OpenArmor and OpenArmor Dashboards

Future Work

Future work on OpenArmor will concentrate on broadening its compatibility across different platforms like Windows and cloud services. Integrating more data sources and enhancing the AI/ML models for better threat detection is also vital. Additionally, exploring areas such as containerization, deception tactics, and refined behavioral baselining can bolster OpenArmor's intelligent security capabilities. Improving incident response integration and user-friendliness will ensure OpenArmor stays a top-notch, easy-to-use cybersecurity solution.

Expand platform support

Going forward, enabling advanced logging capabilities across a wider range of platforms beyond Linux, such as Windows, macOS, virtualization platforms, cloud environments, and container orchestrators, would greatly enhance OpenArmor's versatility and adoption across diverse enterprise environments.

Integrate with more data sources

Incorporating additional contextual data sources like network traffic, endpoint logs, access logs, DNS logs, etc., could further enrich OpenArmor's analytics capabilities, providing a more comprehensive view of potential threats and enabling more accurate detection models.

Improve AI/ML capabilities

Continuous research and development in enhancing the machine learning algorithms for behavioral analytics would be crucial for reducing Here is the continued document in MDX format:

Improve AI/ML capabilities

Continuous research and development in enhancing the machine learning algorithms for behavioral analytics would be crucial for reducing false positives, improving threat detection accuracy, and enabling automated threat hunting capabilities, keeping OpenArmor at the forefront of intelligent cybersecurity solutions.

Compliance frameworks

Building additional integrations and dashboards tailored to common compliance frameworks like PCI DSS, HIPAA, SOX, etc., would simplify auditing processes and ensure OpenArmor's alignment with industry-specific regulatory requirements, further solidifying its value proposition.

Cloud-native options

Developing cloud-native implementations of OpenArmor, leveraging serverless architectures, containers, and Kubernetes, would increase deployment flexibility and enable seamless integration with modern cloud computing environments, facilitating adoption in cloud-centric organizations.

Cyber deception

Incorporating cyber deception techniques, such as honeypots, could provide valuable insights for further validating and tuning OpenArmor's behavioral models, enhancing its ability to detect and respond to advanced persistent threats.

Incident response workflows

Streamlining integration with Security Orchestration, Automation, and Response (SOAR) platforms, ticketing systems, and threat intelligence feeds would accelerate incident response processes, enabling security teams to respond more efficiently to identified threats.

User experience refinements

Continuously enhancing the user interface and user experience aspects of OpenArmor, with a focus on simplified triage of security alerts and actionable analytics, would improve usability and increase the efficiency of security analysts interacting with the platform.

Baseline modeling

Ongoing research and refinement of algorithms for developing precise normal behavioral baselines across different types of systems and applications would be essential for improving the accuracy and effectiveness of OpenArmor's anomaly detection capabilities.

Data lakes

Architecting efficient solutions for storing and querying massive volumes of log data in data lakes would enable deeper historical analysis, facilitating advanced threat hunting and forensic investigations within OpenArmor's analytical framework.

Acknowledgements Conclusion