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LangChain Agent Micro-segmentation — AI Agent
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LangChain Agent Micro-segmentation — AI Agent

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LangChain Micro-segmentation

LangChain Agent Micro-segmentation — AI Agent

LangChain Agent Micro-segmentation AI Network Security Zero Trust Policy Automation ReAct GPT-4 Cilium Calico VMware NSX Lateral Movement Prevention

ToolTypeLayerAI Integrationเหมาะกับ
CiliumeBPF-basedL3-L7APIKubernetes
CalicoNetwork PolicyL3-L4APIKubernetes
VMware NSXSDNL2-L7APIVMware Env
IllumioHost-basedL3-L7Built-inEnterprise
GuardicoreAgent-basedL3-L7Built-inData Center

LangChain Agent Setup

=== LangChain Security Agent ===

pip install langchain langchain-openai

from langchain.agents import AgentExecutor, create_react_agent

from langchain_openai import ChatOpenAI

from langchain.tools import Tool

from langchain.prompts import PromptTemplate

# Tools for Network Security

def analyze_traffic(query: str) -> str:

"""Analyze network traffic patterns"""

# Query traffic database

return f"Traffic analysis: {query}"

def create_policy(spec: str) -> str:

"""Create Kubernetes NetworkPolicy"""

# Generate and apply policy

return f"Policy created: {spec}"

def check_compliance(namespace: str) -> str:

"""Check security compliance"""

# Verify policies

return f"Compliance check: {namespace}"

def block_ip(ip: str) -> str:

"""Block suspicious IP address"""

# Add to blocklist

return f"Blocked: {ip}"

tools = [

Tool(name="TrafficAnalyzer", func=analyze_traffic,

description="Analyze network traffic patterns and anomalies"),

Tool(name="PolicyCreator", func=create_policy,

description="Create Kubernetes NetworkPolicy YAML"),

Tool(name="ComplianceChecker", func=check_compliance,

description="Check namespace security compliance"),

Tool(name="IPBlocker", func=block_ip,

description="Block suspicious IP address"),

]

llm = ChatOpenAI(model="gpt-4o", temperature=0)

agent = create_react_agent(llm, tools, prompt)

executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Example: Auto-analyze and create policy

result = executor.invoke({

"input": "Analyze traffic for namespace 'payment' and create "

"micro-segmentation policy allowing only required connections"

})

from dataclasses import dataclass

@dataclass

class AgentTool:

name: str

function: str

input_type: str

output: str

risk_level: str

agent_tools = [

AgentTool("TrafficAnalyzer", "วิเคราะห์ Traffic Pattern", "Namespace/Pod", "Traffic Report", "Low"),

AgentTool("PolicyCreator", "สร้าง NetworkPolicy", "Policy Spec", "YAML Applied", "Medium"),

AgentTool("ComplianceChecker", "ตรวจ Compliance", "Namespace", "Compliance Score", "Low"),

AgentTool("IPBlocker", "Block IP ที่น่าสงสัย", "IP Address", "Block Confirmed", "High"),

AgentTool("LogAnalyzer", "วิเคราะห์ Security Log", "Time Range", "Threat Report", "Low"),

AgentTool("VulnScanner", "Scan Vulnerabilities", "Target", "CVE List", "Medium"),

]

print("=== Agent Tools ===")

for t in agent_tools:

print(f" [{t.risk_level}] {t.name}")

print(f" Function: {t.function}")

print(f" Input: {t.input_type} | Output: {t.output}")

Kubernetes Network Policy

=== Micro-segmentation with Cilium ===

Kubernetes NetworkPolicy — Deny All Default

apiVersion: networking.k8s.io/v1

kind: NetworkPolicy

metadata:

name: default-deny-all

namespace: payment

spec:

podSelector: {}

policyTypes:

  • Ingress
  • Egress

Allow specific traffic — Payment Service

apiVersion: networking.k8s.io/v1

kind: NetworkPolicy

metadata:

name: allow-payment-api

namespace: payment

spec:

podSelector:

matchLabels:

app: payment-api

policyTypes:

  • Ingress
  • Egress

ingress:

  • from:
  • namespaceSelector:

matchLabels:

LangChain Agent Micro-segmentation — AI Agent

name: frontend

  • podSelector:

matchLabels:

app: checkout

ports:

  • protocol: TCP

port: 8080

egress:

  • to:
  • podSelector:

matchLabels:

app: payment-db

ports:

  • protocol: TCP

port: 5432

  • to:
  • ipBlock:

cidr: 0.0.0.0/0

ports:

