88.5M
Victims of cybercrime every year
Cloud Security
Strengthen cloud-native infrastructure across AWS, Azure, GCP and Kubernetes with practical security architecture, continuous monitoring, policy enforcement and cloud-native security controls.

Victims of cybercrime every year
Average cost per breach
Faster threat detection
Protect cloud-native infrastructure across prevention, policy enforcement, encryption, access control, runtime security and network isolation.
Align cloud environments with recognized security frameworks and benchmarks including CIS, NIST, PCI-DSS, HIPAA, SOC 2 and FedRAMP where applicable.
Apply granular access controls and continuously verify trust to reduce lateral movement and unauthorized access.
Use service-mesh capabilities from technologies such as Istio and Linkerd for mutual TLS, authentication and authorization.
Protect sensitive information in transit and at rest using modern encryption practices.
Enforce security policies at runtime with policy engines such as Open Policy Agent and Kyverno.
Control pod-to-pod and service communication through granular network policies and workload isolation.
Strengthen healthcare cloud environments with controls designed around protecting patient information and supporting HIPAA requirements.
Protect banking, fintech and payment environments with security controls aligned with PCI-DSS and financial-services risk requirements.
Identify critical cloud-security gaps and create a prioritized remediation plan.
Identify where your cloud environment is vulnerable and create a prioritized plan for improving its security posture.
Review current cloud controls, configurations and operating practices.
Surface the security weaknesses that create the greatest business and operational risk.
Turn assessment findings into a practical remediation roadmap.
Strengthen architecture and operating practices to reduce the likelihood and impact of future incidents.
Improve threat detection and response across containerized and cloud-native environments with continuous security visibility and runtime monitoring.
eBPF-based technologies such as Cilium can provide deeper visibility and control across Kubernetes workloads and network activity.
Security tools such as Wazuh, Falco and Tetragon can support detection, alerting and investigation of suspicious runtime behavior.
Protect the integrity of software platforms and delivery pipelines by assessing dependencies, third-party components and security controls throughout the software supply chain.
Use vendor-risk processes, component assessments and continuous monitoring to reduce exposure across the broader software ecosystem.
Use cloud-native backup and recovery practices to protect Kubernetes and OpenShift environments against outages, corruption and ransomware scenarios.
The reference case study demonstrates disaster-recovery implementation for an air-gapped OpenShift environment using Kasten K10, including recovery testing for a large data footprint.
Shift security earlier into delivery pipelines with static analysis, dependency scanning and Infrastructure-as-Code validation during continuous integration.
Protect software artifacts through container-image scanning, signing and runtime security controls after deployment.
Use policy engines such as Gatekeeper and Kyverno to apply consistent security controls throughout cloud-native infrastructure.
Combine policy enforcement with security-focused logging, observability and alerting so suspicious activity can be detected and investigated quickly.
DevSecOps
Strengthen your delivery pipeline by embedding security controls throughout development, deployment and operations.
Use cloud-security architecture and governance practices based on frameworks such as AWS Well-Architected, Cloud Security Posture Management and SIEM.
Security programs can also align infrastructure with benchmarks and requirements such as CIS, NIST, PCI-DSS, HIPAA, SOC 2 and FedRAMP where applicable.
Start with a high-level security assessment to identify major gaps, followed by deeper analysis and prioritized recommendations where required.
AI workloads introduce additional security concerns including prompt manipulation, data exposure and unauthorized access to models and supporting infrastructure.
Protect AI infrastructure using techniques such as segmentation, Zero Trust and confidential computing to improve data privacy and workload isolation.
Confidential-computing technologies can protect sensitive AI workloads and data while they are being processed.
Generative-AI security controls can also help reduce risks associated with adversarial inputs, poisoned data and misuse of AI systems.
Detect and block malicious prompts designed to manipulate AI application behavior.
Reduce sensitive-data leakage through detection and redaction of personally identifiable information.
Protect proprietary AI models and model assets from unauthorized access and extraction.
Security work continues after implementation. Ongoing support can include troubleshooting, security monitoring, platform maintenance, policy improvements and continued hardening as cloud-native environments evolve.
Security assessment and implementation work for a healthcare platform focused on improving its overall security posture.
Cloud-native disaster-recovery work for an air-gapped OpenShift Kubernetes environment.
Kubernetes and database modernization covering cloud cost, security and reliability.
Container and performance optimization work aimed at improving scalability and application throughput.