Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

jfrog

JFrog and Hugging Face Join Forces to Expose Malicious ML Models

Mar 4, 2025 By David Cohen In JFrog
ML operations, data scientists, and developers currently face critical security challenges on multiple fronts. First, staying up to date with evolving attack techniques requires constant vigilance and security know-how, which can only be achieved by a dedicated security team. Second, existing ML model scanning engines suffer from a staggering rate of false positives.
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fidelis security

Advanced Network Traffic Analysis: Machine Learning and Its Impact on NTA

Feb 20, 2025 By Fidelis Security In Fidelis Security
Machine Learning (ML) has revolutionized industries by empowering systems to learn from data, make predictions, automate decisions, and uncover insights—all without the need for explicit programming. With ML, systems can: In network security and cybersecurity, ML and other emerging technologies are crucial for detecting malicious activities such as unauthorized access, data breaches, and other complex security threats.
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securitysenses

Ensuring Data Privacy in Machine Learning: The Role of Synthetic Data in Protecting PII

Feb 12, 2025 By SecuritySenses In SecuritySenses
In today's data-driven world, machine learning (ML) models rely on vast amounts of information to power insights, automation, and decision-making. However, as organizations increasingly leverage these models, they must also address the critical challenge of protecting personally identifiable information (PII). Regulatory frameworks like GDPR, CCPA, and HIPAA place stringent requirements on how data is collected, processed, and shared, making privacy-preserving techniques essential for responsible AI and ML development.
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CrowdStrike

Caught in the Act: CrowdStrike's New ML-Powered LDAP Reconnaissance Detections

Feb 4, 2025 By Shaefer Drew - Mickey Brautbar - Yaron Zinar In CrowdStrike
Early in the cyberattack kill chain, reconnaissance enables attackers to assemble critical network information to plan a tailored attack strategy. In this phase, adversaries aim to map out networks and their users, and locate system vulnerabilities, without setting off alarms. Proactive monitoring and early detection of this activity can disrupt attackers in their tracks and lower the risk of a breach.
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jfrog

Machine Learning Bug Bonanza - Exploiting ML Clients and "Safe" Model Formats

Dec 4, 2024 By Shachar Menashe In JFrog
In our previous blog post in this series we showed how the immaturity of the Machine Learning (ML) field allowed our team to discover and disclose 22 unique software vulnerabilities in ML-related projects, and we analyzed some of these vulnerabilities that allowed attackers to exploit various ML services.
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senseon

The Difference Between Cybersecurity AI and Machine Learning

Nov 12, 2024 By Laura Martisiute In SenseOn
In what feels like 10 minutes, cybersecurity AI and machine learning (ML) have gone from a concept pioneered by a handful of companies, including SenseOn, to a technology that is seemingly everywhere. In a recent SenseOn survey, over 80% of IT teams told us they think that tools that use AI would be the most impactful investment their security operations centre (SOC) could make.
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