News, Article, and Solution in Cybersecurity Realms.

“Cybersecurity talent shortage” is a useful phrase until someone has to decide what to do about it. Hire more people. Train more graduates. Upskill employees. Fine. Train them in what? NIST’s latest cybersecurity workforce investment takes that question seriously. On 18 September, it announced more than $1.7 million for nine Regional Alliances and Multistakeholder Partnerships to Stimulate Cybersecurity Education and Workforce Development, or RAMPS, projects across eight U.S. states. The interesting part isn’t the funding figure. It’s the design. Each project is expected to connect the specific workforce needs of local businesses and nonprofit organizations with learning objectives based on the NICE Workforce Framework. The projects then translate those requirements into curriculum development, internships, apprenticeships, hands-on projects and other learning opportunities. In other words, training starts with the capability that’s missing. ONE SHORTAGE CAN HIDE SEVERAL GAPS Consider three organizations hiring cybersecurity talent. A financial institution may need people who can investigate identity abuse and respond to incidents. A cloud-heavy technology business may be struggling to find people who understand cloud configurations, IAM and

NIST is building an AI agent workflow for one of cybersecurity’s most widely used public resources. On 17 September, its Information Technology Laboratory presented work on an agentic workflow designed to help enrich vulnerability information in the National Vulnerability Database. NIST says the project is intended to help the NVD cope with the increasing scale and complexity of disclosed vulnerabilities, and the webinar covered its architecture, implementation issues and early results. The project illustrates a broader change. AI is moving from producing information toward performing multi-step tasks. NIST describes AI agents as systems capable of autonomous actions that can interact with external systems and internal data. For cybersecurity professionals, that means another layer of skills is arriving. SECURITY HAS TO FOLLOW THE ACTION A conventional AI application might receive a prompt and return an answer. An agent may have access to tools, data and permissions that allow it to continue working. That changes the questions a security professional needs to ask. * What systems can the agent access? * Which actions can

A security alert arrives. An analyst opens it, checks the surrounding activity and begins reconstructing what happened. That sounds like the beginning of detection work. In reality, a considerable amount of work happened earlier. Someone decided which events should be logged, configured the systems to produce them, collected those records centrally and made sure the data contained enough detail to support an investigation. If that work is poor, even an excellent analyst is starting with missing pages. An upcoming ITU cybersecurity exercise in Dushanbe makes this dependency unusually explicit. During the three day program from 21 to 23 September 2026, teams will configure centralized monitoring and telemetry collection before responding to simulated ransomware, data exfiltration, server compromise and command and control traffic. The methodology has a catch: performance against attacks on Day 3 depends on the monitoring participants configured on Day 2. That’s a useful model for SOC training. VISIBILITY IS A SKILL SOC development often concentrates on the visible part of the job: analysing alerts, threat hunting and incident response. Those capabilities