Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In an era where attack surfaces are expanding faster than ever, AI has the potential to transform how organizations find and fix vulnerabilities. Gartner estimates AI agents will reduce the time it takes to exploit account vulnerabilities by 50%. From automating routine scans to developing self-learning attack agents, AI is already changing the red team playbook – and the pace of innovation shows no signs of slowing.

Confronted with a rise in sensitive data breaches, businesses are under pressure to efficiently protect their information while overcoming myriad technical limitations. In a recent video, Jon Loya, VP of Sales Engineering at Cyberhaven, shared valuable insights on the challenges of data loss prevention (DLP) and introduced Cyberhaven's cutting-edge strategies for tracking sensitive data within organizations.

Bias in generative AI refers to the systematic errors or distortions in the information produced by generative AI models, which can lead to unfair or discriminatory outcomes. These models, trained on vast datasets from the internet, often inherit and amplify the biases present in the data, mirroring societal prejudices and inequities.

Deep investigation in cybersecurity isn’t just about watching dashboards and clicking “resolve” on tickets. It’s an intricate process of piecing together attacker behavior across time, systems, and attack vectors to understand not just what happened, but how and why.

Many enterprises turn to the DMCA takedown process when they discover infringing or fraudulent content online. While DMCA takedown serves as a protective mechanism for copyrighted material, it was never designed to address the speed and scale of brand impersonation and phishing scams. To put things into context, it takes less than 60 seconds for users to fall for phishing emails.

Every project brings surprises. Maybe it’s an ambiguous spec that created delays. Or an RFI that clarified critical field conditions. Or a detail missed during handoff that led to costly rework. The gap between knowing and doing is evident when: Most contractors experience these issues, document them somehow, and then move on. The challenge isn’t that lessons aren’t learned—it’s that they aren’t operationalized.

If your company works with the U.S. government, manages sensitive data, or seeks to align with recognized best practices, the answer is almost certainly yes. National Institute of Standards and Technology requirements can be daunting. While many companies focus on firewalls, servers, and cloud environments, the client-side of the web application—where sensitive data is collected from customers and employees—is often left unprotected. This blind spot is a key compliance risk under NIST.

In this article At a breakneck pace, and with it, cyber threats are becoming more sophisticated and harder to detect. Organizations today face a heightened risk of data breaches, system compromises, and sophisticated cyberattacks. To counteract these risks, penetration testing has become a critical tool in the cybersecurity arsenal.

Scammers are opportunists. Nasty ones. They prey on the most fundamental human needs: On the surface, a food-assistance scam and a fake-friend scam may seem worlds apart. One promises food, the other companionship. But underneath, they follow the same psychological playbook: build trust, create urgency, extract resources. In this blog, we’ll unpack two real-world scams – SNAP scams and friendship scams – how they work, why they work, and, most importantly, how to fight back against them.