Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You're probably already living with the problem this model was built to solve. A finance analyst logs in late from a personal laptop, opens a payroll export, and the old role rule says the request looks fine because the person belongs to the right team. The access engine never asks whether it's midnight, whether the device is managed, or whether the file is sensitive enough to deserve a second look.

Two weeks ago I sat in on a webinar where CyCognito’s founders walked through continuous AI pentesting. The framing stayed with me: the pentest itself has not changed, only who runs each part of it. AI has reached nearly every corner of cybersecurity, and pentesting is no exception. The promise is an agent that probes applications, uncovers flaws scanners miss, and delivers a report before a human tester has finished scoping the engagement.

Security analysts have noticed a trend among threat actors shifting towards adopting a novel open-source command and control (C2) framework called Havoc as an alternative to paid solutions like Cobalt Strike and Brute Ratel. Developed in the C language and introduced in 2022, Havoc’s Main branch received updates in 2023.

Quick definition: Cybersecurity threats are potential attacks that aim to gain unauthorized access to a network, steal or destroy data or disrupt business operations and critical infrastructure. Key takeaways: Organizations of all sizes can fall victim to modern cyberthreats. A malicious actor can have various motives for carrying out an attack, though financial gain remains the most common.

Cyber risk quantification (CRQ) models are as accurate as the data and methodology behind them, and the conversation about CRQ accuracy that plays out across security and finance teams is often stuck on the wrong question. Risk is about future events that may or may not happen, and if they do, the impact will vary. ‍ Looking for certainty in a probabilistic model is a category error. The useful question is not whether a CRQ model produces the "right" number.

AI-related security incident detection starts with knowing what AI systems are running across the organization. Without a complete, continuously updated inventory of sanctioned, shadow, and third-party AI tools, security teams cannot detect incidents involving systems they do not know exist. From there, effective incident management requires a structured response framework that connects detection to containment, investigation, remediation, regulatory notification, and governance integration. ‍

A ransomware advisory lands in your inbox before the morning standup, and the room goes quiet. The water utility's firewall logs are in one console, the endpoint alerts are in another, and the OT sensor feed lives somewhere else entirely. Everyone can see a piece of the story, but no one can see the whole incident. That's the part teams feel first. Not the theory, not the policy language, just the blunt realization that point tools don't become a program on their own.