🛡️ Cisco Security - Incident Response AI

Spring Boot + Spring AI + Custom MCP Server

An agentic AI system that autonomously monitors Cisco ISE security logs, analyzes threats with an LLM, and proposes sandboxed firewall rules — all coordinated through a custom MCP (Model Context Protocol) server.

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    REST API + SSE Stream                            │
│  POST /api/incidents/trigger    GET /api/incidents/stream           │
│  GET  /api/incidents/{id}       POST /api/incidents/{id}/approve    │
└───────────────────────────┬─────────────────────────────────────────┘
                            │
              ┌─────────────▼─────────────┐
              │  IncidentResponseWorkflow  │  (Orchestrator)
              └─────────────┬─────────────┘
                            │ Sequential Pipeline
          ┌─────────────────┼──────────────────┐
          │                 │                  │
    ┌─────▼──────┐   ┌──────▼─────┐   ┌───────▼──────┐
    │  MONITOR   │   │  ANALYZER  │   │  REMEDIATOR  │
    │  Agent 1   │──▶│  Agent 2   │──▶│   Agent 3    │
    │            │   │            │   │              │
    │ Scan ISE   │   │ FP detect  │   │ Sandbox rule │
    │ logs via   │   │ + LLM      │   │ + block IP   │
    │ MCP tool   │   │ reasoning  │   │ via MCP      │
    └────────────┘   └────────────┘   └──────────────┘
          │                │                  │
          └────────────────┴──────────────────┘
                           │  MCP Tool Calls
              ┌────────────▼──────────────────────┐
              │      CiscoIseMcpServer (MCP)       │
              │  @Tool  getSecurityLogs()          │
              │  @Tool  getEndpointStatus()        │
              │  @Tool  blockEndpoint()            │
              │  @Tool  testRuleInSandbox()        │
              │  @Tool  getActivePolicies()        │
              └───────────────────────────────────┘
                           │
              ┌────────────▼────────────┐
              │  Anthropic Claude       │
              │  (claude-sonnet-4)      │
              │  via Spring AI          │
              └─────────────────────────┘

Project Structure

incident-response-ai/
├── pom.xml
└── src/main/java/com/security/incident/
    ├── IncidentResponseApplication.java        ← Spring Boot entry point
    │
    ├── mcp/
    │   ├── service/
    │   │   ├── CiscoIseMcpServer.java          ← MCP Server wrapper
    │   │   ├── CiscoIseClient.java             ← [NEW] Gateway interface
    │   │   └── RestCiscoIseClient.java         ← [NEW] Real REST API impl
    │   └── model/
    │       ├── LogEntry.java                   ← ISE log event model
    │       └── SecurityModels.java             ← Endpoint/Block/Sandbox models
    │
    ├── agents/
    │   ├── MonitorAgent.java                   ← Agent 1: Log scanning
    │   ├── AnalyzerAgent.java                  ← Agent 2: Threat analysis
    │   └── RemediatorAgent.java                ← Agent 3: Rule + block
    │
    ├── workflow/
    │   ├── IncidentResponseWorkflow.java       ← Pipeline orchestrator
    │   └── IncidentReport.java                 ← Shared state carrier
    │
    ├── events/
    │   └── AgentEvent.java                     ← SSE broadcast events
    │
    ├── config/
    │   └── AgentConfig.java                    ← Spring AI + MCP wiring
    │
    └── controller/
        └── IncidentController.java             ← REST + SSE endpoints

Tech Stack

Setup & Run

1. Prerequisites

• Java 21+
• Maven 3.9+
• Anthropic API Key

2. Set your API key

export ANTHROPIC_API_KEY=sk-ant-...

3. Build & run

cd incident-response-ai
mvn clean package -DskipTests
java -jar target/incident-response-ai-1.0.0.jar

API Usage

Trigger the 3-agent pipeline

curl -X POST http://localhost:8080/api/incidents/trigger

Response:

{
  "status": "PIPELINE_STARTED",
  "message": "Incident response pipeline is running...",
  "stream": "/api/incidents/stream"
}

Watch real-time SSE events (in another terminal)

curl -N http://localhost:8080/api/incidents/stream

You'll see events like:

event: agent-event
data: {"agent":"MonitorAgent","status":"STARTED","message":"Scanning Cisco ISE logs..."}

event: agent-event
data: {"agent":"MonitorAgent","status":"COMPLETED","message":"Found 2 suspicious IPs: [185.220.101.47, 198.199.67.82]"}

event: agent-event
data: {"agent":"AnalyzerAgent","status":"STARTED","message":"Analyzing IP 185.220.101.47..."}

event: agent-event
data: {"agent":"RemediatorAgent","status":"COMPLETED","message":"Sandbox APPROVED, block applied, rollback: RBK-1002"}

Get incident report

curl http://localhost:8080/api/incidents/INC-ABC12345

Approve the AI's recommended remediation (human-in-the-loop)

curl -X POST http://localhost:8080/api/incidents/INC-ABC12345/approve

Agent Decision Logic

MonitorAgent
    │ Calls: getSecurityLogs(50, "ALL")
    │ Flags IPs with: auth_attempts > 5, EventType IN [BRUTE_FORCE, PORT_SCAN...]
    ▼
    Suspicious IPs found?
    ├── NO  → Close incident: CLEAN
    └── YES ▼
        AnalyzerAgent
            │ Calls: getEndpointStatus(primaryIp)
            │ LLM evaluates: threat score, posture, compliance tags, ISE profile
            ▼
            Verdict?
            ├── FALSE_POSITIVE        → Close: FALSE_POSITIVE (no action)
            ├── confidence < 0.6      → Close: ESCALATED (human SOC review)
            └── GENUINE_THREAT ▼
                RemediatorAgent
                    │ Designs FTD block rule
                    │ Calls: testRuleInSandbox(...)
                    │ Sandbox result?
                    │   REJECT / false_positives found → DEFERRED (human review)
                    │   APPROVE ▼
                    │ Calls: blockEndpoint(ip, reason, QUARANTINE)
                    ▼
                    Status: AWAITING_APPROVAL
                    (human must call /approve to push to production)

MCP Server Tools

The CiscoIseMcpServer exposes 5 tools via @Tool annotations:

These tools are auto-registered by Spring AI's MCP Server Starter and available:

• In-process: via MethodToolCallbackProvider (used by agents in this app)
• Via MCP stdio: any external MCP client (Claude Desktop, etc.) can connect

Extending to Production

Learning Outcomes (SDE Transition)

This project demonstrates:

• ✅ Spring Boot 3 – REST APIs, SSE streaming, async processing
• ✅ Spring AI – ChatClient, tool calling, agentic loops
• ✅ MCP Protocol – Custom server with @Tool annotations
• ✅ Agent Design Patterns – Monitor → Analyze → Remediate pipeline
• ✅ Human-in-the-loop – Safety gate before production changes
• ✅ Domain-Driven Design – Rich domain model (IncidentReport)
• ✅ Event-Driven – ApplicationEventPublisher + @EventListener