• Posted by: Eng. Donya Bino
• December 08, 2025

LLMs and Threat Intelligence: How AI Changes Cybersecurity Analysis

Large Language Models (LLMs) are popping up everywhere and security teams are starting to notice. They can read through mountains of logs, summarize threat reports, and even suggest mitigation steps in seconds. Impressive, yes. But like any new tool, there are caveats.
Here’s the plain truth about how LLMs fit into threat intelligence and what you should actually pay attention to.

1. Speed Without Judgment
LLMs can process data fast. Really fast.
1. They can scan thousands of threat reports in minutes.
2. They can highlight suspicious indicators.
The catch? They don’t “understand” context. They might flag harmless activity or miss subtle attack patterns. They’re an assistant, not a replacement for human analysts.

2. Summarizing Threat Feeds
One of the biggest advantages of LLMs is summarization.
1. Turn raw threat feeds into digestible reports.
2. Spot trends across multiple sources quickly.
3. Generate briefings for executives without the usual slog through technical jargon.
It saves time, but human review is still essential, LLMs don’t know your environment the way you do.

3. Automated Analysis Risks
LLMs can try to fill gaps or make predictions based on patterns.
1. Sometimes it works beautifully.
2. Other times, it invents data that “looks right” but isn’t.
Always verify insights with your internal data or other threat intelligence sources.

4. Data Sensitivity
Feeding sensitive security logs, indicators of compromise, or internal documentation into an LLM can create risk.
1. If it’s cloud-based, your data may be processed or stored in ways you can’t fully control.
2. Even small leaks can be critical in a security context.
Keep private or high-risk data out of external AI tools.

5. Threat Actors Can Use LLMs Too
Attackers are using LLMs to:
1. Write more convincing phishing emails.
2. Generate malware scripts faster.
3. Automate social engineering at scale.
Your defenders are not the only ones leveraging AI, which makes understanding it even more important.

Practical Tips for Security Teams
1. Use LLMs for triage, not decision-making.
2. Keep private and sensitive information in-house or in an enclosed, secure AI system.
3. Examine insights produced by the AI manually before actually taking any action on the basis of those insights.
4. Utilize traditional feeds of threat information and human knowledge as well as AI for guidance in decision-making.
5. Train your analysts on the limitations and potential misuses of AI.

LLMs are like hyper-efficient interns: fast, capable, and a little naive. They can make threat intelligence faster and more readable, but they can’t replace experience, judgment, or a human eye on context. Used wisely, they’re a force multiplier, used blindly, they can mislead.