Why Foundation Models for Cybersecurity?
Traditional cybersecurity has relied on signatures and static rules to detect threats. But today's adversaries move fast—constantly evolving to outpace legacy defenses. Foundation models, trained on massive and diverse security datasets, offer a new approach: dynamic, intelligent systems that adapt in real time to detect and mitigate threats.
Where LLMs Are Used in Security
LLMs are used in two main ways:
Augmenting Security Products
Embedding capabilities like alert summarization, detection enrichment, and threat simulation into existing security solutions.
Custom Security Workflows
Building end-to-end security workflows internally because off-the-shelf products often don't meet evolving needs. These workflows are becoming common across SOCs and security engineering teams pushing toward AI-native operations.
Current Approaches to AI in Security
Proprietary Models (GPT-4, Claude)
- ✓
Strong general reasoning capabilities
- ✓
Fast time-to-value with high-quality prompting
- ✗
Security nightmare: Sensitive data must leave your environment
- ✗
API-only: Impossible to deploy on-premises or in VPC
- ✗
Unreliable fine-tuning that breaks with model updates
Generic Open-Source Models (Llama-3, Mistral)
- ✓
Full data sovereignty and control
- ✓
Deployment flexibility (on-prem, cloud, air-gapped)
- ✗
Significant expertise required for security tuning
- ✗
Substantial resources needed for model optimization
- ✗
Poor security performance without months of custom work
The Foundation AI Solution
Our security-specific foundation models deliver the control and compliance benefits of open-source with the performance and usability of proprietary systems—eliminating the traditional trade-offs.
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