Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every cloud security vendor launched an AI-SPM dashboard in the past year. Strip away the branding and most of them are presenting the same concept: a new posture management layer for AI workloads. Sit through four demos in the same week and a practical question surfaces. The dashboards look broadly similar — pie charts of findings, compliance tags, a list of AI assets, a severity ranking. Why, then, do the tools underneath cover completely different parts of the problem?

In early April 2026, Arctic Wolf began tracking a large-scale device code phishing campaign impacting organizations across multiple regions and sectors. Similar to the widespread “Riding the Rails” campaign first observed in late March by Huntress, the threat actors were observed abusing OAuth device code flow to trick victims into providing authentication codes and obtain initial access into victim environments.

We’ve been collaborating with others to explore how agentic commerce and enterprise agents will work. Alfonso Gómez-Jordana Mañas, co-founder of Crossmint shares his thoughts on the current state of payment security and how compliance mechanisms like KYC and AML need to evolve to support agentic commerce. OpenClaw provided users with an open-source framework to launch AI agents.

As a solutions architect, building out customer demo environments is part of the job. I regularly spin up lab scenarios to support evaluations and proof-of-concept work — and if you've done this before, you know it can eat up days of your life. So when I recently decided to refresh my homelab and migrate to Proxmox, I saw it as the perfect opportunity to put AI-assisted infrastructure automation to the test. The goal?

Your CIEM report came back clean this morning. Every AI agent in the cluster is exercising its granted permissions — no idle roles, no service accounts with broad scope and a handful of API calls behind them, nothing that looks obviously over-provisioned. The dashboard is green, and by the diagnostic your tool was built on, it should be.

A Tier 1 bank’s security architecture already spends heavily on detection. On one side sits the financial surveillance stack — fraud scoring platforms processing thirty thousand transactions an hour, AML monitoring watching money movement patterns, DLP engines scanning data in transit, payment anomaly detection tuned by a decade of production signal.

The Mythos-ready briefing names secrets rotation, NHI governance, and honeytokens as critical controls. Zero-days don't replace credential attacks; they accelerate them. Credential security deserves to move up every CISO's priority list.

Generative AI security is the practice of protecting the data that flows into AI systems, and the outputs those systems produce, from leaks, attacks, and unauthorized access. Every organization using AI today has the same blind spot. Sensitive data enters an AI pipeline, and most security teams have no visibility into where it goes next. An employee pastes a customer record into ChatGPT. A developer submits code containing API keys to an AI debugging tool.