Discover 10 actionable data protection strategies to secure sensitive enterprise data across hybrid, cloud, and edge environments
As organizations increasingly embrace AI-powered coding tools to accelerate development and reduce engineering overhead, a new threat is emerging at the intersection of generative AI and open-source software (OSS): slopsquatting.
The widespread use of OSS — from foundational operating systems like Linux to orchestration platforms like Kubernetes — has enabled rapid development and faster innovation. However, as OSS adoption accelerates with the rise of GenAI tools and platforms, so too does the complexity and risk associated with securing open source software. This blog is intended for IT and security leaders who recognize these challenges and are seeking effective strategies to address them.
Shadow AI is exposing businesses to security risks and data leaks. Learn how to secure Generative AI usage with governance, AI firewalls, and compliance controls.
OWASP Top 10 for Large Language Model (LLM) Applications, highlighting critical security vulnerabilities such as prompt injection, data poisoning, and system prompt leakage. It emphasizes the need for proactive security measures, including Zero Trust principles, access controls, and continuous monitoring to protect AI-driven applications from emerging threats.
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