With authentication, you can face serious consequences if you follow the old motto, “if it ain’t broke, don’t fix it.” From applications to APIs, authentication tells you whether the person or technology accessing a resource is legitimate. In 2017, the Open Worldwide Application Security Project (OWASP), identified broken authentication as #2 on its list of Top 10 application security threats.

In today’s digital age, ransomware attacks have emerged as one of the most formidable threats to organizations worldwide. These malicious software attacks encrypt files on a device, rendering them inaccessible to users, and demand a ransom for decryption keys. The impact of ransomware can be devastating, leading to significant financial losses, operational downtime, and reputational damage.

Comprehending and effectively addressing cybersecurity threats is paramount to organizational security. As artificial intelligence continues to evolve, how companies respond to cybersecurity threats and how they take proactive steps to mitigate them will factor heavily into profitability, reputation and long-term success.

Modern software development techniques are creating flaws faster than they can be fixed. While using third-party libraries, microservices, code generators, large language models (LLMs), etc., has remarkably increased productivity and flexibility in development, it has also increased the rate of generating insecure code. An automated and intelligent solution is needed to bridge the widening gap between the introduction and remediation of flaws.

Remote monitoring and management (RMM) solutions provide flexible methods to enable MSPs to detect network or device anomalies early, facilitating proactive systems monitoring. While these tools are deployed for legitimate purposes, it is common for cybercriminals to make malicious use of them.

Welcome to the final installment in our “Add to Chrome?” research! In this post, we'll experiment with a method to find masquerading, or suspicious clusters of Chrome extensions using Model-Assisted Threat Hunting (M-ATH) with Splunk and the Data Science & Deep Learning (DSDL) App. M-ATH is a SURGe-developed method from the PEAK framework, which uses models or algorithms to help find threat-hunting leads, or to help make complex problems more approachable.

‍ Today, cybersecurity is evolving into cyber risk management. The last few decades of immense technological and digital transformations have meant that, to a large extent, a business’s ability to be resilient is dependent on a reliable, free flow of data and on technology functioning without interruption. This shift has profound implications for the global economy’s ability to remain stable. ‍