Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Weak passwords cause the majority of data breaches, making password-only security a serious risk in 2025. Understand why Two-Factor Authentication (2FA) is now a must-have for Atlassian tools like Jira and Confluence, how it blocks phishing and credential-based attacks, and why it’s become a baseline security standard.

Standard query patterns often miss specialized device data like QoS stats, feature-specific configurations, or vendor-specific health metrics. Custom commands allow you to fill those gaps—delivering targeted insights without sifting through raw CLI logs or spreadsheets.

Nearly everyone has had “that cold,” the one where most symptoms have resolved except that lingering cough. The cough can continue for weeks or months, all while you feel mostly well across the board. In cybersecurity, an advanced persistent threat (APT) is your IT environment’s lingering cough, albeit a much more damaging one. An APT stealthily gains initial access to your company’s systems and networks, then hides within them to complete objectives.

On August 14, 2025, Cisco released fixes for a maximum-severity vulnerability affecting Cisco Secure Firewall Management Center (FMC) Software, tracked as CVE-2025-20265. FMC is the centralized platform used to manage security settings and network devices across Cisco Firepower and ASA deployments.

The swift adoption of generative AI (GenAI) by the software industry has introduced a new area of focus for security engineers: threats targeting the various components of their AI applications. Understanding how these areas are vulnerable to attacks will become increasingly significant as the space evolves. In this series, we'll look at common threats that target the following components of AI applications.

In Parts 1 and 2 of this series, we looked at how attackers get access to and take advantage of the infrastructure and supply chains that shape generative AI applications. In this post, we'll discuss AI interfaces, which we define as the entry points and logic that determine how a user interacts with an AI application. These elements can include chat interfaces, such as AI assistants, and API endpoints for supporting services.

The AI ecosystem is rapidly changing, and with this growth comes unique challenges in securing the infrastructure and services that support it. In Part 1 of this series, we explored how attackers target the underlying resources that host and run AI applications, such as cloud infrastructure and storage. In this post, we'll look at threats that affect AI-specific resources in supply chains, which are the software and data artifacts that determine how an AI service operates.

Organizations across Australia and New Zealand are increasingly confronted with the challenge of bridging the cybersecurity talent shortage with 69 per cent of ICT professional occupations in shortage, a gap that compounds concern as the complexity of cyberthreats continues to evolve. Faced with a finite talent pool, businesses must find innovative ways to maintain their security posture without sacrificing operational efficiency or increasing strain on their resources.

When vulnerability scans return thousands or even millions of findings, leading to an avalanche of tickets to evaluate, the real challenge begins: figuring out what to fix first. Exposure prioritization is the critical next phase of a mature exposure management program. After defining what exposure management is and establishing a normalized foundation of aggregated data, the question becomes: how do we cut through the noise and focus on what truly matters?

Written in collaboration with Arik Galansky The GENIUS Act’s passage and the recent release of the Report from the Presidents Working Group on Digital Asset Markets reflects a pivotal shift in how the U.S. financial system is preparing for blockchain innovation.