Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this article In 2025, the role of the Chief Information Security Officer (CISO) and compliance leadership has become even more critical in ensuring that risk assessments are not only comprehensive but also agile and adaptive. Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity, enabling risk assessments to be automated, more accurate, and proactive.

In this article Chief Information Security Officers (CISOs) face a dual imperative in the digital landscape of today: safeguarding their organizations from an ever-evolving threat landscape while managing escalating security costs. Artificial Intelligence (AI) offers transformative potential in meeting these challenges by automating threat detection, streamlining compliance, and optimizing resource allocation.

Unauthorized access, insider threats, and privilege misuse can all lead to significant data breaches, and often, the root of the problem lies in how privileged accounts are managed. That’s why controlling privileged access is a top security priority for organizations of all sizes. Two common approaches to managing privileged access are Privileged User Management (PUM) and Privileged Access Management (PAM).

The Arctic Wolf Labs team has identified a new campaign by cyber-espionage group Dropping Elephant targeting Turkish defense contractors, specifically a manufacturer of precision-guided missile systems. The campaign employs a five-stage execution chain delivered via malicious LNK files disguised as conference invitations sent to targets interested in learning more about unmanned vehicle systems.

With global e-commerce transactions projected to exceed $8.1 trillion by 2026, according to Statista, payment gateways are an irresistible target for attackers. A single exploit, like a poorly configured API or insecure redirect, can lead to massive fraud, compliance violations, and irreparable loss of customer trust. Yet, many businesses still rely on surface-level testing or compliance checklists, missing critical flaws in business logic, API behavior, and payment flow integrations.

The recent SharePoint zero-day exploits expose a critical blind spot: hardcoded secrets hidden in collaboration tools. While teams secure code repositories, API keys and credentials lurking in SharePoint documents create dangerous attack vectors for lateral movement.

Security teams often find themselves having to put out the immediate fires in front of them, but this comes at the expense of implementing a more methodical risk reduction strategy. Attack surfaces are expanding, and new risks emerge with new tech. Modern security operations center (SOC) teams are drowning in alerts, stretched thin by talent shortages, and racing to stay ahead of increasingly sophisticated adversaries.

What is LLM Scraping? We’re entering a new phase of the Internet, one that is increasingly shaped by generative AI. These systems need data, and lots of it. To meet this hunger, they scrape the web, pulling in everything from news articles and academic journals to product listings, metadata, and user-generated content. This practice, known as large language model (LLM) scraping, has moved far beyond traditional bots indexing public sites.