Sujeet Bambawale

Security Operations Metrics & Executive Risk Dashboard Synthesis Agent

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Specialized Cyber Defense & Operational Agents

Problem Statement

CISOs and security leadership need high-level security posture visibility (MTTR, MTTD, Patch Compliance, Incident Trends), but pulling raw metrics from 10+ disconnected tools (SIEM, EDR, Vulnerability Scanners, Ticketing systems) to build executive reporting dashboards is manual and time-consuming.

Goal

Build a master synthesis agent that ingests raw telemetry metrics across SOC, GRC, and Vulnerability management modules, calculates key operational metrics, and generates an executive risk briefing dashboard.

Outcome Measurement

  1. Dashboard Preparation Time: Reporting compilation time reduced from 2 weeks to under 1 minute.
  2. Executive Alignment: Instant generation of executive ready risk metrics and strategic recommendations.

How the Code was Built

Aggregates heterogeneous security operational indicators and maps them into an executive Markdown dashboard.

Executable Code

from typing import List
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
class ExecutiveMetricsDashboard(BaseModel):
    overall_posture_score: int = Field(description="0 to 100 enterprise security score")
    key_metrics: dict = Field(description="MTTR, MTTD, Critical Vulnerability Count, etc.")
    top_security_risks: List[str]
    strategic_recommendations: List[str]
    executive_summary_markdown: str
def synthesize_executive_dashboard(soc_metrics: dict, grc_metrics: dict, vuln_metrics: dict) -> ExecutiveMetricsDashboard:
    llm = ChatOpenAI(model="gpt-4o", temperature=0)
    structured_llm = llm.with_structured_output(ExecutiveMetricsDashboard)
    prompt = f"""
    Synthesize an Executive CISO Security Dashboard from these raw operational metrics:
    SOC METRICS: {soc_metrics}
    GRC METRICS: {grc_metrics}
    VULNERABILITY METRICS: {vuln_metrics}
    """
    return structured_llm.invoke(prompt)
if __name__ == "__main__":
    soc = {"mttd_minutes": 4.5, "mttr_hours": 1.2, "alerts_triaged_auto_pct": 82}
    grc = {"soc2_compliance_pct": 94, "iso27001_status": "Compliant", "open_policy_gaps": 2}
    vuln = {"critical_cve_count": 5, "avg_patch_days": 3.2, "kev_vulnerabilities": 0}
    dashboard = synthesize_executive_dashboard(soc, grc, vuln)
    print(f"Enterprise Security Score: {dashboard.overall_posture_score}/100\n")
    print("Executive Summary:\n", dashboard.executive_summary_markdown)
    print("\nTop Risks:")
    for risk in dashboard.top_security_risks:
        print(f"  - {risk}")

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