Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The integration of AI, especially Gen AI, into healthcare has been transforming the industry, enabling providers to enhance patient care, streamline operations, and reduce costs. Below is an overview of the most promising AI use cases in healthcare that are reshaping the industry.

Enterprise applications, whether on-premise or in the cloud, access LLMs via APIs hosted in public clouds. These applications might be used for content generation, summarization, data analysis, or a plethora of other tasks. Riscosity’s data flow posture management platform protects sensitive data that would otherwise be accessible to LLM integrations.

This is a burning issue that must be considered by all organizations striving to decrease their cybersecurity vulnerabilities. Keep reading to get familiar with the risks of GenAI utilized for email attacks and how advanced implementations can counterpose malicious activities.

The latest OWASP Top 10 For Mobile list ranks credential mismanagement as the largest concern our applications face. Let's work to solve this secrets problem together.

Passwordless authentication for end users is taking the world by storm, offering organizations and individuals alike unprecedented security, user experience, and efficiency benefits. By all indications, the next generation of authentication for end users has finally arrived, sending the password the way of the dodo. Although they don’t get anywhere near the same hype, advanced authentication strategies for APIs are as critical as passwordless authentication for end-users.

Cyber threats against AI systems are on the rise, and today’s AI developers need a robust approach to securing AI applications that address the unique vulnerabilities and attack patterns associated with AI systems and ML models deployed in production environments. In this blog, we’re taking a closer look at two specific tools that AI developers can use to help detect cyber threats against AI systems.