Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most breaches don't announce themselves; they whisper. A subtly malformed DNS query here. A DHCP lease request that looks almost normal there. A client that suddenly requests a domain no one in your organization has ever heard of. By the time these whispers become alarms on a SIEM dashboard, attackers have often already moved laterally, exfiltrated data, or cemented persistence. In traditional DNS, DHCP, and IPAM (DDI) setups, these signals are buried under millions of legitimate transactions.

Being a security analyst can feel like being trapped in a Where’s Waldo book. You can find yourself staring at a data stream looking for something that “isn’t like the others.” However, as your organization collects and correlates more data from the environment, finding the Waldo can feel overwhelming. In a modern IT environment, organizations have hundreds or thousands of devices, users, and data points that they need to correlate so they can identify normal network activity.

Signature-based detections provide fast, effective defense against known attacks. But the threat landscape is rapidly changing: Attackers are utilizing novel, sophisticated techniques that can bypass traditional, signature-based detection methods and also weaponizing legitimate tools and processes to avoid established detection tools, including endpoint detection. In this dynamic environment, organizations must in turn deploy new detection techniques to keep pace.

The explosion of Internet of Things (IoT) devices has transformed our world in countless ways, from smart factories to connected healthcare systems. According to recent projections by IoT Analytics, the number of connected IoT devices is expected to reach 40 billion by 2030 . This exponential growth has created an expansive and often invisible attack surface that traditional security measures struggle to protect.

System downtime from faulty software updates can cost businesses huge money losses every second. This reality shows why up-to-the-minute data analysis has become a vital part of modern enterprises. Companies now deal with endless data streams from countless transactions. Knowing how to spot unusual patterns right away could make all the difference between grabbing opportunities and facing harsh setbacks.

Data anomalies indicate serious issues like fraud, cyberattacks, or system breakdowns. It is crucial to preserve operational integrity and security as the complexity and volume of data is increasing as days pass by. To find anomalies in your datasets, anomaly detection uses a variety of algorithms be it statistical or machine learning or deep learning. To protect sensitive assets and ensure seamless operations, organizations require a robust anomaly detection system.

Anybody who monitors logs of any kinds, knows that the extracting useful information from the gigabytes of data being collected remains one of the biggest challenges. One of the more important metrics to keep an eye on are all sorts of logons that occur in your network – especially if they originate on the Internet – such as VPN logins.