As businesses continue to embrace the flexibility and scalability of cloud-native applications managed by Azure Kubernetes Service (AKS), ensuring robust data protection for them across diverse locations, including edge environments, becomes paramount. For organizations leveraging Kubernetes at the edge with Azure Arc, the need for a comprehensive and easily managed backup and disaster recovery solution is crucial.

Phishing attacks occur when cybercriminals trick their victims into sharing personal information, such as passwords or credit card numbers, by pretending to be someone they’re not. Artificial Intelligence (AI) has made it easier for cybercriminals to carry out phishing attacks by writing believable phishing messages, mimicking people’s voices, researching targets and creating deepfakes.

Developers are constantly exploring new technologies that can improve the performance, flexibility, and usability of applications. GraphQL is one such technology that has gained significant attention for its ability to fetch data efficiently. Unlike the traditional REST API, which requires multiple round trips to the server to gather various pieces of data, GraphQL allows developers to retrieve all the needed data in a single request.

Application Programming Interfaces (APIs) have become the backbone of modern applications. They enable seamless interaction between different software systems, allowing businesses to innovate rapidly. With the proliferation of APIs comes an increased risk of security vulnerabilities. Ensuring API security is crucial to safeguarding sensitive data, maintaining user trust and protecting the integrity of applications.

From the moment ChatGPT was released to the public, offensive actors started looking to use this new wealth of knowledge to further nefarious activities. Many of the controls we have become familiar with didn’t exist in its early stages. The ability to request malicious code or the process to execute an advanced attack was there for the asking from an open prompt. This proved that the models could provide adversarial recommendations and new attacks never before seen.

As organizations increasingly integrate artificial intelligence (AI) into their operations, the nature of data security is undergoing significant transformation. With AI’s ability to process vast amounts of data quickly, the risk of data breaches and leaks has grown exponentially. In this context, Data Loss Prevention (DLP) has (re)emerged as a critical component for IT professionals seeking to safeguard sensitive information.