Technology Leadership Governance: Enterprise Architecture and Portfolio-Level Risk Management

from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import numpy as np
from datetime import datetime, timedelta

class RiskCategory(Enum):
    SECURITY = "security"
    COMPLIANCE = "compliance"
    OPERATIONAL = "operational"
    STRATEGIC = "strategic"
    FINANCIAL = "financial"

class RiskSeverity(Enum):
    CRITICAL = 5
    HIGH = 4
    MEDIUM = 3
    LOW = 2
    MINIMAL = 1

@dataclass
class TechnologyRisk:
    risk_id: str
    category: RiskCategory
    severity: RiskSeverity
    probability: float  # 0.0 to 1.0
    impact_description: str
    financial_impact_range: Tuple[int, int]  # Min, Max USD
    affected_systems: List[str]
    mitigation_strategies: List[str]
    compliance_implications: List[str]
    timeline_to_materialization: timedelta
    detection_methods: List[str]
    response_procedures: List[str]

@dataclass
class PortfolioCompanyRiskProfile:
    company_id: str
    company_name: str
    business_model: str
    technology_maturity: float  # 1.0 to 5.0
    current_risks: List[TechnologyRisk]
    risk_tolerance: float  # 0.0 to 1.0
    compliance_requirements: List[str]
    critical_systems: List[str]
    investment_timeline: timedelta
    exit_strategy_requirements: List[str]

class PortfolioTechnologyRiskManager:
    """Enterprise-scale technology risk management for PE portfolios."""

    def __init__(self, portfolio_companies: List[PortfolioCompanyRiskProfile]):
        self.portfolio_companies = portfolio_companies
        self.risk_correlation_matrix = self._build_risk_correlation_matrix()
        self.systemic_risk_thresholds = self._define_systemic_risk_thresholds()

    def assess_portfolio_wide_risks(self) -> Dict[str, Any]:
        """Assess technology risks across entire portfolio."""

        # Identify systemic risks affecting multiple companies
        systemic_risks = self._identify_systemic_risks()

        # Calculate portfolio-level risk exposure
        total_risk_exposure = self._calculate_portfolio_risk_exposure()

        # Assess risk correlation and concentration
        risk_concentration = self._analyze_risk_concentration()

        # Evaluate compliance risk across portfolio
        compliance_risk_assessment = self._assess_compliance_risks()

        # Generate risk mitigation recommendations
        mitigation_recommendations = self._generate_portfolio_mitigation_strategies()

        return {
            "assessment_date": datetime.now(),
            "systemic_risks": systemic_risks,
            "total_risk_exposure": total_risk_exposure,
            "risk_concentration": risk_concentration,
            "compliance_assessment": compliance_risk_assessment,
            "mitigation_recommendations": mitigation_recommendations,
            "executive_summary": self._generate_executive_risk_summary()
        }

    def _identify_systemic_risks(self) -> List[Dict[str, Any]]:
        """Identify risks that could affect multiple portfolio companies."""

        systemic_risks = []
        risk_frequency = {}

        # Count risk types across portfolio
        for company in self.portfolio_companies:
            for risk in company.current_risks:
                risk_key = f"{risk.category.value}_{hash(risk.impact_description)}"
                if risk_key not in risk_frequency:
                    risk_frequency[risk_key] = {
                        "risk_template": risk,
                        "affected_companies": [],
                        "total_financial_impact": 0
                    }

                risk_frequency[risk_key]["affected_companies"].append(company.company_id)
                risk_frequency[risk_key]["total_financial_impact"] += risk.financial_impact_range[1]

        # Identify risks affecting multiple companies
        for risk_key, risk_data in risk_frequency.items():
            if len(risk_data["affected_companies"]) >= 2:  # Systemic threshold
                systemic_risk = {
                    "risk_type": risk_data["risk_template"].category.value,
                    "description": risk_data["risk_template"].impact_description,
                    "affected_companies": risk_data["affected_companies"],
                    "portfolio_financial_impact": risk_data["total_financial_impact"],
                    "systemic_severity": self._calculate_systemic_severity(risk_data),
                    "recommended_portfolio_response": self._recommend_systemic_response(risk_data)
                }
                systemic_risks.append(systemic_risk)

        return sorted(systemic_risks, key=lambda x: x["portfolio_financial_impact"], reverse=True)

    def _calculate_portfolio_risk_exposure(self) -> Dict[str, float]:
        """Calculate total portfolio risk exposure by category."""

