Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

jfrog

Data Scientists Targeted by Malicious Hugging Face ML Models with Silent Backdoor

Feb 27, 2024 By David Cohen In JFrog
In the realm of AI collaboration, Hugging Face reigns supreme. But could it be the target of model-based attacks? Recent JFrog findings suggest a concerning possibility, prompting a closer look at the platform’s security and signaling a new era of caution in AI research. The discussion on AI Machine Language (ML) models security is still not widespread enough, and this blog post aims to broaden the conversation around the topic.
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WatchGuard

ChatGPT and the dark web: a dangerous alliance

Feb 27, 2024 By Carlos Arnal In WatchGuard
Artificial intelligence (AI) is now present in almost every aspect of our lives. However, its influence is particularly relevant in the field of cybersecurity, where it impacts both defense and attack. While it has become a great tool to protect an organization's digital assets, it has also been weaponized by cybercriminals to spread and execute increasingly sophisticated and difficult to detect cyberattacks.
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cycognito

The CISA Points Way Forward for More Effective Vulnerability Management with Directive 23-01

Feb 26, 2024 By Carrie Oakes In CyCognito
While tech sector media coverage on cybersecurity has primarily focused in recent years on trends such as ransomware attacks, vulnerabilities in the DevOps chain, and the growing role of AI in combating threats, a quiet but significant development has been advancing under the radar on several fronts: we refer to the more assertive stance taken by the U.S. Cybersecurity and Infrastructure Security Agency (CISA) to elevate security best practices in government and the private sector.
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protecto

Unstructured Data Protection: Securing Your Data On-Premises and in the Cloud

Feb 26, 2024 By Rahul Sharma In Protecto
Businesses generate vast amounts of unstructured data daily. Securing this data is essential to prevent breaches, ensure compliance, and maintain business integrity. Unstructured data protection requires different strategies for on-premises and cloud environments. Strong security measures help organizations safeguard sensitive information, minimize risks, and prevent unauthorized access. Businesses face data leaks, regulatory fines, reputational damage, and financial losses without proper security.
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Protecto SecRAG - Launch Secure AI Assistants/Chatbots in Minutes

Feb 24, 2024 By Protecto In Protecto
Introducing Protecto's SecRAG, the game-changer for secure AI. SecRAG stands for Secure Retrieval Augmented Generation, a turnkey solution. No need to build complex rag or access controls from scratch. Protecto provides a simple interface and APIs to connect data sources, assign roles, and authorize the data. In a few minutes, your secure AI assistant will be ready. When users ask your Protecto-powered AI assistants, Protecto applies appropriate access control to find the right data and generate responses that don't expose other sensitive information that the user is not authorized to see.
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protecto

All You Need to Know About Retrieval-Augmented Generation (RAG) - Why Your Organization Needs It

Feb 23, 2024 By Rahul Sharma In Protecto
Imagine accessing a giant repository of knowledge, extracting the most relevant information in response to your specific needs, and then using that information to generate intelligent, factual responses - that's the power of Retrieval-Augmented Generation (RAG). This innovative technology is taking the world of Artificial Intelligence (AI) by storm, and for good reason. Let's delve into what RAG is, why it counts, and how it can transform your organization.
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Snyk

Copilot amplifies insecure codebases by replicating vulnerabilities in your projects

Feb 22, 2024 By Randall Degges In Snyk
Did you know that GitHub Copilot may suggest insecure code if your existing codebase contains security issues? On the other hand, if your codebase is already highly secure, Copilot is less likely to generate code with security issues. AI coding assistants can suggest insecure code due to their limited understanding of your specific codebase. They imitate learned patterns or utilize available context without providing judgment.
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