Risk Assessment (also “Risk Analysis” or “Security Risk Assessment”) is the systematic process of identifying, analyzing, and evaluating security risks to determine their probability, impact, and priority, facilitating informed decisions on risk treatment. This process is fundamental in information security risk management and typically includes identifying assets, threats, and vulnerabilities, evaluating the probability and impact of risks, and prioritizing risks based on criteria such as asset criticality, threat exposure, and effectiveness of existing controls, being essential for efficient allocation of security resources and continuous improvement of organizational security posture.

What is Risk Assessment?

Risk assessment is a fundamental process in security management that allows organizations to understand their vulnerabilities, threats, and potential impact, establishing the basis for strategic security investment decisions.

Process Components

Risk Identification

  • Assets: Identification of information assets
  • Threats: Identification of potential threats
  • Vulnerabilities: Identification of vulnerabilities
  • Controls: Evaluation of existing controls

Risk Analysis

  • Probability: Assessment of occurrence probability
  • Impact: Assessment of potential impact
  • Scenarios: Development of risk scenarios
  • Quantification: Risk quantification

Risk Evaluation

  • Prioritization: Risk prioritization
  • Comparison: Comparison with risk criteria
  • Decision: Decision on treatment
  • Documentation: Documentation of results

Assessment Methodologies

Quantitative Methodology

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import numpy as np
import pandas as pd
from scipy import stats

class QuantitativeRiskAssessment:
    def __init__(self):
        self.risk_factors = {
            'probability': {
                'Very_Low': 0.1,
                'Low': 0.3,
                'Medium': 0.5,
                'High': 0.7,
                'Very_High': 0.9
            },
            'impact': {
                'Very_Low': 1000,
                'Low': 10000,
                'Medium': 100000,
                'High': 1000000,
                'Very_High': 10000000
            }
        }
    
    def calculate_risk_value(self, probability, impact):
        """Calculate risk value"""
        return probability * impact
    
    def calculate_annual_loss_expectancy(self, single_loss_expectancy, annual_rate_of_occurrence):
        """Calculate ALE (Annual Loss Expectancy)"""
        return single_loss_expectancy * annual_rate_of_occurrence
    
    def monte_carlo_simulation(self, risk_scenarios, iterations=10000):
        """Monte Carlo simulation for risk analysis"""
        results = []
        
        for _ in range(iterations):
            total_risk = 0
            for scenario in risk_scenarios:
                # Generate random values based on distributions
                prob = np.random.beta(scenario['prob_alpha'], scenario['prob_beta'])
                impact = np.random.lognormal(scenario['impact_mean'], scenario['impact_std'])
                total_risk += prob * impact
            results.append(total_risk)
        
        return {
            'mean': np.mean(results),
            'std': np.std(results),
            'percentile_95': np.percentile(results, 95),
            'percentile_99': np.percentile(results, 99),
            'distribution': results
        }
    
    def risk_matrix(self, risks_data):
        """Create risk matrix"""
        matrix = np.zeros((5, 5))  # 5x5 matrix for probability vs impact
        
        for risk in risks_data:
            prob_idx = list(self.risk_factors['probability'].keys()).index(risk['probability'])
            impact_idx = list(self.risk_factors['impact'].keys()).index(risk['impact'])
            matrix[prob_idx][impact_idx] += 1
        
        return matrix

# Usage example
quantitative_assessment = QuantitativeRiskAssessment()

# Calculate risk value
risk_value = quantitative_assessment.calculate_risk_value(0.7, 1000000)
print(f"Risk value: ${risk_value:,.2f}")

# Calculate ALE
ale = quantitative_assessment.calculate_annual_loss_expectancy(100000, 0.3)
print(f"ALE: ${ale:,.2f}")

# Monte Carlo simulation
scenarios = [
    {'prob_alpha': 2, 'prob_beta': 8, 'impact_mean': 10, 'impact_std': 1},
    {'prob_alpha': 5, 'prob_beta': 5, 'impact_mean': 12, 'impact_std': 1.5}
]
simulation_results = quantitative_assessment.monte_carlo_simulation(scenarios)
print(f"Monte Carlo simulation - Mean: ${simulation_results['mean']:,.2f}")

