Incident Detection

5 automated security scanners

Log Management

Purpose: The Log Management Scanner evaluates log coverage, retention policies, and search capabilities to ensure that organizations maintain adequate logging practices for incident detection and response. Inadequate logging can hinder forensic analysis and compliance adherence.

What It Detects:

Identifies gaps in log collection across critical systems and applications.
Verifies that all necessary logs are being captured, including system, application, and security-related logs.
Checks for defined retention periods for different types of logs and ensures compliance with industry standards and regulatory requirements regarding log retention.
Assesses the effectiveness of search functionalities within the logging system.
Detects signs of tampering or unauthorized access to log data and ensures that logs are stored securely and cannot be altered without detection.
Examines company security documentation for policies related to logging and verifies that documented policies align with actual logging practices in place.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: Maintaining adequate logging practices is crucial for effective incident detection and response, which can directly impact the ability to conduct forensic analyses and ensure compliance with regulatory standards.

Risk Levels:

Critical: Inadequate or non-existent log management policies that significantly hinder incident detection and response capabilities.
High: Limited log coverage or retention periods that may lead to critical information loss during forensic analysis.
Medium: Inefficient search functionalities or unclear documentation regarding logging practices, which could compromise the effectiveness of security operations.
Low: Minor issues with log integrity that do not significantly impact overall security posture but are still recommended for improvement.
Info: Informal or non-existent logging policies and procedures, generally considered less critical but still beneficial to address for enhanced security.

Example Findings:

The organization lacks comprehensive log coverage across all critical systems, which could lead to significant gaps in incident detection capabilities.
Log retention periods are not aligned with industry standards, potentially leading to the loss of valuable forensic evidence required for post-incident analysis and compliance audits.

Network Traffic Analysis

Purpose: The Network Traffic Analysis Scanner is designed to identify visibility gaps, protocol support issues, and encrypted traffic vulnerabilities within a specified domain and IP range. It aims to ensure robust network security by analyzing DNS records, HTTP security headers, TLS/SSL certificates, open ports, and API configurations.

What It Detects:

DNS Record Vulnerabilities: Analyzes TXT, MX, NS, CAA, and DMARC records for misconfigurations or missing entries. Examples of findings include incorrect SPF records and missing DMARC policies.
HTTP Security Headers: Checks for the presence and correctness of security headers such as Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, and X-Content-Type-Options. Findings might include missing required headers.
TLS/SSL Inspection: Inspects SSL/TLS certificates for outdated protocols (TLSv1.0, TLSv1.1) and weak cipher suites (RC4, DES, MD5). Examples of issues detected are enabled TLSv1.0 and TLSv1.1, as well as the use of weak cipher suites.
Port Scanning: Identifies open ports and services running on the specified IP range to detect potential vulnerabilities. Open ports might indicate unsecure configurations.
API Vulnerabilities: Analyzes APIs for security misconfigurations such as missing authentication or insecure data handling. Examples include unauthenticated access endpoints.

Inputs Required:

domain (string): The primary domain to analyze, e.g., acme.com.
ip_range (string): The IP range to scan for open ports and services, e.g., 192.168.1.0/24.

Business Impact: This scanner is crucial for maintaining the security of network traffic by identifying potential vulnerabilities in DNS records, HTTP headers, TLS configurations, port settings, and API interfaces that could be exploited by attackers. Early detection of these issues helps mitigate risks associated with data breaches, unauthorized access, and other cyber threats.

Risk Levels:

Critical: Findings include missing or incorrect DMARC policies, enabled TLSv1.0/TLSv1.1, and weak cipher suites. These are critical because they directly affect the security of encrypted communications and can lead to significant data exposure.
High: Issues such as missing HTTP security headers (e.g., Strict-Transport-Security) or unauthenticated API endpoints pose a high risk if exploited by malicious actors, potentially leading to unauthorized access and data theft.
Medium: Medium severity findings might include outdated protocols in TLS configurations or open ports that are not typically required for secure communications but could indicate misconfigurations or potential attack vectors.
Low: Informational findings such as the presence of certain cipher suites are less critical but still important to be aware of, especially if they represent weak cryptographic practices.
Info: These include general DNS record analyses and basic HTTP header checks that provide baseline network security posture information without immediate risk.

