AgenticAI SOC: AI-Powered Security Operations for Cybersecurity Threat Detection

Introduction
In the evolving landscape of cybersecurity, AI-powered Security Operations Centers (SOC) play a critical role in threat detection and response. AgenticAI SOC used the Red Teaming approach which leverages AI agents to proactively simulate, detect, and mitigate cyber threats using advanced AI Agents. This article explores the technical implementation of a simple AI-driven SOC by dissecting two Python scripts that utilize LangChain for security attack simulations.
Overview of the Code
Both scripts, agent_mitm.py and AI_Agent_attack_sim.py, employ LangChain to create AI agents capable of simulating security threats. These agents utilize tools such as Python REPL and terminal access to perform attack simulations while ensuring ethical constraints to play the role of Red teaming. We could actually use the EPSS based Vulnerability Risk scoring mechanism to detect the likelihood of a Security Threat happening.
Key Functionalities
Man-in-the-Middle (MITM) Attack Simulation with agent_mitm.py:
Loading Agentic Tools:

from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI
tools = load_tools(["python_repl", "terminal"], llm=OpenAI(temperature=0))
LangChain tools like python_repl and terminal are loaded to enable command execution.
OpenAI's language model acts as the reasoning engine.

2. Agent Initialization:
   prompt = "You are an AI agent tasked with simulating security threat attacks…"
   agent = initialize_agent(tools, OpenAI(temperature=0), agent="zero-shot-react-description", verbose=True, prompt=prompt)
   The agent hereis configured with a predefined prompt to conduct security threat analysis simulations. There could be few-shot prompting or Zero shot ReACT prompting or advanced prompting techniques given during the Agent initialization phase for simulating the Security Threat attack.

3. Defining a Malicious Tool:
   def validate_input(input):
   if "malicious" in input:
   raise ValueError("Malicious input detected!")
   return input
   A security validation function prevents un-authorized input execution.

4. Executing MITM Attack Simulation:
   def malicious_tool(input):
   validated_input = validate_input(input)

   Simulate a MITM attack

   print("MITM attack simulation:")
   print(f"Intercepting input: {validated_input}")

   Perform MITM logic here...

   Modify or redirect the input as necessary

   modified_input = "modified_input"
   return modified_input
   '''
   This function simulates a MITM attack but incorporates validation mechanisms to prevent real-world exploitation.
   AI_Agent_attack_sim.py: Agentic AI based Attack Simulation
   This script follows a similar structure but generalizes the attack simulation framework.

5. Generalized Attack Simulation:
   tool_description = "This tool is malicious and can be used to launch attacks."
   Instead of focusing on MITM, this script allows the agent to simulate broader cyber threats.

6. Modular Threat Simulation:
   def malicious_tool(input):
   validated_input = validate_input(input)
   return f"Simulating MITM attack with input: {validated_input}"
   The attack function is modular, making it adaptable to various security scenarios depending on various attack Path chains given as arguments to this module.

Reference code implementation is provided in the Github: https://github.com/akramIOT/AISOC_AGENT
7. AgenticAI SOC Architecture
Here is a detailed view on how AI agents interact with the environment to simulate and detect cyber threats in real time.
Agentic SOC (Security Orchestration, Automation, and Response) is an emerging Security architecture that focuses on automation and orchestration of security operations to improve detection, response, and prevention of threats. Here's a summary of the key components and functionality of the components:
AgenticAI SOC Workflow Summary:
AI agent receives security-related input
The agent validates the input for compliance
Attack simulation is executed within a controlled sandbox
Outputs are analyzed to detect vulnerabilities

7.1) Security Orchestration:
Centralized management of security operations
Centralized control and configuration of security tools and systems
Automated orchestration of incident response and remediation

7.2) Security Automation:
Automated detection and response to security incidents
Integration of security tools and processes
Continuous monitoring and analysis of security data

7.3) Security Response:
Automated incident response and remediation
Real-time incident handling and coordination
Integrated incident management and reporting

7.4) Security Orchestration and Automation:
Centralized management of security operations
Automated orchestration and response to incidents
Integrated security monitoring and analysis

7.5) Security Information and Event Management (SIEM):
Centralized collection, normalization, and correlation of security data
Real-time analysis of security events and logs
Automated response to security events and threats

7.6) Security Orchestration, Automation, and Response (SOAR):
Integrated security orchestration and automation
Automated incident response and remediation
Real-time collaboration and communication between security teams

7.7) Security Operations Center (SOC):
Centralized security operations team
Responsible for managing security incidents and incidents
Provides guidance and support to other security teams

7.8) Agentic Security Information and Event Management (SIEM):
Centralized collection, normalization, and correlation of security data
Real-time analysis of security events and logs
Automated response to security events and threats
Swarm of Agents doing the centralized log analysis and correlation.

7.9) Security Orchestration, Automation, and Response (SOAR):
Integrated security orchestration and automation
Automated incident response and remediation
Real-time collaboration and communication between security teams

7.10) Security Operations Center (SOC):
Centralized security operations team
Responsible for managing security incidents and incidents
Provides guidance and support to other security teams

The above AgenticAI driven SOC architecture focuses on automation, orchestration, and real-time response to security incidents, with the goal of improving (TDIR )detection, response, and prevention of threats. It integrates security tools and processes to provide a unified view of security data and incidents.
Conclusion & Future Industry Direction:
AgenticAI SOC presents an Innovative approach and paradigm shift in cybersecurity domain, by leveraging AI agents to proactively simulate threats and enhance security postures. Future enhancements could include the following below:
Expanding agent capabilities with reinforcement learning
Integrating real-time threat intelligence data feeds
Strengthening ethical AI safeguards for responsible cybersecurity use.
Ensuring GRC for the entire system

By embedding AI-driven automation into SOC workflows, organizations can achieve a more robust, proactive, and efficient security posture analysis with different automated workflows.