        exposure_by_category = {category.value: 0 for category in RiskCategory}

        for company in self.portfolio_companies:
            for risk in company.current_risks:
                expected_loss = (
                    risk.probability *
                    (risk.financial_impact_range[0] + risk.financial_impact_range[1]) / 2
                )
                exposure_by_category[risk.category.value] += expected_loss

        total_exposure = sum(exposure_by_category.values())

        return {
            "total_portfolio_exposure": total_exposure,
            "exposure_by_category": exposure_by_category,
            "average_company_exposure": total_exposure / len(self.portfolio_companies),
            "risk_concentration_ratio": max(exposure_by_category.values()) / total_exposure if total_exposure > 0 else 0
        }

    def optimize_portfolio_risk_mitigation(self, budget_constraint: int) -> Dict[str, Any]:
        """Optimize risk mitigation investments across portfolio within budget."""

        # Generate all possible mitigation options across portfolio
        mitigation_options = []

        for company in self.portfolio_companies:
            for risk in company.current_risks:
                for mitigation in risk.mitigation_strategies:
                    mitigation_cost = self._estimate_mitigation_cost(risk, mitigation, company)
                    risk_reduction = self._estimate_risk_reduction(risk, mitigation)

                    mitigation_options.append({
                        "company_id": company.company_id,
                        "risk_id": risk.risk_id,
                        "mitigation_strategy": mitigation,
                        "cost": mitigation_cost,
                        "risk_reduction": risk_reduction,
                        "roi": risk_reduction / mitigation_cost if mitigation_cost > 0 else 0
                    })

        # Sort by ROI and select within budget
        mitigation_options.sort(key=lambda x: x["roi"], reverse=True)

        selected_mitigations = []
        total_cost = 0
        total_risk_reduction = 0

        for option in mitigation_options:
            if total_cost + option["cost"] <= budget_constraint:
                selected_mitigations.append(option)
                total_cost += option["cost"]
                total_risk_reduction += option["risk_reduction"]

        return {
            "selected_mitigations": selected_mitigations,
            "total_investment": total_cost,
            "total_risk_reduction": total_risk_reduction,
            "portfolio_roi": total_risk_reduction / total_cost if total_cost > 0 else 0,
            "unused_budget": budget_constraint - total_cost
        }

class PortfolioComplianceManager:
    """Manage compliance requirements across PE portfolio companies."""

    def __init__(self, compliance_requirements: Dict[str, List[str]]):
        self.compliance_requirements = compliance_requirements
        self.compliance_frameworks = self._initialize_compliance_frameworks()
        self.audit_schedules = self._generate_audit_schedules()

    def assess_portfolio_compliance_status(self) -> Dict[str, Any]:
        """Assess compliance status across entire portfolio."""

        compliance_status = {}

        for company_id, requirements in self.compliance_requirements.items():
            company_status = {
                "overall_compliance_score": 0,
                "requirement_status": {},
                "critical_gaps": [],
                "remediation_timeline": {},
                "certification_status": {}
            }

            for requirement in requirements:
                requirement_assessment = self._assess_requirement_compliance(
                    company_id, requirement
                )
                company_status["requirement_status"][requirement] = requirement_assessment

                if requirement_assessment["compliance_percentage"] < 0.9:
                    company_status["critical_gaps"].append({
                        "requirement": requirement,
                        "gap_description": requirement_assessment["gaps"],
                        "risk_level": requirement_assessment["risk_assessment"],
                        "remediation_cost": requirement_assessment["remediation_cost"]
                    })

            company_status["overall_compliance_score"] = self._calculate_overall_score(
                company_status["requirement_status"]
            )

            compliance_status[company_id] = company_status

        return {
            "portfolio_compliance_summary": self._generate_portfolio_summary(compliance_status),
            "company_assessments": compliance_status,
            "systemic_compliance_risks": self._identify_systemic_compliance_risks(compliance_status),
            "investment_recommendations": self._recommend_compliance_investments(compliance_status)
        }

    def implement_portfolio_compliance_standards(self) -> Dict[str, Any]:
        """Implement standardized compliance frameworks across portfolio."""