Qualitative Methodology

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class QualitativeRiskAssessment:
    def __init__(self):
        self.risk_levels = {
            'Very_Low': 1,
            'Low': 2,
            'Medium': 3,
            'High': 4,
            'Very_High': 5
        }
        
        self.risk_categories = {
            'Confidentiality': 'Loss of data confidentiality',
            'Integrity': 'Corruption or unauthorized modification',
            'Availability': 'Service or data interruption',
            'Compliance': 'Regulatory non-compliance',
            'Reputation': 'Organizational reputation damage'
        }
    
    def assess_risk_level(self, probability, impact):
        """Assess risk level"""
        prob_score = self.risk_levels.get(probability, 0)
        impact_score = self.risk_levels.get(impact, 0)
        
        risk_score = prob_score * impact_score
        
        if risk_score <= 2:
            return 'Very_Low'
        elif risk_score <= 4:
            return 'Low'
        elif risk_score <= 9:
            return 'Medium'
        elif risk_score <= 16:
            return 'High'
        else:
            return 'Very_High'
    
    def risk_heatmap(self, risks_data):
        """Create risk heatmap"""
        heatmap_data = {}
        
        for risk in risks_data:
            category = risk['category']
            level = self.assess_risk_level(risk['probability'], risk['impact'])
            
            if category not in heatmap_data:
                heatmap_data[category] = {}
            
            if level not in heatmap_data[category]:
                heatmap_data[category][level] = 0
            
            heatmap_data[category][level] += 1
        
        return heatmap_data
    
    def prioritize_risks(self, risks_data):
        """Prioritize risks"""
        prioritized_risks = []
        
        for risk in risks_data:
            risk_level = self.assess_risk_level(risk['probability'], risk['impact'])
            risk_score = self.risk_levels[risk_level]
            
            prioritized_risks.append({
                **risk,
                'risk_level': risk_level,
                'risk_score': risk_score
            })
        
        # Sort by risk score (descending)
        prioritized_risks.sort(key=lambda x: x['risk_score'], reverse=True)
        
        return prioritized_risks

# Usage example
qualitative_assessment = QualitativeRiskAssessment()

# Assess risk level
risk_level = qualitative_assessment.assess_risk_level('High', 'High')
print(f"Risk level: {risk_level}")

# Example data
risks_data = [
    {'category': 'Confidentiality', 'probability': 'High', 'impact': 'High', 'description': 'Data breach'},
    {'category': 'Availability', 'probability': 'Medium', 'impact': 'High', 'description': 'DDoS attack'},
    {'category': 'Integrity', 'probability': 'Low', 'impact': 'Medium', 'description': 'Data corruption'}
]

prioritized = qualitative_assessment.prioritize_risks(risks_data)
print(f"Prioritized risks: {[(r['description'], r['risk_level']) for r in prioritized]}")

Assessment Tools

Risk Management System

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class RiskManagementSystem:
    def __init__(self):
        self.risks = {}
        self.assets = {}
        self.threats = {}
        self.vulnerabilities = {}
        self.controls = {}
        self.assessments = {}
    
    def add_asset(self, asset_id, asset_data):
        """Add asset"""
        self.assets[asset_id] = {
            **asset_data,
            'created_date': '2025-10-26',
            'last_updated': '2025-10-26',
            'status': 'Active'
        }
    
    def add_threat(self, threat_id, threat_data):
        """Add threat"""
        self.threats[threat_id] = {
            **threat_data,
            'created_date': '2025-10-26',
            'last_updated': '2025-10-26',
            'status': 'Active'
        }
    
    def add_vulnerability(self, vuln_id, vuln_data):
        """Add vulnerability"""
        self.vulnerabilities[vuln_id] = {
            **vuln_data,
            'created_date': '2025-10-26',
            'last_updated': '2025-10-26',
            'status': 'Open'
        }
    
    def create_risk_assessment(self, assessment_id, assets, threats, vulnerabilities):
        """Create risk assessment"""
        assessment = {
            'assessment_id': assessment_id,
            'assets': assets,
            'threats': threats,
            'vulnerabilities': vulnerabilities,
            'risks': [],
            'created_date': '2025-10-26',
            'status': 'Draft'
        }
        
