• Networking: Manage VPNs, DNS, load balancing, and network services to ensure secure, reliable, and scalable connectivity.
• Storage: Implement distributed and cloud storage solutions with robust backup mechanisms to ensure data availability and disaster recovery.
• Compute: Provision and manage virtual machines, container orchestration (e.g., Kubernetes), cloud platforms, and Linux systems to support scalable and efficient computing resources.

Outcome: A robust, scalable, and secure infrastructure that supports seamless operations, high availability, and efficient resource utilization.

• CI/CD Pipelines: Automate and streamline development, testing, and deployment processes to accelerate software delivery.
• Version Control: Manage code changes and collaboration across teams to ensure consistency and traceability.
• Monitoring: Continuously monitor system performance and reliability to proactively address issues and ensure uptime.
• Deployment Tools: Facilitate seamless application deployment across environments with minimal downtime.
• Development: Enable programming and scripting to build, customize, and automate solutions.

Outcome: Faster, more reliable software delivery with improved collaboration, reduced manual effort, and enhanced system stability.

• Data Collection: Deploy and manage agents, integrations, and custom collectors to gather data from endpoints, syslog, and APIs.
• Data Ingestion: Parse raw events, normalize them into a common format, and enrich them with threat intelligence and contextual data.
• Data Management: Index, store, and retain data efficiently, with fast search APIs for quick access and analysis.

Outcome: A centralized, well-organized, and enriched data repository that enables efficient data access, analysis, and decision-making.

• Noise Reduction: Filter and prioritize data to focus on security-relevant events, reducing false positives and improving efficiency.
• Data Visualization: Create interactive dashboards and charts to visualize data trends and insights for better understanding and reporting.
• Data Science: Analyze data to uncover patterns, trends, and actionable insights that support strategic decision-making.
• Detections and alerts: Behavior Analytics to detect and alert on issues or threats using rules based on known behaviors and patterns. Anomaly Detection to identify and alert on unusual activity using unsupervised machine learning to uncover potential issues or threats.
• Research and Development: R&D focuses on a collaborative analytics framework. This integrates detection, validation, and remediation, enabling rapid learning cycles and agile responses to evolving data.

Outcome: Enhanced ability to identify, analyze, and act on critical data insights, improving operational efficiency and decision-making.

• Machine Learning: Engineer features, optimize models, and deploy AI/ML solutions to automate and enhance decision-making.
• Predictive AI: Enrich suspicious indicators (e.g., IPs, domains, urls, hashes) in real time to enable faster, more informed decisions.
• Generative AI: Use large language models (LLMs) to automate response documentation and script creation for rapid, customized playbooks.
• Automation AI: Leverage AI agents or tools to handle repetitive tasks (e.g., blocking, isolating, notifying) and streamline incident response workflows.

Outcome: Accelerated incident response, reduced manual workload, and improved decision-making through AI-driven automation and insights.