Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

On April 8, 2025, the National Security Division (NSD) of the US Justice Department (DOJ) implemented the Data Security Program (DSP).

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo Noam Cohen is a serial entrepreneur building seriously cool data and AI companies since 2018. Noam’s insights are informed by a unique combination of data, product, and AI expertise — with a background that includes winning the Israel Defense Prize for his work in leveraging data to predict terror attacks.

Let’s face it: humans are creatures of habit, and nothing rattles us quite like the prospect of change. (Just ask anyone who’s dared to swap out the office coffee brand—revolutions have started over less.) According to SHRM's research on change fatigue, today’s relentless pace of disruption is exhausting employees faster than a budget ergonomic chair. But here’s where it gets fascinating—where security, HR, and fraud analysis converge in ways you might not expect.

If Darth Vader and the rest of the Empire made one major strategic mistake, it was failing to understand the important role that the human element plays in security. Convinced of their superiority, the Empire’s leaders assumed that the Death Star was impenetrable. However, in the end, it was a scientist and his team who compromised the technology by building in a backdoor.

A security information and event management (SIEM) solution aggregates and correlates data from across the organization’s complex, interconnected environment. Modern enterprise IT consists of decentralized users and applications that require organizations to implement technologies that provide visibility across disparate security solutions. Simultaneously, SIEMs have a reputation for being difficult and expensive to manage.

The irony of being an adult working in IT and security is that where having your head “in the clouds” was inappropriate as a child, today most of your activities require you to have your head in the cloud. Organizations moved their business operations to the cloud because they could achieve various operational benefits, like improved collaboration and reduced costs. Yet, many companies still maintain an on-premises SIEM.

Security leaders spent most of the past year testing AI driven security automation. Many discovered that the promise of fully autonomous SOC operations collided with the reality of hallucinations, opaque recommendations, and inconsistent outcomes. McKinsey research now shows that more than 80 percent of organizations have not realized meaningful results from gen AI programs.

Security and observability teams generate terabytes of log data every day—from firewalls, identity systems, and cloud infrastructure, in addition to application and access logs. To control SIEM costs and meet long-term retention requirements, many organizations archive a significant portion of this data in cost-optimized object storage such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.

The evolution of your security stack is similar to the different phases of buying cars. In the beginning, you just need enough to transport a few items, maybe yourself and a few friends. The inexpensive two-door hatchback is perfect. However, as your family grows, whether with small humans or pets, you increasingly need more space and more capacity, leading to purchasing a four-door sedan or, even, a mini-van.

Most teams think of data lakes as cold storage. A long-term archive. A place to keep logs “just in case” while budgets tighten and ingest volumes rise. Functional, sure. But limited. The traditional data lake keeps everything, helps occasionally, and rarely fits the way analysts work. Graylog approaches the data lake differently. In Graylog 7.0, the data lake is not a warehouse. It is a pressure release valve for teams overwhelmed by storage cost, investigation delays, and cloud data sprawl.