        # Generate risks based on assets, threats and vulnerabilities
        for asset_id in assets:
            for threat_id in threats:
                for vuln_id in vulnerabilities:
                    risk_id = f"{asset_id}_{threat_id}_{vuln_id}"
                    risk = self.calculate_risk(asset_id, threat_id, vuln_id)
                    assessment['risks'].append(risk)
        
        self.assessments[assessment_id] = assessment
        return assessment
    
    def calculate_risk(self, asset_id, threat_id, vuln_id):
        """Calculate specific risk"""
        asset = self.assets.get(asset_id, {})
        threat = self.threats.get(threat_id, {})
        vulnerability = self.vulnerabilities.get(vuln_id, {})
        
        # Simplified risk calculation
        probability = threat.get('probability', 0.5)
        impact = asset.get('value', 100000) * vulnerability.get('severity', 0.5)
        risk_value = probability * impact
        
        return {
            'risk_id': f"{asset_id}_{threat_id}_{vuln_id}",
            'asset_id': asset_id,
            'threat_id': threat_id,
            'vulnerability_id': vuln_id,
            'probability': probability,
            'impact': impact,
            'risk_value': risk_value,
            'status': 'Open'
        }
    
    def get_risk_summary(self, assessment_id):
        """Get risk summary"""
        if assessment_id not in self.assessments:
            return None
        
        assessment = self.assessments[assessment_id]
        risks = assessment['risks']
        
        if not risks:
            return {'total_risks': 0}
        
        total_risks = len(risks)
        high_risks = len([r for r in risks if r['risk_value'] > 500000])
        medium_risks = len([r for r in risks if 100000 < r['risk_value'] <= 500000])
        low_risks = len([r for r in risks if r['risk_value'] <= 100000])
        
        total_value = sum(r['risk_value'] for r in risks)
        avg_risk = total_value / total_risks if total_risks > 0 else 0
        
        return {
            'total_risks': total_risks,
            'high_risks': high_risks,
            'medium_risks': medium_risks,
            'low_risks': low_risks,
            'total_value': total_value,
            'average_risk': avg_risk
        }

# Usage example
risk_system = RiskManagementSystem()

# Add assets
risk_system.add_asset('ASSET-001', {
    'name': 'Database Server',
    'type': 'Infrastructure',
    'value': 1000000,
    'owner': 'IT Department'
})

# Add threats
risk_system.add_threat('THREAT-001', {
    'name': 'SQL Injection',
    'type': 'Cyber Attack',
    'probability': 0.3,
    'source': 'External'
})

# Add vulnerabilities
risk_system.add_vulnerability('VULN-001', {
    'name': 'Unpatched Database',
    'type': 'Software',
    'severity': 0.8,
    'cvss_score': 7.5
})

# Create assessment
assessment = risk_system.create_risk_assessment(
    'ASSESS-001',
    ['ASSET-001'],
    ['THREAT-001'],
    ['VULN-001']
)

summary = risk_system.get_risk_summary('ASSESS-001')
print(f"Assessment summary: {summary}")

Scenario Analysis

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class ScenarioAnalysis:
    def __init__(self):
        self.scenarios = {}
        self.scenario_templates = {
            'Data_Breach': {
                'description': 'Confidential data breach',
                'probability_factors': ['Security_controls', 'Employee_training', 'System_patches'],
                'impact_factors': ['Data_volume', 'Data_sensitivity', 'Regulatory_requirements']
            },
            'Ransomware': {
                'description': 'Ransomware attack',
                'probability_factors': ['Email_security', 'Backup_systems', 'User_awareness'],
                'impact_factors': ['System_downtime', 'Data_recovery', 'Business_continuity']
            },
            'Insider_Threat': {
                'description': 'Insider threat',
                'probability_factors': ['Access_controls', 'Monitoring_systems', 'Employee_satisfaction'],
                'impact_factors': ['Data_access', 'System_damage', 'Reputation']
            }
        }
    
