Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI did not create data exposure. It changed the consequences of it. Sensitive data, excessive permissions, and broad access policies have long existed across cloud environments. In the AI era, those issues are no longer passive governance concerns. They directly influence what AI users, copilots, agents, and applications can access, process, and expose. AI has turned a posture problem into operational risk. The challenge is not simply that more data is available.

Despite three forum seizures, five administrator arrests across three operations, and the conviction of its founder, ShinyHunters remains active. The real story of ShinyHunters in 2026 is not just persistence, but the evolution of a cybercrime brand that adapts faster than defenders and law enforcement can respond. The 2025–2026 tactics make this persistence especially dangerous. Organizations using Salesforce, Salesloft Drift, Gainsight, or similar third-party SaaS integrations are at risk.

Agentic AI is evolving from assistants that answer questions into systems that can remember, use tools, call APIs, interact with SaaS applications, and improve over time. Hermes Agent, developed by Nous Research, reflects this shift as a self-improving agent that can create skills, persist knowledge, and build context across sessions., reflects this shift as a self-improving agent that can create skills, persist knowledge, and build context across sessions.

Cato CTRL recently analyzed an operator’s command-and-control (C2) server’s entire 33 days operation, including the steps he took to preserve access after the takedown. 339 commands. Four French victims. Between March 30 and May 1, 2026, Cato CTRL studied every command issued by a French-speaking threat actor (“Poisson”) against one French automotive small business and four French individuals.

A CVE lands in the morning. Hours later, attackers are exploiting it in the wild. The patch is not ready, the change window is days away, and the clock is already running. None of this is new. What changed is that vulnerability exploitation is now the most common path into organizations.

Modern IT and security teams no longer evaluate platforms in isolation. They ask how a platform fits into the architecture they run, the workflows they trust, and the outcomes they need to improve. Enterprise stacks are not isolated; they are interdependent. Identity shapes access, endpoint posture influences policy, while SIEM tools drive investigations and rely on shared data and context. AI tools introduce new layers and patterns of usage, risk, and data movement across the network.

Cato CTRL researchers recently identified an undocumented, active phishing campaign targeting Brazilian organizations with fake business-document lures, downloading a NinjaOne Remote Monitoring and Management (RMM) agent. The use of NinjaOne is particularly significant, underscoring how attackers no longer need exotic malware to penetrate an enterprise. Familiar business workflows and software is enough.

TL;DR: In the age of frontier AI models, vulnerability discovery and exploit development are scaling faster than human defenders can manually respond. Security teams already face growing CVE volumes, shorter exploitation windows, and manual workflows for researching vulnerabilities, creating protections, validating them, and preparing them for deployment. As attackers weaponize vulnerabilities faster than organizations can patch them, time-to-protect is becoming a critical security metric.

As organizations advance toward Security Service Edge (SSE), secure access to private applications has become a practical priority. Executives rightly expect these programs to improve security while increasing agility. Yet many initiatives slow down at the same point: extending access to private applications. The work often depends on firewall exceptions, routing changes, and cross-team coordination, followed by tightly controlled maintenance windows.

GigaOm’s latest analysis highlights a clear shift in the market. As they note, “The standalone Secure Service Edge (SSE) market has largely disappeared, with leading vendors now offering complete SASE solutions that converge software-defined wide-area network (SD-WAN) and SSE into single-vendor platforms. Organizations increasingly favor this consolidated approach to reduce operational complexity and improve visibility.”