Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The traditional network perimeter is dead. In an era of hybrid work, cloud proliferation, and sophisticated insider threats, every laptop, server, and remote device connected to your system represents a potential attack vector. Endpoint monitoring has become an indispensable baseline for enterprise security, providing security leaders with continuous visibility into employee and device activity.

By the time you realize what’s missing, it’s already gone. That’s the brutal reality of data exfiltration, the unauthorized transfer of sensitive data from a system or network to an external destination. And it’s happening more often than you think. It’s now the leading form of insider threat in the U.S., accounting for 62% of all such cases.

Organizations have spent years hardening their perimeters against external attackers. Yet some of the most damaging breaches today originate from within. Insider threats — whether from disgruntled employees, compromised accounts, or AI agents — are responsible for a growing share of data loss and costly security incidents. Traditional security tools weren’t built for this reality.

AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI — often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.

Anthropic Claude has become a powerhouse for enterprise organizations, supercharging everything from software engineering to data analysis. But with great agentic AI power comes an equally massive security challenge. Every prompt, snippet of code, or piece of customer PII fed into the Claude platform represents a potential data leak or compliance breach. To safely leverage AI’s full potential, CISOs and engineering leaders can no longer rely on yesterday’s security stack.

With great power comes great responsibility — and in the cybersecurity world, immense risk. Your privileged users hold the keys to your organization’s digital kingdom. They have the elevated permissions to keep your infrastructure running, but this means a single compromised account or insider mistake can result in a catastrophic data breach. That’s where privileged user monitoring becomes non-negotiable.

It only takes one accidental file share, one rogue USB drive, or one compromised account to turn your company’s sensitive data into a costly headline. That’s where DLP monitoring steps in. Think of it as a smart, real-time safety net that tracks, detects, and blocks unauthorized data transfers before the damage is done. But what does effective monitoring look like in practice, and how do you deploy it without bottlenecking your team’s daily workflow?

User and entity behavior analytics (UEBA) tools are essential cybersecurity solutions, helping organizations to detect anomalous activities and hidden threats. In this article, we explore the top 10 UEBA tools on the market today. You’ll learn about their key features, use cases, pricing, and customer experiences.

Generative AI has revolutionized productivity — but it has also introduced a massive, often invisible new vulnerability: AI data exfiltration. Whether it’s a well-meaning engineer pasting source code into an LLM for debugging, or a marketer feeding sensitive customer data into a prompt for analysis, your organization’s most valuable intellectual property is likely walking out the virtual front door.

The rapid adoption of generative AI has transformed enterprise productivity, but it’s also quietly introduced a new, sophisticated vulnerability: the AI insider threat. For years, securing the internal perimeter meant watching for data exfiltration via USB sticks or unauthorized emails. Today, the risk looks entirely different.