Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

When organizations adopt remote work, they face increased cybersecurity risks. Privileged Access Management (PAM) helps mitigate these risks by reducing the attack surface, minimizing insider threats, and providing enhanced visibility and monitoring capabilities. Continue reading to learn the cybersecurity risks unique to remote work environments and how PAM helps address them.

Service accounts are nonhuman privileged accounts used by systems or applications to perform certain tasks, access resources or run processes. These accounts are typically given only the permissions they need for a specific job. According to ReliaQuest, 85% of data breaches between January 2024 and July 2024 that organizations responded to involved compromised service accounts. To prevent the misuse of credentials, organizations should secure their service accounts.

The landscape of artificial intelligence (AI) governance is undergoing significant changes, particularly as it relates to the rise of AI Agents—autonomous systems that can independently make decisions and execute tasks. Recently, a key executive order on AI safety was rescinded, which previously required developers to share safety test results with federal agencies and mandated comprehensive assessments of AI-related risks.

The Network and Information Security Directive (NIS2 Directive) is an important piece of European Union legislation aimed at bolstering cyber security across the member states. NIS2 officially came into force on 17th October, and while some EU member countries have met this deadline, many are still working to fully transpose the legislation.

We are excited to announce the integration of the CelesTLSH Malware Scanner into the LimaCharlie ecosystem. Developed by Magonia Research, CelesTLSH enhances your security operations by scanning files collected via the BinLib extension. It identifies known malware and threat actor tools through advanced fuzzy hashing techniques.

The process of crafting new malware detection features is usually time-consuming and requires extensive domain knowledge outside the expertise of many machine learning practitioners. These factors make it especially difficult to keep up with a constantly evolving threat landscape. To mitigate these challenges, the CrowdStrike Data Science team explored the use of deep learning to automatically generate features for novel malware families.