Lost in Translation? Leveraging AI to Simplify SIEM Migration Challenges

Migrating to a new Security Information and Event Management (SIEM) platform is rarely a simple process, particularly when navigating the complexities of SIEM migrations. Cloud-native platforms like Google SecOps are compelling and a strong step forward for organizations. But translating detection rules from legacy formats such as KQL and SPL to YARA-L can remain a time-consuming task for security teams. These rules represent years of expertise, acting as a critical layer of enterprise security.

Manual translations are labor-intensive, error-prone and time-consuming, potentially leaving organizations vulnerable during the transition. To address these challenges, EPAM developed the AI Rule Translator, utilizing a custom Large Language Model (LLM) enhanced by Retrieval-Augmented Generation (RAG) built on EPAM's DIAL platform.

This innovative solution efficiently translated over 300 use cases, showcasing the potential of advanced AI in modernizing security operations. For professionals seeking streamlined processes, this represents a practical approach to minimizing operational disruption while maximizing rule migration accuracy.

The Challenge of Migrating SIEM Rules

Moving to a new SIEM system often requires converting hundreds or even thousands of detection rules into a new language or framework. For example, shifting from KQL or SPL to YARA-L in Google SecOps requires revisiting and reconfiguring the building blocks of your security posture. Many traditional SIEM solutions rely on static rule formats and manual processes, which make the migration process even more tedious. With manual translations, inaccuracies and inefficiencies abound, creating unnecessary pressure and increasing the risk of exposure to malicious activities during the transition.

Security teams must ensure that their valuable rule libraries, comprising years of institutional knowledge and expertise, are preserved and adapted to the new system. The stakes are high, and the solution must balance precision, scalability and efficiency.

A New Approach: Leveraging AI for Seamless Rule Translation

EPAM recognized that the process of translating detection rules could be reimagined with AI. The AI Rule Translator is a state-of-the-art assistant designed to help security engineers migrate rules quickly and accurately. Built on advanced LLM and RAG technologies, this solution handles the heavy lifting while integrating smoothly into your existing security workflow.

Think of it as a highly skilled co-pilot that brings more than automation to the table. It contextualizes, validates and enhances rule translations, ensuring that every output aligns with the needs of real-world security operations.

How It Works

Transitioning from old SIEM systems to a new platform is often complex, especially when dealing with multiple information sources, intricate log data and stringent compliance needs. Traditional methods of rule translation during a SIEM replacement are time-consuming and open the door to data loss, threatening the integrity of threat detection workflows.

The AI Rule Translator transforms workflows by leveraging advanced AI pipelines to effortlessly migrate and validate detection rules with precision and efficiency. Whether targeting the existing SIEM or moving to a cloud-native platform, this approach ensures secure transitions while maintaining the accuracy of security monitoring.

1. Building a Knowledge Base with Contextual Precision

Central to the system's success is a hyper-specialized "semantic brain" that provides deep contextual understanding of security rules. This was developed by curating a robust dataset that included:

• Examples of translated rules

• YARA-L rules examples

• Google SecOps Unified Data Model (UDM) documentation

• Microsoft Sentinel and Splunk documentation

The approach to creating this database involved:

• Document Processing: Source materials are split into smaller, retrievable chunks.

• Embedding Generation: Each chunk is transformed into a vector representation using an AI model.

• Indexing: The resulting vectors are stored in a vector database to enable fast, context-aware similarity search.

2. Query Processing Using Advanced AI Pipelines

When a user submits a detection rule written in either KQL or SPL through the web interface, the system initiates a semantic search across the knowledge base. Here's how:

• Hybrid Retrieval: The system performs a combined search that balances semantic similarity and keyword relevance to identify the most meaningful content.

• Re-Ranking with Cross-Encoder: Retrieved results are further refined and prioritized to ensure that only the most contextually relevant information is passed to the language model.

3. Rule Generation and Validation

Equipped with an enriched context, the platform crafts a prompt tailored to the submitted detection rule. This is processed through GPT-4 hosted on Azure, generating output in YARA-L. Key validation steps ensure the rule is not only syntactically correct but operationally effective:

• Backstory Validation: The Google Chronicle Backstory API verifies the functionality of the translated rule.

• Debugging: Users are provided feedback on any errors encountered during validation.

• Frontend Response: Django returns the translated rule and debug info to the frontend, displayed in the UI with JavaScript handling AJAX submissions and loading states.

With these processes, the entire query lifecycle ensures high precision, making the translated rules immediately applicable.

Challenges Addressed

Migrating to a new platform involves more than simply transferring rules — it's about ensuring that log data from diverse data sources continues to power accurate threat detection and insightful dashboards. Organizations relying on legacy SIEM tools often encounter obstacles such as mismatched rule formats, incomplete data mappings and interruptions to post-migration workflows. Addressing these requires overcoming key technical challenges:

• Complex Logic Translation: Detection rules contain intricate field mappings and nuanced conditions. By curating a contextually rich library, the AI effectively "learns" the language of detection rules, ensuring consistency and accuracy.

• Context Relevance: AI-driven searches can occasionally retrieve misaligned results. The inclusion of re-ranking through a Cross-Encoder ensures that only the most contextually relevant documents are used during translation.

These challenges highlight the value of EPAM's expertise in harnessing cloud-native security and AI technologies to deliver practical solutions for complex issues.

"Manual rule translation is like reconstructing a bridge, plank by plank, while traffic keeps moving. There's too much risk of things falling through the cracks." — Valentin Chichurov, Security Architect at EPAM

Results and Key Takeaways

Using the AI Rule Translator, over 300 use cases were successfully migrated from KQL/SPL to YARA-L. Key outcomes included:

• Accelerated adoption of Google SecOps by reducing translation timelines from months to weeks

• Maintenance of detection integrity, ensuring uninterrupted security coverage

• Improved productivity, freeing engineers to focus on strategic activities such as threat hunting

This project follows EPAM's proven track record of leveraging AI to drive efficiency and accuracy in security operations. For instance, in vulnerability remediation, AI tools automate up to 85% of security fixes.

Looking Forward: Broader Applications for Enterprise AI

A well-defined enterprise AI strategy ensures organizations can maximize the value of cutting-edge technologies in diverse operational workflows. Beyond SIEM migration, the underlying framework of the EPMC-MDRS AI Rule Translator showcases how AI can be strategically integrated to streamline and elevate activities across security and IT operations.

The possibilities don't stop at migration. The underlying framework of this translator can be adapted to:

• Translate additional rule formats

• Automate rule generation from emerging threat intelligence

• Assist in report generation, incident response and other security workflows

These developments align with EPAM's continuous drive for innovation, as outlined in our 2025 AI Research Report. By investing in AI-enabled solutions, security leaders can future-proof their operations and maintain a competitive edge.