  • protocol: TCP

port: 443 # Stripe API

Cilium L7 Policy — HTTP Method Filtering

apiVersion: cilium.io/v2

kind: CiliumNetworkPolicy

metadata:

name: payment-l7

spec:

endpointSelector:

matchLabels:

app: payment-api

ingress:

  • fromEndpoints:
  • matchLabels:

app: checkout

toPorts:

  • ports:
  • port: "8080"

rules:

http:

  • method: POST

path: "/api/v1/charge"

  • method: GET

path: "/api/v1/status/.*"

@dataclass

class SegmentPolicy:

namespace: str

service: str

allowed_ingress: str

allowed_egress: str

l7_filter: bool

status: str

policies = [

SegmentPolicy("payment", "payment-api", "checkout only", "payment-db + Stripe", True, "Enforced"),

SegmentPolicy("payment", "payment-db", "payment-api only", "None (isolated)", False, "Enforced"),

SegmentPolicy("frontend", "web-app", "ALB ingress", "api-gateway", False, "Enforced"),

SegmentPolicy("backend", "api-gateway", "web-app + mobile", "All backend services", True, "Enforced"),

SegmentPolicy("monitoring", "prometheus", "All namespaces (scrape)", "alertmanager", False, "Enforced"),

]

print("\n=== Segmentation Policies ===")

for p in policies:

l7 = "L7" if p.l7_filter else "L3/L4"

print(f" [{p.status}] {p.namespace}/{p.service}")

print(f" Ingress: {p.allowed_ingress}")

print(f" Egress: {p.allowed_egress} | Filter: {l7}")

AI-powered Security

# === AI Security Automation ===

# Agent Workflow:
# 1. Collect traffic data from Cilium Hubble
# 2. LLM analyzes patterns and anomalies
# 3. Generate recommended policies
# 4. Human approval (or auto-apply low-risk)
# 5. Monitor and adjust

# Traffic Analysis Prompt
# """
# Analyze the following network traffic for namespace 'payment':
# - Source: checkout-pod -> payment-api:8080 (500 req/min)
# - Source: unknown-pod -> payment-api:8080 (50 req/min)
# - Source: payment-api -> payment-db:5432 (480 req/min)
# - Source: payment-api -> external:443 (100 req/min)
#
# Identify:
# 1. Normal traffic patterns
# 2. Suspicious connections
# 3. Recommended NetworkPolicy
# """

@dataclass
class SecurityEvent:
    time: str
    source: str
    destination: str
    action: str
    risk: str
    ai_decision: str

events = [
    SecurityEvent("14:23:15", "checkout", "payment-api:8080", "ALLOW", "Low", "Normal traffic"),
    SecurityEvent("14:23:45", "unknown-pod", "payment-api:8080", "ALERT", "High", "Block + investigate"),
    SecurityEvent("14:24:00", "payment-api", "payment-db:5432", "ALLOW", "Low", "Normal DB query"),
    SecurityEvent("14:24:30", "payment-api", "external:443", "ALLOW", "Medium", "Stripe API call"),
    SecurityEvent("14:25:00", "debug-pod", "payment-db:5432", "DENY", "Critical", "Unauthorized DB access"),
    SecurityEvent("14:25:30", "scanner-pod", "payment-api:22", "DENY", "Critical", "Port scan detected"),
]

print("AI Security Events:")
for e in events:
    print(f"  [{e.risk}] {e.time} | {e.source} -> {e.destination}")
    print(f"    Action: {e.action} | AI: {e.ai_decision}")

security_metrics = {
    "Policies Enforced": "28",
    "Namespaces Protected": "8",
    "Blocked Connections (24h)": "1,250",
    "AI-generated Policies": "12",
    "False Positive Rate": "2.1%",
    "Mean Time to Detect": "15 seconds",
    "Mean Time to Respond": "45 seconds",
    "Compliance Score": "96%",
}

print(f"\n\nSecurity Dashboard:")
for k, v in security_metrics.items():
    print(f"  {k}: {v}")

เคล็ดลับ

  • Default Deny: เริ่มจาก Deny All แล้วค่อย Allow
  • L7: ใช้ Cilium L7 Policy กรอง HTTP Method Path
  • AI: ใช้ LangChain Agent วิเคราะห์ Traffic แนะนำ Policy
  • Human-in-loop: AI แนะนำ แต่คนอนุมัติ High-risk Policy
  • Monitor: ใช้ Hubble ดู Traffic Real-time ทุก Namespace

LangChain Agent คืออะไร

AI Agent LLM ตัดสินใจ Tool ReAct คิด-ทำ-สังเกต API Database Search GPT-4 Claude Autonomous วิเคราะห์ Network สร้าง Policy อัตโนมัติ

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