> ## Documentation Index
> Fetch the complete documentation index at: https://threatbasis.io/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Knowledge Base

> Comprehensive knowledge base for applying AI and Large Language Models to security engineering, operations, and threat detection.

The AI Knowledge Base provides security engineers with practical guidance on leveraging artificial intelligence and Large Language Models (LLMs) to enhance security operations, automate threat detection, and improve incident response. As AI capabilities rapidly evolve, security teams must understand both the opportunities and risks these technologies present.

This knowledge base bridges the gap between AI/ML capabilities and security engineering practice. Content focuses on actionable implementation patterns, security-specific considerations, and integration strategies that work within enterprise security architectures.

## Why AI for Security Engineering?

Security operations face exponential growth in data volume, alert fatigue, and sophisticated threats that outpace human analysis capabilities. AI and LLMs offer transformative potential across the security lifecycle:

| Challenge        | AI/LLM Capability                      | Security Application                               |
| ---------------- | -------------------------------------- | -------------------------------------------------- |
| Alert fatigue    | Pattern recognition, anomaly detection | Intelligent alert triage and prioritization        |
| Knowledge gaps   | Semantic search, knowledge retrieval   | Instant access to threat intelligence and runbooks |
| Manual analysis  | Natural language processing            | Automated log analysis and report generation       |
| Skill shortages  | Workflow automation                    | AI-assisted investigation and response             |
| Threat evolution | Continuous learning                    | Adaptive detection and threat hunting              |

## Knowledge Domains

Explore our structured AI knowledge base, designed for security engineers integrating AI capabilities into their security programs.

### AI Foundations

Core concepts and techniques for working with LLMs in security contexts.

<CardGroup cols={3}>
  <Card title="Prompt Engineering for Security" icon="terminal" href="/ai-knowledge/prompt-engineering-for-security">
    Security-specific prompt patterns, chain-of-thought reasoning, and
    adversarial testing
  </Card>

  <Card title="Context Window Management" icon="window-maximize" href="/ai-knowledge/context-window-management">
    Strategies for managing limited context windows with security logs and
    documentation
  </Card>

  <Card title="Context Compression & Distillation" icon="compress" href="/ai-knowledge/context-compression-and-distillation">
    Techniques for reducing token usage while preserving semantic meaning in
    security contexts
  </Card>
</CardGroup>

### AI Architecture & Patterns

Advanced patterns for building AI-powered security systems.

<CardGroup cols={2}>
  <Card title="AI Orchestration for Security" icon="robot" href="/ai-knowledge/ai-orchestration-for-security">
    AI agents and workflows for automated threat response and security
    decision-making
  </Card>

  <Card title="Advanced RAG" icon="database" href="/ai-knowledge/advanced-rag">
    Retrieval-Augmented Generation for security knowledge bases and threat
    intelligence
  </Card>
</CardGroup>

### AI Integration

Connecting AI systems with security infrastructure.

<CardGroup cols={1}>
  <Card title="AI Security Tooling Integration" icon="plug" href="/ai-knowledge/ai-security-tooling-integration">
    Integrating LLMs with SIEM, SOAR, EDR, and security platforms
  </Card>
</CardGroup>

### AI Security & Governance

Protecting AI systems and defending against AI-powered threats.

<CardGroup cols={3}>
  <Card title="Defending Against AI Threats" icon="shield-halved" href="/ai-knowledge/defending-against-ai-threats">
    Counter AI-powered phishing, deepfakes, malware, and adversarial attacks
  </Card>

  <Card title="AI Red Teaming" icon="user-secret" href="/ai-knowledge/ai-red-teaming">
    Test AI systems for prompt injection, jailbreaking, and data extraction
  </Card>

  <Card title="AI Governance & Compliance" icon="gavel" href="/ai-knowledge/ai-governance-and-compliance">
    Frameworks, policies, and compliance for AI in security contexts
  </Card>
</CardGroup>

## Security Considerations for AI Systems

Deploying AI in security contexts introduces unique risks that must be addressed:

* **Prompt injection attacks** — Adversaries may attempt to manipulate AI systems through crafted inputs
* **Data leakage** — LLMs may inadvertently expose sensitive information from training or context
* **Hallucination risks** — AI-generated security recommendations must be validated before action
* **Model poisoning** — Training data integrity is critical for security-focused models
* **Adversarial evasion** — Attackers may craft inputs specifically designed to evade AI detection

<Note>
  The AI Knowledge Base focuses on practical implementation for security
  engineers. Content assumes familiarity with security operations fundamentals
  and basic AI/ML concepts.
</Note>

## Getting Started

<CardGroup cols={2}>
  <Card title="Start with AI Orchestration" icon="play" href="/ai-knowledge/ai-orchestration-for-security">
    Begin with understanding how AI agents can enhance security workflows
  </Card>

  <Card title="Explore RAG for Security" icon="magnifying-glass" href="/ai-knowledge/advanced-rag">
    Learn how to build AI-powered security knowledge retrieval systems
  </Card>
</CardGroup>