In the early days of cybersecurity, implementing a Security Information and Event Management (SIEM) system was akin to constructing a house from scratch. The SIEM was a blank slate, and transforming raw data into actionable insights was a long and arduous journey. It began with the daunting task of ingesting data from various disparate sources and formats. From there, security teams had to craft detections — rules designed to identify malicious or suspicious activity.

In today’s digital landscape, ensuring the security of both web applications and websites is paramount. As cyber threats become more sophisticated, organisations must employ robust security measures to protect their assets. Penetration Testing is a critical strategy used to identify vulnerabilities and strengthen defences. However, the approach to Penetration Testing can vary significantly between web applications and websites.

Ever since systems started restricting access through passwords, the trial and error method has been used to crack them. But even today, brute force attacks remain a serious danger for organizations. According to the 2024 Data Breach Investigations Report by Verizon, the brute force technique accounts for 21% of all basic web application attacks.

In this age of digital supremacy, keeping our data safe and respecting privacy are super important. As more and more people and businesses use online platforms, it’s crucial to understand what types of data need that extra layer of protection, especially when it comes to PII vs PHI vs PCI. Understanding the distinctions between PII (Personally Identifiable Information), PHI (Protected Health Information), and PCI (Payment Card Information) is crucial.

Striking the right balance between innovation using Artificial Intelligence (AI) and Large Language Models (LLMs) and data protection is essential. In this blog, we’ll explore critical strategies for ensuring AI and LLM data security, highlighting some trade-offs.

As cyber threats continue to grow more sophisticated, the demand for dynamic, scalable security solutions has led to the increasing adoption of Security Operations Center as a Service (SOC as a Service, or SOCaaS). The SOC as a Service model offers a significantly more cost-effective alternative for organizations without the resources to establish full-scale, in-house security operations centers (SOCs).

DevOps practices have significantly transformed the software industry, leading to faster release cycles and more streamlined workflows. The enduring presence of the DevOps model is undeniable, and its influence on modern development methodologies is profound. However, this accelerated pace introduces challenges, particularly regarding DevOps security.