Innovation rarely starts with acknowledging the restrictions. It’s only after you’ve fleshed out the practical concepts that you begin to understand how they can align with the predefined boundaries, ensuring that your final product is both useful and compliant. This dynamic process encourages a more organic pathway to discovery, leading to solutions that are not only innovative but also viable within the given constraints.

Since its mainstream emergence in 2022, generative AI has triggered a seismic shift in data management and security. It is estimated that one in four employees now uses genAI apps daily, often unbeknownst to their employer and IT team. This raises concerns, as genAI is designed with a voracious appetite for consuming both mundane and sensitive data. Effectively securing your data as genAI becomes prevalent is a strategic imperative.

In the rapidly evolving landscape of artificial intelligence (AI), ensuring robust security and compliance is becoming more difficult for enterprises. AI audit logs emerge as a critical tool in this mission, offering a detailed record of all activities within AI systems. By leveraging these logs, businesses can enhance their security posture, ensure regulatory compliance, and optimize AI usage. Let's delve into how AI audit logs can serve as a secret weapon in bolstering enterprise security.

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.