Cloud adoption, tool integrations, and AI are spurring significant changes in how security information and event management (SIEM) systems are evolving.
Security information and event management AI (SIEM) platforms have evolved far beyond their basic log collection and correlation roots.
With cyber threats moving too fast for manual intervention, leading vendors have been integrating artificial intelligence and machine learning technologies into their SIEM platforms.
In addition, modern SIEM platforms now incorporate extended detection and response (XDR) and security orchestration, automation, and response (SOAR), enabling real-time threat detection and automated remediation.
SIEMs have become a platform to monitor log data for anomalies and suspicious events before triggering alerts based on unusual behavior and detection rules.
“[SIEM] often serves as the workspace for security analysts to investigate incidents that are correlations of alerts with other contexts such as asset information, vulnerabilities, and threat intelligence,” according to analyst group IDC. “IDC expects that in the future, the SIEM will also be the response center of the SOC with automated handling of many incidents via playbooks.
And as enterprise cloud use continues to rise, Google’s Cloud Cybersecurity Forecast predicts that SIEM products will become central to enterprise SOCs (security operations centers) ingesting “everything from cloud logs to endpoint telemetry.”
Joe Turner, global director of research and business development at market intelligence firm Context, notes that larger attack surfaces and more sophisticated attacks are spurring enterprises to invest in SIEM in combination with other technologies, including XDR and SOAR, as a platform to correlate, detect, and remediate threats. As such, his firm reports that the SIEM market grew 20% in 2024.
SIEM, XDR, and SOAR convergence
The convergence of SIEM with security tools such as XDR and SOAR is a major factor driving growth in the market.
SIEM provides log analytics and broad visibility, XDR extends detection across endpoints and cloud, and SOAR orchestrates response.
When SIEM detects a security incident, SOAR triggers automated response actions via XDR — isolating compromised endpoints, disabling compromised user accounts, or blocking malicious traffic in real-time.
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Source : https://www.csoonline.com/article/3829750/4-key-trends-reshaping-the-siem-market.html
Thousands of active AWS accounts are vulnerable to a cloud image name confusion attack that could allow attackers to execute codes within those accounts.
According to DataDog research, vulnerable patterns exist in the way multiple software projects retrieve Amazon Machine Image (AMIs) IDs to create Amazon elastic compute cloud (EC2) instances.
“The vulnerable pattern allows anyone that publishes an AMI with a specially crafted name to gain code execution within the vulnerable AWS account,” the researchers said in a blog post. “If executed at scale, this attack could be used to gain access to thousands of accounts.”
The whoAMI attack
Researchers have demonstrated that the attack vector “whoAMI” can impact many private and open-source code repositories. Over 10,000 AWS accounts are vulnerable to this attack, about 1% of the reported one million active AWS deployments.
The whoAMI attack is a name confusion exploit, a type of supply chain attack where misconfigured software is tricked into using a malicious resource. Unlike the dependency confusion attacks, which targets software dependency like pip packages, whoAMI involves a rogue virtual machine image impersonating a legitimate one.
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