Lax security measures in the software development lifecycle (SDLC) can lead to severe financial repercussions for organizations. The Verizon 2024 Data Breach Investigations Report highlights this growing risk, stating, "Our ways-in analysis witnessed a substantial growth of attacks involving the exploitation of vulnerabilities as the critical path to initiate a breach when compared to previous years.

This is an analysis of the impacts and implications on cybersecurity practices, benefits, challenges, and how to deal with the transition to the new NIST CSF 2.0 framework. NIST released an update to its Cyber Security Framework (CSF) in February 2024. Two of the most obvious takeaways from this version are the addition of a new pillar and the expansion of its application beyond critical infrastructure.

A malicious code, commonly known as malware is simply a software created to harm computer systems and applications, make changes to networks, and the victim completely unaware. Unlike accidental security flaws and applications’ configuration failures, which are occurring because of irrelevant mistakes like coding mistakes or other mistakes during development activities, deliberate code is purposely designed in order to cause some harm.

The release of the February 2024 White House Technical Report, Back to the Building Blocks: A Path Towards Secure Measurable Software, brings about a timely shift in prioritizing software security. Software is ubiquitous, so it’s becoming increasingly crucial to address the expanding attack surface, navigate complex regulatory environments, and mitigate the risks posed by sophisticated software supply chain attacks.

In the ever-evolving landscape of software development, artificial intelligence (AI) is emerging as a transformative force, reshaping the software development lifecycle. While AI use is still not without risk, it's time to reframe the conversation and explore how AI can enhance and streamline various stages of the SDLC. Let’s take a look at how you can strategically incorporate AI in the SDLC and address lingering concerns.

Did you know that nine out of 10 companies detected software supply chain risks in the past 12 months? The increase in the number of dependencies in a supply chain has extended the attack surface for adversaries. It has also caused threat actors to shift their focus from the downstream chain affecting just end users to the upstream chain affecting vendors, customers, and end users alike.

AI has become a hot topic thanks to the recent headlines around the large language model (LLM) AI with a simple interface — ChatGPT. Since then, the AI field has been vibrant, with several major actors racing to provide ever-bigger, better, and more versatile models. Players like Microsoft, NVidia, Google, Meta, and open source projects have all published a list of new models. In fact, a leaked Google document makes it seem that these models will be ubiquitous and available to everyone soon.

Today, an intricate web of tools, programs, and individuals collaborates to bring applications to life. This interconnected network, the software supply chain, encompasses the various entities and processes that shape the software development lifecycle (SDLC), including developers, dependencies, network interfaces, and DevOps practices. Given the diverse nature of these components, ensuring the security of each element becomes paramount.

AppSec integrations can help keep development secure at the speed your business requires. Whether you’re building software, selling it, or using it to run your business, in today’s fully digitized environment, every business is, necessarily, a software business. And to keep your business running at the speed today’s competitive environment requires, you increasingly depend on technology.