What Does an LLM-Powered Threat Intelligence Program Look Like?

Discover how Large Language Models can revolutionize threat intelligence with answers to complex questions, task automation, and prioritized security controls.

Key takeaways
  • An LLM-powered threat intelligence program should prioritize providing answers to difficult and uncertain questions that enable organizations to effectively apply a limited number of security resources to a virtually infinite number of threats.
  • Scalability of LLM-powered workflows requires high-quality domain-specific data sets to pre-train or fine-tune models, as well as processing and interpretation capabilities to derive meaning from raw threat artifacts.
  • LLMs have limitations, including hallucinations, which can produce outputs that are not based in reality; fact-checking models’ outputs is crucial for grounding them in factuality.
  • A framework for components of successful cyber threat intelligence programs includes threat visibility, interpretation, and action, with LLMs playing a significant role in the interpretation phase.
  • When implementing LLMs in a threat intelligence workflow, it’s essential to codify human expertise and intentionally use LLMs to augment human decision-making.
  • A threat intelligence program needs visibility into security-related data, including telemetry data, and may require human analysts to review outputs and correct for hallucinations.
  • LLMs can be used to automate tasks such as log data translation, to improve the speed and accuracy of threat intelligence analysis.
  • There are potential applications of LLMs in threat intelligence, including the ability to summarize and explain complex data, and to translate log data into human-readable formats.
  • LLMs can be used to prioritize security controls, by providing insights on the top threats facing an organization, and to enable security leadership to effectively prioritize security resources.
  • When deciding whether to use LLMs, consider the time and consequences of hallucinations, prioritizing human review for critical or high-stakes decisions.
  • To scale LLM-powered workflows, organizations may need to invest in high-quality domain-specific data sets, as well as processing and interpretation capabilities.
  • A key challenge for organizations implementing LLMs is ensuring that they are providing accurate and trustworthy outputs, and using fact-checking and other techniques to mitigate the effects of hallucinations.