In the dynamic landscape of cloud security, detecting and responding to threats efficiently is paramount. We've visualized the journey of a threat actor using a compromised identity across cloud platforms (AWS, Azure, or GCP) and how cloud-native security services pinpoint malicious activities through anomaly detection and how MTTR for such event can be reduced with security automation .
This Cloud Security automation encapsulates various attack vectors, including:
Credential Access, Defense Evasion, Discovery, Exfiltration, and more, showcasing the depth of potential Cloud IAM user-related anomalies.
When Cloud native threat detection tools detects such threats, doesn't stop at mere detection. Triggers a sophisticated webhook automation, initially guided by security team-defined deterministic logic. This pivotal stage involves if-else conditions to ensure accuracy before invoking generative AI for a deeper contextual understanding.
The AI considers user permissions, severity, and the nature of the finding, presenting a detailed context to the SOC team. Upon approval in the automated workflow, proactive measures like adding permission boundaries to compromised instances, disabling console access, rotating cloud access keys, and informing the identity owner are executed seamlessly.
Many organizations activate cloud-native detection services yet overlook the critical layer of automated response. Use case emphasizes the necessity of integrating intelligent, automated response mechanisms to not just detect but also effectively neutralize threats, safeguarding your cloud environment against sophisticated attacks.
#cloudsecurity #incedentresponse #IAM #autobotAI #cybersecurity #automation #securityautomation #aws #azure #gcp
