How Qevlar leverages open source LLMs

Security Operations Centers (SOCs) analysts are under relentless pressure to analyse and act on vast amounts of unstructured data—from alerts and logs to emails—quickly and accurately. Traditional automation tools are ill-equipped for these tasks, as they struggle to handle complex, non-repetitive processes. Qevlar integrates LLMs, including Meta’s Llama, to enhance SOC workflows with intelligent, flexible automation that adapts to nuanced and dynamic information.

The new role of LLMs in Security Operations

LLMs have provided powerful natural language processing capabilities that revolutionise how SOCs manage and investigate incidents. Here’s how Qevlar applies these models at different stages of the workflow:

1. Parsing and Retrieval: LLMs help retrieving contextually relevant information from internal systems and historical records—such as logs, emails, and alerts—, enriching the investigation with insights SOC analysts would otherwise spend hours gathering manually.
2. Segmentation and Structuration: organising data into structured categories, such as threat types, potential business impacts, or success outcome, making it easier for analysts to evaluate the severity of alerts.
3. Contextualization and Generation: LLMs dynamically connect the dots between diverse data sources, adding critical context to incidents. Additionally, they generate detailed responses, remediation steps, and comprehensive reports that SOC teams can act on immediately.

Why this matters

Traditional automation tools excel at handling repetitive, deterministic tasks but fall short when dealing with non-trivial, variable information. For example, understanding the content of an email or correlating anomalies across multiple systems is beyond the scope of rule-based systems. Llama, on the other hand, excels in understanding semantics and adapting to unique scenarios, enabling SOCs to automate previously unapproachable tasks.

The power of open-source

One of the advantages of open-source LLMs, such as Llama, is the ability to maintain full control over data. SOCs can run these models locally, ensuring sensitive or proprietary data remains in-house. This capability is essential for organizations handling confidential information, as it mitigates risks associated with sharing data externally.

Open-source also offers unmatched flexibility:

• Customisation: SOCs can customize or finetune models to suit their unique environments and challenges.
• Transparency: Open-source models allow teams to understand and adapt the model’s behaviour, building trust and enabling performance enhancements as needed.

Building more resilient SOCs

By integrating Llama into its technical stack, Qevlar empowers SOC teams to handle investigations with greater speed, precision, and scalability. Whether tackling structured alerts or unstructured emails, Qevlar’s AI-driven approach enables SOCs to operate at the cutting edge of cybersecurity without compromising security or flexibility. In a world of ever-evolving threats, this partnership sets a new standard for resilience and efficiency.