        # Define common compliance architecture
        standard_architecture = {
            "identity_management": {
                "sso_implementation": "okta_or_azure_ad",
                "mfa_requirement": "hardware_tokens_for_privileged_access",
                "access_review": "quarterly_automated_reviews"
            },

            "data_protection": {
                "encryption_standards": "aes_256_at_rest_tls_1_3_in_transit",
                "backup_requirements": "encrypted_offsite_3_2_1_strategy",
                "retention_policies": "automated_data_lifecycle_management"
            },

            "security_monitoring": {
                "siem_solution": "centralized_portfolio_siem",
                "incident_response": "standardized_playbooks_and_procedures",
                "threat_intelligence": "shared_portfolio_threat_feeds"
            },

            "audit_and_reporting": {
                "log_management": "centralized_immutable_audit_logs",
                "compliance_reporting": "automated_regulatory_report_generation",
                "control_testing": "continuous_control_monitoring"
            }
        }

        implementation_plan = {}

        for company_id in self.compliance_requirements.keys():
            company_plan = self._generate_implementation_plan(
                company_id, standard_architecture
            )
            implementation_plan[company_id] = company_plan

        return {
            "standard_architecture": standard_architecture,
            "implementation_timeline": self._optimize_implementation_timeline(implementation_plan),
            "shared_service_opportunities": self._identify_shared_services(implementation_plan),
            "cost_optimization": self._calculate_portfolio_cost_savings(implementation_plan)
        }

class PortfolioTechnologyInvestmentOptimizer:
    """Optimize technology investments across PE portfolio for maximum value creation."""

    def __init__(self, portfolio_data: Dict[str, Any], investment_constraints: Dict[str, Any]):
        self.portfolio_data = portfolio_data
        self.investment_constraints = investment_constraints
        self.roi_models = self._initialize_roi_models()

    def optimize_technology_investments(self, total_budget: int, investment_horizon: timedelta) -> Dict[str, Any]:
        """Optimize technology investments across portfolio within budget and timeline constraints."""

        # Generate investment opportunities across portfolio
        investment_opportunities = self._generate_investment_opportunities()

        # Model expected returns for each opportunity
        opportunity_models = []
        for opportunity in investment_opportunities:
            model = self._model_investment_returns(opportunity, investment_horizon)
            opportunity_models.append(model)

        # Optimize investment allocation using portfolio theory
        optimal_allocation = self._optimize_investment_allocation(
            opportunity_models, total_budget, investment_horizon
        )

        # Assess portfolio-level synergies
        synergy_benefits = self._assess_cross_portfolio_synergies(optimal_allocation)

        # Calculate risk-adjusted returns
        risk_adjusted_analysis = self._calculate_risk_adjusted_returns(
            optimal_allocation, synergy_benefits
        )

        return {
            "optimal_allocation": optimal_allocation,
            "expected_portfolio_returns": risk_adjusted_analysis,
            "synergy_benefits": synergy_benefits,
            "implementation_roadmap": self._generate_implementation_roadmap(optimal_allocation),
            "risk_mitigation_requirements": self._identify_risk_mitigation_needs(optimal_allocation)
        }

    def _model_investment_returns(self, opportunity: Dict[str, Any], timeline: timedelta) -> Dict[str, Any]:
        """Model expected returns for technology investment opportunity."""

        # Base case financial modeling
        base_case = self._calculate_base_case_returns(opportunity, timeline)

        # Sensitivity analysis for key variables
        sensitivity_analysis = self._perform_sensitivity_analysis(opportunity, timeline)

        # Monte Carlo simulation for risk assessment
        risk_assessment = self._monte_carlo_risk_simulation(opportunity, timeline)

        # Portfolio impact analysis
        portfolio_impact = self._assess_portfolio_impact(opportunity)

        return {
            "opportunity_id": opportunity["id"],
            "investment_required": opportunity["cost"],
            "base_case_returns": base_case,
            "sensitivity_analysis": sensitivity_analysis,
            "risk_assessment": risk_assessment,
            "portfolio_impact": portfolio_impact,
            "strategic_value": self._assess_strategic_value(opportunity)
        }