses that could be exploited. SAST helps developers identify and fix security issues early in the Software Development Life Cycle (SDLC), potentially reducing costs and improving the overall security posture of the application.