Revolutionizing Language Analysis with Generative AI

Mike Brown, Strider's Chief Data Officer and Co-Founder, wrote an article for Mayfield’s “Enterprise AI in 2025: The CIO’s Roadmap” report about how Strider is using generative AI to process and understand vast amounts of open-source data across nearly 100 different languages.

By leveraging LLMs, Strider has doubled individual analyst output, enabling our team to focus on higher-value tasks like important decision-making and strategic analysis that require human expertise and judgement. It is also important to layer AI into existing systems rather than overhauling them—a strategy that has allowed Strider to navigate linguistic challenges and optimize unstructured data for actionable insights.

The report features insights from 200 IT leaders who are driving AI transformation. The full Mayfield report can be found here.

In the complex world of multilingual security analysis, AI is transforming how we process and understand vast amounts of data. At Strider, implementing LLMs has dramatically improved our ability to analyze billions of documents across multiple languages like Russian and Chinese.

We’ve nearly doubled individual analyst output by leveraging LLMs for translation, issue identification, and data review. This efficiency gain isn’t just about speed—it’s about enabling our analysts to focus on higher-level tasks requiring human expertise and judgment. They can concentrate on complex analysis and decision-making rather than spending hours on basic translation and initial review.

Success comes from adding AI as a complementary layer, enhancing existing capabilities rather than replacing them.

Our success stems from a pragmatic integration approach. Instead of completely rebuilding our systems, we’ve added AI as a complementary layer to enhance existing capabilities. This strategy has proven particularly valuable in transforming unstructured data into structured formats, making it easier to visualize and analyze complex information. For instance, our systems can now efficiently process and analyze content in right-to-left languages like Farsi, demonstrating AI’s adaptability to complex linguistic challenges.

With this transformation, the role of language specialists has evolved. Rather than focusing solely on translation, they now collaborate closely with engineers to optimize AI systems and ensure accurate, culturally nuanced analysis. This shift represents a broader trend in how AI is changing specialized roles, enhancing rather than replacing human expertise.

Key Takeaways:

Focus on augmenting existing systems rather than complete rebuilds
Measure success through concrete productivity metrics (2x analyst output)
Evolve specialist roles to combine domain expertise with AI capabilities
Build systems that can handle complex linguistic challenges