Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Researchers at ESET warn that malvertising campaigns are impersonating AI tools to trick users into installing malware. The Rilide infostealer, for example, is being distributed via a malicious browser extension posing as Sora or Gemini. “In the case of the malicious browser extension, it is delivered to victims who have been duped into clicking on malicious ads, typically on Facebook, that promise the services of a generative AI model,” the researchers write.

Would you trust AI to call 911? GitGuardian's ML engineer Nicolas posed this question at PyData Berlin, sparking a discussion on integrating ML into critical systems, debunking AI myths, and balancing innovation with safety in AI deployment.

The Arizona Department of Homeland Security (AZDOHS) operates in an environment that requires a robust cybersecurity strategy to protect against ever-evolving threats. With a mission to safeguard state and local infrastructures, the team at AZDOHS faced the daunting task of monitoring an expansive array of data points and potential vulnerabilities.

It’s impractical to operate security operations alone, using manual human processes. Finding opportunities to automate SecOps is an underlying foundation of Zero Trust and an essential architecture component for enterprise-scale SOCs. Let’s discuss what SOAR is, its common uses, and the future of SOAR with AI.

Zero Trust Security Models are a cybersecurity paradigm that assumes no entity, whether inside or outside the network, can be trusted by default. This model functions on the principle of "never trust, always verify," meaning every access request must be authenticated and authorized regardless of origin.

Cyber threats are getting more sophisticated and frequent. As a result, organizations are always looking for ways to outsmart cybercriminals. This is where artificial intelligence (AI) comes in handy. Artificial intelligence (AI) is transforming the cybersecurity landscape by offering faster, more precise, and more efficient means of identifying cyber threats.

In an era of unprecedented advancements in AI, the National Institute of Standards and Technology (NIST) has released its "strategic vision for AI," focusing on three primary goals: advancing the science of AI safety, demonstrating and disseminating AI safety practices, and supporting institutions and communities in AI safety coordination.

Large Language Models (LLMs) are advanced artificial intelligence systems that understand and generate human language. These models, such as GPT-4, are built on deep learning architectures and trained on vast datasets, enabling them to perform various tasks, including text completion, translation, summarization, and more. Their ability to generate coherent and contextually relevant text has made them invaluable in the healthcare, finance, customer service, and entertainment industries.