Organizations are in a sheer need to stay up-to the minute, and be ahead for protecting their systems and data. Penetration testing can prove to be a structured process that can mimic the real-world vulnerabilities before it’s prone to any malicious exploits. In this entire lifecycle of pentesting, we got some tools in order to automate, streamline, and enhance each phase of it. They could be of various types, based on their specific role in this entire lifecycle.

One of the major challenges in detecting phishing is the limitations at the Internet Service Provider (ISP) level. Traditional tools often lack the visibility and ability to recognize phishing sites in real time, as they mostly rely on network-level information. Phishing schemes frequently use complex, changing tactics, like rotating domains or imitating legitimate sites, which can go unnoticed without a detailed analysis of the site’s content.

Querying databases comes with costs—wall clock time, CPU usage, memory consumption, and potentially actual dollars. As your application scales, optimizing these costs becomes crucial. Materialized views offer a powerful solution by creating a pre-computed, optimized data representation. Imagine a retail scenario with separate customer and product tables. Typically, retrieving product details for a customer's purchase requires cross-referencing both tables.

If you want to run a SaaS app where your customers can each have one of their own domains pointing to your app, then you will encounter two problems: For example, let's say you wanted to provide a status page service hosted at yourapp.com that allows customers to set up their own domain (like status.acmecorp.com) to be served by your app. Let's dive into the solutions..

Many enterprises have heterogeneous data platforms and technology stacks across different business units or data domains. For decades, they have been struggling with scale, speed, and correctness required to derive timely, meaningful, and actionable insights from vast and diverse big data environments. Despite various architectural patterns and paradigms, they still end up with perpetual “data puddles” and silos in many non-interoperable data formats.

We’re happy to announce that Codemagic has received a perfect SOC 2 Type 2 report! We received our SOC 2 Type 1 report earlier this year and now we have received our Type 2 report with no exceptions noted! At Codemagic, we take the security and privacy of your data seriously. That’s why we went through the tough process of getting audited with the SOC 2 framework. “We’re proud to have achieved a perfect SOC 2 Type 2 report!

Buffer overflow is one of the most common code vulnerabilities. If a buffer overflow vulnerability is not addressed, an untrustworthy agent can take advantage and cause a buffer overrun. In this blog, we break down what is buffer overflow, how to prevent buffer overflow, and we provide examples to help make safeguarding your software against buffer overflow simpler and easier. Read along or jump ahead to the section that interests you the most.

The rise of generative AI models are spurring organizations to incorporate AI and large language models (LLMs) into their business strategy. After all, these models open up new opportunities to extract greater value from a company’s data and IP and make it accessible to a wider audience across the organization. One key to successfully leveraging gen AI models is the ability to share data.