Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage
In the era of rapidly evolving Advanced Persistent Threats (APTs) in cyberspace, traditional security solutions are increasingly proving inadequate for effective threat hunting within organizations. Security Operation Centers (SOCs) face significant challenges as analysts grapple with the overwhelming volume of logs generated by diverse devices. To address these pressing issues, a novel automated and dynamic threat hunting framework has been proposed, aimed at enhancing the monitoring of evolving threats and adapting to fluctuating network conditions.
Framework Overview
The newly developed framework primarily focuses on risk-based prioritization for the mitigation of suspicious and malicious traffic. By integrating Agentic AI with Splunk, a well-established Security Information and Event Management (SIEM) platform, this innovative approach systematically brings together various threat hunting modules. These modules encompass:
- Traffic ingestion
- Anomaly assessment using a reconstruction-based autoencoder
- Deep reinforcement learning (DRL) with dual layers for initial triage
- Large language model (LLM) for contextual analysis
This comprehensive integration not only streamlines the threat hunting process but also enhances the capabilities of SOC analysts in identifying and responding to potential threats efficiently.
Evaluation and Results
The framework was rigorously evaluated using both a publicly available benchmark dataset and a simulated dataset. The experimental results indicated that the framework is capable of autonomously adapting to various SOC objectives, effectively identifying suspicious and malicious traffic. This adaptability is critical as it allows for a more proactive approach to cybersecurity, enabling organizations to respond swiftly to threats as they arise.
Operational Benefits
One of the key advantages of this framework is its ability to enhance operational effectiveness within SOCs. By supporting analysts in their decision-making processes regarding whether to block, allow, or monitor network traffic, the framework significantly reduces the cognitive load on human operators. This support is vital in a landscape where the speed and efficiency of threat response can determine the overall security posture of an organization.
Conclusion
This study presents a significant advancement in cybersecurity and threat hunting literature through the introduction of a novel threat hunting framework designed for security decision-making. By promoting cumulative research efforts, the framework aims to foster the development of more effective solutions to combat the continuously evolving landscape of cyber threats. The integration of AI-driven methodologies not only improves detection capabilities but also empowers SOC analysts to operate more effectively in their critical roles.
In conclusion, as cyber threats continue to evolve, the adoption of innovative frameworks like the one proposed in this study will be crucial for organizations aiming to enhance their cybersecurity defenses and improve their threat hunting capabilities.