Security Agent Architecture

The Security Agent Architecture for Showmaker is designed to provide threat detection, incident response, and security governance through a cycle of perception, reasoning, learning, and action. The key components of this architecture include:

Perception

1. The agent collects traffic flow data, user input, and potential threat intelligence.

2. This step ensures real-time monitoring and threat recognition.

Data Processing

1. Raw data from the perception phase is processed, analyzed, and transformed into actionable insights.

2. This step prepares the data for deeper reasoning.

Reasoning (LLM + Deep Learning)

1. At the core of the architecture, reasoning leverages large language models (LLMs) and deep learning to evaluate security threats, predict risks, and determine the most appropriate response.

2. The system integrates security knowledge memory and user context memory for contextual decision-making.

Action

1. Based on the reasoning output, the agent executes security responses such as incident response, security operations, and alert generation.

2. This ensures proactive defense and mitigation of security risks.

Communication

1. The agent communicates insights and actions to both users and other agents via APIs.

2. This supports multi-agent collaboration and enhances situational awareness.

Learning

1. The system continuously learns through a feedback loop, improving detection accuracy and response efficiency over time.

2. Continuous improvement is applied across perception, reasoning, and communication components.