Example Findings:

“Missing or incorrect DMARC policy: v=DMARC1; p=none” - This indicates a significant vulnerability in the domain’s email authentication mechanisms, potentially allowing for phishing attacks.
“TLSv1.0 is enabled” - Enabling outdated TLS versions exposes the network to well-known vulnerabilities and should be promptly addressed to comply with modern security standards.
“Unauthenticated access detected at https://acme.com/api/v1/data” - This reveals a significant API misconfiguration, allowing unauthenticated users to interact with potentially sensitive data, leading to unauthorized data exposure and manipulation.

SIEM Effectiveness

Purpose: The SIEM Effectiveness Scanner evaluates the effectiveness of a company’s Security Information and Event Management (SIEM) system by assessing detection coverage, rule quality, and alert fatigue. It helps identify gaps in security monitoring, suboptimal rule configurations, and excessive alerts that can desensitize incident response teams.

What It Detects:

Detection Coverage: Identifies missing or incomplete log sources integrated into the SIEM, checks for coverage of critical systems and applications, and verifies whether all relevant data is being collected and monitored.
Rule Quality: Evaluates the specificity and relevance of existing detection rules, flags overly broad or generic rules that may generate false positives, and assesses the use of advanced analytics and machine learning in rule creation.
Alert Fatigue: Analyzes the frequency and volume of alerts generated by the SIEM, identifies patterns of repetitive or irrelevant alerts that can overwhelm security teams, and suggests optimizations to reduce noise and improve alert relevance.
Policy Compliance: Reviews company policies related to SIEM implementation and management, ensures adherence to industry standards and best practices (e.g., SOC 2, ISO 27001), and verifies that compliance certifications are up-to-date and relevant.
Documentation and Trust Center Information: Examines publicly available security documentation for clarity and completeness, checks trust center pages for transparency regarding incident response and monitoring capabilities, and validates the accuracy of information provided in public policy statements.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial as it helps organizations assess the effectiveness of their SIEM systems, which are essential for detecting and responding to security incidents efficiently. Inefficient or inadequate SIEM configurations can lead to missed detections, false positives causing alert fatigue, and ultimately a less robust security posture that may leave an organization vulnerable to attacks.

Risk Levels:

Critical: Conditions where the absence of critical log sources, poorly configured rules, or significant compliance gaps could directly impact the ability to detect and respond to high-priority threats.
High: Situations where suboptimal rule quality or excessive alert volumes could lead to missed detections or unnecessary resource consumption by security teams.
Medium: Issues that might not significantly impair detection capabilities but can contribute to operational inefficiencies or increased alert noise, potentially affecting team morale and response efficiency.
Low: Informalities such as minor compliance gaps in documentation or incomplete log source integration that do not materially impact the SIEM’s effectiveness.
Info: General information about the scanner’s capabilities, findings, and how to interpret results for continuous improvement without immediate security implications.

Example Findings: The scanner might flag a critical system missing from the SIEM configuration as a log source or identify rules that are overly broad and likely to produce false positives. Additionally, it could detect outdated compliance certifications or lack of transparency in public trust center pages regarding incident response capabilities.

User Behavior Analytics

Purpose: The User Behavior Analytics Scanner is designed to analyze user behavior in order to detect deviations from baseline activity, identify false positives in security alerts, and assess the coverage of existing monitoring solutions. This tool helps in understanding normal operational patterns and pinpointing anomalies that may indicate potential security threats.

What It Detects:

Baseline Quality Analysis: Identifies typical user activities and establishes a baseline for normal behavior, detecting consistent patterns in user interactions with systems and applications.
False Positive Rate Evaluation: Analyzes alerts generated by security tools to determine the ratio of false positives and flags alerts that do not align with known attack vectors or unusual user behavior.
Coverage Assessment: Evaluates the extent to which current monitoring solutions capture user activities, identifying gaps in coverage where critical actions may be overlooked.
Anomaly Detection: Detects deviations from established baseline behavior that could indicate malicious activity and highlights unusual patterns that require further investigation.
User Activity Monitoring: Monitors and logs user interactions to provide a comprehensive view of system usage, using historical data to refine detection algorithms and improve accuracy.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: Understanding normal operational patterns and pinpointing anomalies that may indicate potential security threats is crucial for enhancing the overall security posture of a company, helping to prevent potential breaches and ensuring compliance with security policies and standards.

