Microsoft Security Copilot: AI-Powered Threat Defense

Microsoft Security Copilot brings GPT-4 to cybersecurity operations. Analyze threats, investigate incidents, and generate reports with natural language. This transforms how security teams operate.

Security Copilot Capabilities

from dataclasses import dataclass
from typing import Optional
from datetime import datetime

@dataclass
class SecurityIncident:
    id: str
    severity: str
    title: str
    description: str
    affected_assets: list[str]
    indicators: list[dict]
    timestamp: datetime

class SecurityCopilot:
    """AI-powered security operations assistant."""

    def __init__(self, client, security_apis):
        self.client = client
        self.apis = security_apis

    async def investigate_incident(
        self,
        incident: SecurityIncident
    ) -> dict:
        """Comprehensive incident investigation."""

        # Gather context
        context = await self._gather_context(incident)

        prompt = f"""Investigate this security incident.

Incident: {incident.title}
Severity: {incident.severity}
Description: {incident.description}
Affected assets: {', '.join(incident.affected_assets)}

Indicators of Compromise:
{self._format_indicators(incident.indicators)}

Additional Context:
{context}

Provide:
1. Initial assessment
2. Attack vector analysis
3. Scope of impact
4. Timeline reconstruction
5. Related threats/campaigns
6. Immediate containment steps
7. Evidence to preserve"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a senior security analyst."},
                {"role": "user", "content": prompt}
            ]
        )

        return {
            "investigation": response.content,
            "incident_id": incident.id,
            "timestamp": datetime.utcnow().isoformat()
        }

    async def analyze_threat_intel(
        self,
        indicators: list[dict],
        context: str = None
    ) -> dict:
        """Analyze threat intelligence."""

        iocs_str = self._format_indicators(indicators)

        prompt = f"""Analyze these indicators of compromise.

IOCs:
{iocs_str}

{f'Context: {context}' if context else ''}

Provide:
1. Threat assessment
2. Known associations (malware families, APT groups)
3. Attack patterns indicated
4. Risk level
5. Detection recommendations
6. Hunting queries"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"analysis": response.content}

    async def generate_hunting_query(
        self,
        threat_description: str,
        platform: str = "microsoft_sentinel"
    ) -> dict:
        """Generate threat hunting query."""

        prompt = f"""Generate a threat hunting query.

Threat to hunt: {threat_description}
Platform: {platform}

Generate:
1. KQL/Query for the platform
2. What the query detects
3. Expected false positives
4. Tuning recommendations"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1
        )

        return {"hunting_query": response.content}

    async def explain_alert(
        self,
        alert_data: dict
    ) -> str:
        """Explain security alert in plain language."""

        import json
        alert_str = json.dumps(alert_data, indent=2)

        prompt = f"""Explain this security alert for a non-technical audience.

Alert:
{alert_str}

Provide:
1. What happened (plain language)
2. Why it matters
3. Immediate risk
4. Recommended action"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

    async def generate_incident_report(
        self,
        incident: SecurityIncident,
        investigation_findings: dict,
        audience: str = "executive"
    ) -> str:
        """Generate incident report."""

        import json
        findings_str = json.dumps(investigation_findings, indent=2)

        prompt = f"""Generate a security incident report.

Incident: {incident.title}
Severity: {incident.severity}
Investigation Findings:
{findings_str}

Audience: {audience}

Generate a professional report including:
1. Executive summary
2. Incident timeline
3. Technical details
4. Impact assessment
5. Response actions taken
6. Recommendations
7. Lessons learned"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

    def _format_indicators(self, indicators: list[dict]) -> str:
        """Format IOCs for prompt."""
        lines = []
        for ioc in indicators:
            lines.append(f"- Type: {ioc.get('type')}, Value: {ioc.get('value')}")
        return "\n".join(lines)

    async def _gather_context(self, incident: SecurityIncident) -> str:
        """Gather additional context from security APIs."""
        context_parts = []

        # Get user context
        for asset in incident.affected_assets:
            if "@" in asset:  # Likely a user
                user_context = await self.apis.get_user_context(asset)
                context_parts.append(f"User {asset}: {user_context}")

        # Get recent alerts
        recent = await self.apis.get_recent_alerts(hours=24)
        context_parts.append(f"Recent alerts: {len(recent)} in last 24h")

        return "\n".join(context_parts)

Integration with Microsoft Security Stack

class SecurityStackIntegration:
    """Integrate with Microsoft security products."""

    async def query_sentinel(
        self,
        natural_query: str
    ) -> dict:
        """Convert natural language to KQL for Sentinel."""

        prompt = f"""Convert to KQL for Microsoft Sentinel.

Query: {natural_query}

Generate valid KQL that:
1. Is efficient
2. Handles common edge cases
3. Includes time bounds"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            temperature=0
        )

        return {"kql": response.content}

    async def analyze_defender_alert(
        self,
        defender_alert: dict
    ) -> dict:
        """Analyze Microsoft Defender alert."""

        import json
        alert_str = json.dumps(defender_alert, indent=2)

        prompt = f"""Analyze this Microsoft Defender alert.

Alert:
{alert_str}

Provide:
1. Threat assessment
2. Attack technique (MITRE ATT&CK)
3. Recommended response
4. Additional queries to run"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"analysis": response.content}

Use Cases

1. Incident triage - Quickly assess and prioritize alerts
2. Threat hunting - Generate queries from threat descriptions
3. Report generation - Create executive and technical reports
4. Knowledge augmentation - Explain complex security concepts
5. Response guidance - Step-by-step remediation instructions

Security Copilot addresses the security skills gap by augmenting analysts with AI-powered investigation and response capabilities.