    def create_scenario(self, scenario_id, scenario_type, custom_factors=None):
        """Create risk scenario"""
        template = self.scenario_templates.get(scenario_type, {})
        
        scenario = {
            'scenario_id': scenario_id,
            'type': scenario_type,
            'description': template.get('description', ''),
            'probability_factors': template.get('probability_factors', []),
            'impact_factors': template.get('impact_factors', []),
            'probability_score': 0,
            'impact_score': 0,
            'risk_score': 0,
            'created_date': '2025-10-26'
        }
        
        if custom_factors:
            scenario.update(custom_factors)
        
        self.scenarios[scenario_id] = scenario
        return scenario
    
    def assess_scenario(self, scenario_id, factor_scores):
        """Assess scenario with factor scores"""
        if scenario_id not in self.scenarios:
            return None
        
        scenario = self.scenarios[scenario_id]
        
        # Calculate probability based on factors
        prob_factors = scenario['probability_factors']
        prob_scores = [factor_scores.get(factor, 0) for factor in prob_factors]
        scenario['probability_score'] = sum(prob_scores) / len(prob_scores) if prob_scores else 0
        
        # Calculate impact based on factors
        impact_factors = scenario['impact_factors']
        impact_scores = [factor_scores.get(factor, 0) for factor in impact_factors]
        scenario['impact_score'] = sum(impact_scores) / len(impact_scores) if impact_scores else 0
        
        # Calculate risk score
        scenario['risk_score'] = scenario['probability_score'] * scenario['impact_score']
        
        return scenario
    
    def compare_scenarios(self, scenario_ids):
        """Compare scenarios"""
        comparison = []
        
        for scenario_id in scenario_ids:
            if scenario_id in self.scenarios:
                scenario = self.scenarios[scenario_id]
                comparison.append({
                    'scenario_id': scenario_id,
                    'type': scenario['type'],
                    'probability': scenario['probability_score'],
                    'impact': scenario['impact_score'],
                    'risk_score': scenario['risk_score']
                })
        
        # Sort by risk score
        comparison.sort(key=lambda x: x['risk_score'], reverse=True)
        
        return comparison

# Usage example
scenario_analysis = ScenarioAnalysis()

# Create scenarios
scenario_analysis.create_scenario('SCEN-001', 'Data_Breach')
scenario_analysis.create_scenario('SCEN-002', 'Ransomware')
scenario_analysis.create_scenario('SCEN-003', 'Insider_Threat')

# Assess scenarios
factor_scores = {
    'Security_controls': 0.7,
    'Employee_training': 0.6,
    'System_patches': 0.8,
    'Data_volume': 0.9,
    'Data_sensitivity': 0.8,
    'Regulatory_requirements': 0.7,
    'Email_security': 0.5,
    'Backup_systems': 0.8,
    'User_awareness': 0.6,
    'System_downtime': 0.7,
    'Data_recovery': 0.6,
    'Business_continuity': 0.8,
    'Access_controls': 0.7,
    'Monitoring_systems': 0.6,
    'Employee_satisfaction': 0.8,
    'Data_access': 0.9,
    'System_damage': 0.5,
    'Reputation': 0.8
}

for scenario_id in ['SCEN-001', 'SCEN-002', 'SCEN-003']:
    scenario_analysis.assess_scenario(scenario_id, factor_scores)

comparison = scenario_analysis.compare_scenarios(['SCEN-001', 'SCEN-002', 'SCEN-003'])
print(f"Scenario comparison: {comparison}")

Best Practices

Assessment Process

  • Consistent Methodology: Use consistent methodology
  • Participation: Include relevant stakeholders
  • Documentation: Document completely
  • Review: Regular review of assessments

Risk Analysis

  • Objectivity: Maintain objectivity in analysis
  • Evidence: Base on solid evidence
  • Scenarios: Consider multiple scenarios
  • Uncertainty: Recognize uncertainty

Communication

  • Clarity: Communicate results clearly
  • Context: Provide adequate context
  • Recommendations: Include clear recommendations
  • Follow-up: Establish follow-up

References