Risk Levels:

Critical: Conditions that pose an immediate threat to system integrity or confidentiality, requiring urgent attention.
High: Conditions that could lead to significant disruptions or data loss if not addressed promptly.
Medium: Conditions that may require intervention but do not immediately impact critical functions.
Low: Informal observations that might suggest areas for improvement in security practices.
Info: Non-critical findings providing supplementary information useful for continuous monitoring and improvement.

Example Findings:

A user consistently accessing sensitive data without legitimate business justification could be flagged as a potential insider threat, requiring further investigation to confirm the legitimacy of their actions.
An alert indicating unusual login attempts from different geographical locations might be initially considered a false positive until verified through behavioral analysis that confirms it is part of an ongoing phishing campaign targeting specific employees.

Endpoint Detection

Purpose: The VIGILGUARD Endpoint Detection Scanner is designed to evaluate and enhance the endpoint security posture of organizations by comprehensively assessing agent coverage, detection capabilities, performance metrics, policy compliance, and incident response readiness. This tool helps identify vulnerabilities that could be exploited by attackers, ensuring a robust defense mechanism for digital assets.

What It Detects:

Agent Coverage Analysis: The scanner identifies endpoints without installed security agents to ensure comprehensive protection is in place. It also verifies agent version compatibility with current policies and checks for outdated or unpatched agents to prevent potential vulnerabilities.
Detection Capabilities Evaluation: This includes assessing the ability of endpoint detection systems to identify and respond effectively to known threats, evaluating both signature-based and behavior-based detection methods, as well as testing the system’s capability to detect novel (zero-day) exploits that traditional defenses might miss.
Performance Metrics Review: The scanner measures response times for threat detection and remediation actions, analyzes false positive rates, which can impact operational efficiency, and monitors resource utilization by security agents across endpoints to ensure optimal performance without excessive overhead.
Policy Compliance Verification: Ensuring up-to-date enforcement of endpoint security policies aligned with industry standards (e.g., SOC 2, ISO 27001) and adherence to best practices in endpoint protection is crucial for maintaining a secure environment.
Incident Response Readiness Assessment: This involves evaluating the organization’s preparedness to respond swiftly to detected incidents, assessing the availability of an effective incident response plan, and verifying communication protocols between security teams and other stakeholders to ensure swift action against potential threats.

Inputs Required:

domain (string): The primary domain to analyze, which serves as the base for evaluating endpoint security measures across the organization’s network.
company_name (string): The company name is used during the analysis to search for relevant documentation and policies that pertain to the organization’s specific security stance.

Business Impact: This scanner plays a pivotal role in enhancing an organization’s cybersecurity posture by proactively identifying gaps in endpoint security measures, which can significantly mitigate risks associated with potential cyber threats and data breaches. The insights gained from this assessment are critical for informed decision-making regarding updates to security policies, agent upgrades, or changes in incident response strategies.

Risk Levels:

Critical: Failures in agent coverage analysis that result in significant exposure to known vulnerabilities without protection could lead to immediate threats if exploited by malicious actors.
High: Inadequate detection capabilities for both known and zero-day threats can significantly increase the risk of data breaches or system compromises, impacting business operations and reputation.
Medium: Performance issues such as slow response times or high false positive rates may require additional resources to manage security alerts effectively but do not pose immediate critical risks.
Low: Minor compliance gaps with industry standards might affect regulatory posture but generally have minimal impact on the overall risk profile if other controls are robust.
Info: Informational findings such as minor policy updates or outdated agent versions that do not directly contribute to significant security risks but can be part of an ongoing improvement process for endpoint protection and compliance.

Example Findings:

An organization has several endpoints without installed security agents, indicating a potential gap in basic endpoint protection across the network.
The detection system frequently generates false positives that require manual verification before action is taken, which could lead to delayed response times during actual threats.