ARMO launches behavioral-based cloud detection and response to protect workloads from cyberattacks before and during attempted exploits, without being overwhelmed by alerts
ARMO, the cloud security and workload protection innovator and creator of Kubescape, announced today the launch of its new ARMO Cloud Detection & Response solution, providing robust security for workloads.
This new offering addresses the residual threats that may persist during runtime, even after thorough scanning during development and deployment. The solution builds on Kubescape’s open-source threat detection capabilities by adding observed application behavior with context from Kubernetes, cloud environment, security policies, and workload characteristics. This creates a unique Application Profile DNA (APD™) that serves as the baseline for detecting anomalies, malicious activities, and malware in real-time.
ARMO’s approach focuses on providing actionable results while reducing false positives, without impacting application functionality. This approach leads to more secure applications while mitigating alert fatigue for security teams.
By utilizing Kubescape, ARMO Platform boosts workload protection within Kubernetes clusters by providing runtime threat detection and response capabilities. Using an eBPF-based runtime sensor to determine expected application behavior, Kubescape establishes a baseline and will then detect and flag any deviations or suspicious behavior, leading to enhanced workload protection. This technology focuses on reducing false positives and maintaining a low resource footprint, reducing operating costs by up to 60% compared to traditional runtime agents.
“Legacy Endpoint Detection & Response solutions have struggled to keep up with the visibility and context challenges posed by modern containerized and cloud-native microservices architectures running on Kubernetes, necessitating the evolution to Cloud Detection Response,” said Ben Hirschberg, CTO and co-founder of ARMO, and core maintainer of Kubescape.
ARMO combines anomaly detection with behavioral inspection, addressing a broad spectrum of threats and malicious attacks targeting cloud workloads and Kubernetes clusters – zero days, supply chain attacks, ransomware, crypto miners, data breaches, file-based or fileless attacks, and more. The platform’s adaptive rules focus on responding to malicious incidents, minimizing alert fatigue, and ensuring quick remediation.
“Runtime security is crucial since it serves as the final layer of defense against threats,” said Shauli Rozen, CEO and co-founder of ARMO. “While mitigating security risks within the pipeline and cluster architecture is essential, runtime security is necessary to alert you to and manage threats that were not caught by other defenses.”
Explore AITechPark for the latest advancements in AI, IOT, Cybersecurity, AITech News, and insightful updates from industry experts!
