When it comes to managing AWS cloud security, a growing concern for security operations (SecOps teams) is the increasing sophistication of digital threats. While conventional cyber threats deploy widely known tools and techniques in crude, all-or-nothing attempts to breach enterprise security controls, sophisticated attacks known as Advanced Persistent Threats (APTs) employ more advanced technologies and methods to gain and maintain access to secure systems for long periods of time.

Cyberattacks are becoming more advanced, and groups like Scattered Spider are leading the way with their sophisticated techniques. This group is notorious for using social engineering methods like SIM swapping, voice phishing, and SMS phishing to trick employees into giving them access to sensitive systems. By pretending to be IT administrators, they bypass traditional security defenses, moving through networks unnoticed and stealing valuable data.

Security Operations Centers (SOCs) are critical to protecting organizations against cyber threats. Tasked with monitoring networks, analyzing data, and responding to incidents, these teams rely heavily on threat intelligence to detect and mitigate risks. However, one of the most significant challenges they face is the issue of false positives — when benign activities are flagged as potential threats.

Cyber threats against AI systems are on the rise, and today’s AI developers need a robust approach to securing AI applications that address the unique vulnerabilities and attack patterns associated with AI systems and ML models deployed in production environments. In this blog, we’re taking a closer look at two specific tools that AI developers can use to help detect cyber threats against AI systems.

In the world of enterprise security, most teams are laser-focused on defending organizational IT assets from external actors: cybercriminals, digital fraudsters, state-backed hackers, and other external adversaries. But data on the frequency and cost of insider attacks suggests that security teams should shift their focus toward threats that originate from inside their organizations.

Modern SecOps teams use Security Information and Event Management (SIEM) software to aggregate security logs, detect anomalies, hunt for threats, and enable rapid incident response. While SIEMs enable accurate, near real-time detection of threats, today's SIEM solutions were never designed to handle the volume of security data organizations generate daily. As daily log ingestion grows, so do the costs of data management.

Managed Detection and Response (MDR) is a cybersecurity service offered by a Managed Security Services Provider (MSSP) that combines human security expertise with modern security tools to deliver managed threat detection, security monitoring, and incident response capabilities for both SMBs and enterprise clients.

Managed Detection and Response (MDR) services play a critical role in cybersecurity. These technologies remotely monitor, detect, and respond to threats, blending threat intelligence with human expertise to hunt down and neutralize potential risks. However, one of the biggest challenges MDRs face is managing the sheer volume and variety of threat intelligence data they receive.

The 2024 Global Digital Trust Insights survey from PwC reports that 36% of businesses have experienced a data breach that cost more than $1 million to remediate. Cyber threats are clearly on the rise and in today’s volatile threat environment, it is a matter of when - not if - a cybersecurity incident will occur. Digital adversaries are becoming more sophisticated and relying on weak links to exploit company applications and infrastructure.

SecOps teams at midsize companies face a unique set of challenges when it comes to managing organizational cybersecurity. Midsize companies (those with 100-999 employees and $50 million-$1 billion in annual revenue, according to Gartner) possess significant financial resources and valuable data that may be targeted by digital adversaries.