Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

According to Vanta’s State of Trust Report, 54% of businesses say that regulating AI would make them more comfortable investing in it. But with regulation still in flux, how can companies adopt AI safely and responsibly to minimize risk while accelerating innovation?

The OWASP Mobile Application Security Testing Guide (MASTG) and the OWASP Mobile Application Security Verification Standard (MASVS) are two vital resources that have been instrumental in reshaping the landscape of mobile app security. Developed by cybersecurity experts, the MASTG is an elaborate manual that describes the technicalities for meeting the security requirements listed in the OWASP Mobile Application Security Verification Standard (MASVS).

In an era where artificial intelligence is transforming industries, AI agents are emerging as powerful tools for automating workflows, enhancing decision-making, and delivering tailored user experiences. These agents are entrusted with handling vast amounts of sensitive data from sensitive healthcare records to financial transactions and intellectual property. However, this trust comes with a significant responsibility: ensuring robust data security and compliance.

As we keep a close eye on trends impacting businesses this year, it is impossible to ignore the impacts of Artificial Intelligence and its evolving relationship with technology. One of the key areas experiencing this transformational change is cybersecurity. The integration of AI with cybersecurity practices is imperative, and it also demands a shift in how businesses approach their defenses.

Could you imagine our interstate highway system without roadway bridges? I don’t think anyone would argue that bridges are not an essential part of an effective ground transportation network. So it doesn’t surprise me that when I ask people what makes a highway bridge “good,” I get quick responses with pretty consistent answers: guardrails, proper lighting, clear signage, smooth driving surface, lane markings, load capacity, structural integrity, and so on.

Today, we’re thrilled to share that Salt has launched extended capabilities to our powerful platform, adding yet another industry-first technical advancement to our trophy case! (full announcement here.) Since its founding, Salt’s been on a mission to create a platform that can detect, prioritize and solve the most complex API security challenges and risks.

What do you get when you combine artificial intelligence (AI) and cybersecurity? If you answered with faster threat detection, quicker response times and improved security measures... you're only partially correct. Here's why.

Cloud adoption is exploding, and rightfully so. Businesses are seeing the value of improved agility and efficiency when leveraging public cloud, resulting in 60% of all corporate data globally being stored in the cloud in 2022. As such, securing the cloud is becoming an increasingly important skill for defensive security teams, ergo red teaming the cloud is becoming increasingly important for us offensive security teams too.

Today, we’re excited to announce a new integration with Amazon SageMaker! SageMaker helps companies build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. By leveraging JFrog Artifactory and Amazon SageMaker together, ML models can be delivered alongside all other software development components in a modern DevSecOps workflow, making each model immutable, traceable, secure, and validated as it matures for release.

Keeping up with threats is an ongoing problem in the constantly changing field of cybersecurity. The integration of artificial intelligence (AI) into cybersecurity is emerging as a vital roadmap for future-proofing cybersecurity, especially as organizations depend more and more on digital twins to mimic and optimize their physical counterparts.