Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Everyone knows that running outdated computer applications comes with annoyances and risks. An outdated application might face performance issues or just become slower than modern versions. It might face compatibility issues and have limited functionalities. It might lose technical support and even fail to meet current industry standards and regulations, which could put your organization at risk for non-compliance penalties and legal action.

In the 50th episode of the Trust Issues podcast, host David Puner interviews Justin Hutchens, an innovation principal at Trace3 and co-host of the Cyber Cognition podcast (along with CyberArk’s resident Technical Evangelist, White Hat Hacker and Transhuman Len Noe). They discuss the emergence and potential misuse of generative AI, especially natural language processing, for social engineering and adversarial hacking.

Cybersecurity has always been a complex field. Its adversarial nature means the margins between failure and success are much finer than in other sectors. As technology evolves, those margins get even finer, with attackers and defenders scrambling to exploit them and gain a competitive edge. This is especially true for AI.

The cloud provides multiple benefits for running services and storing data. Just like with data stored on-premises, data stored offsite and in the cloud should be backed up. Data stored in the cloud is not invulnerable by default, as the risk of data loss is still present due to accidental deletions and cloud-specific threats. At the same time, the cloud can be useful for disaster recovery.

The Gartner research paper “What You Need to Do to Protect Your APIs” outlines key requirements for bolstering API security measures. In this blog post, we’ll delve deeper into these requirements as introduced by Gartner, explain their significance, and demonstrate how AppSentinels offers comprehensive solutions for each requirement. As per Gartner, the second step is to assess the security of these APIs.

With the new release of CloudCasa migration and replication workflows, businesses harnessing Kubernetes can now streamline complex transitions across multiple clouds with unprecedented ease.

The power of artificial intelligence (AI) and machine learning (ML) is a double-edged sword — empowering cybercriminals and cybersecurity professionals alike. AI, particularly generative AI’s ability to automate tasks, extract information from vast amounts of data, and generate communications and media indistinguishable from the real thing, can all be used to enhance cyberattacks and campaigns.

Achieving an effective bring-your-own-device (BYOD) program has been aspirational for many IT organizations. There are explicit security and privacy concerns, which have led many admins to sour on the concept, despite its benefits. Admins have even reluctantly accepted the risk of personal PCs being left unmanaged, which leaves gaps in management and visibility.

How would you feel about your home security system if it only checked to see if your doors and windows were locked periodically? This security system would provide great visualizations of your house and how a criminal could get from one room to another, ultimately reaching one of your prized possessions, like a safe. However, it doesn’t have cameras on your doorbell or windows to alert you in real time when someone suspicious was approaching, or worse, trying to break into your house.

Generative AI has exploded in popularity across many industries. While this technology has many benefits, it also raises some unique cybersecurity concerns. Securing AI must be a top priority for organizations as they rush to implement these tools. The use of generative AI in manufacturing poses particular challenges. Over one-third of manufacturers plan to invest in this technology, making it the industry's fourth most common strategic business change.