LLMs at the Core: From Attention to Action in Scaling Security Teams

LLMs can effectively augment security teams by reducing manual work and helping humans focus on high-priority issues

Key success factors for implementing LLMs in security:

• Always keep humans in the loop for oversight
• Use high-quality, relevant context data
• Tell the model it’s an expert in the specific domain
• Start with simple use cases before complex ones
• Evaluate results systematically using frameworks

Proven security use cases for LLMs:

• Bug bounty report triage and categorization
• SDLC security review and risk assessment
• Access management and permissions review
• Security alert triage and incident response
• Document analysis for security issues

Technical implementation best practices:

• Use large context windows (32K-128K tokens)
• Focus on prompt engineering over fine-tuning
• Implement systematic evaluation frameworks
• Start with off-the-shelf models before customizing
• Collect feedback data to improve accuracy

Important limitations to consider:

• Models can hallucinate and make mistakes
• Need high-quality input data and context
• Should not make critical security decisions autonomously
• More effective in high-trust internal environments
• Requires ongoing monitoring and adjustment

Cost considerations:

• LLM compute costs are negligible compared to engineer time
• Focus on ROI from reducing manual work
• Start with simple automations that save time
• Evaluate impact through metrics and feedback