AI-Enhanced Security Audit Tool with Vulnerability Assessment and Patch Priority Ranking Go

Okay, let's break down the development of an "AI Enhanced Security Audit Tool with Vulnerability Assessment and Patch Priority Ranking" in Go, focusing on project details, operation logic, and real-world implementation considerations.  I'll provide a code structure and illustrate core concepts, acknowledging that a complete, production-ready tool would be a much larger undertaking.

**Project Title:**  AegisAI: Intelligent Security Audit & Remediation Prioritization

**1. Project Goals**

*   **Automated Security Auditing:** Scan systems (servers, applications, networks) for security vulnerabilities.
*   **Vulnerability Assessment:** Identify, categorize, and assess the risk level of discovered vulnerabilities.
*   **AI-Powered Analysis:** Leverage machine learning to improve vulnerability detection accuracy and reduce false positives.
*   **Patch Priority Ranking:** Rank vulnerabilities based on severity, exploitability, and potential impact, providing prioritized remediation recommendations.
*   **Comprehensive Reporting:** Generate detailed reports on security posture, vulnerabilities, and recommended actions.
*   **Integration Capabilities:**  Allow for integration with existing security tools and infrastructure.

**2. Core Components**

*   **Scanner Module:**
    *   Discovers hosts, services, and applications on the target network.
    *   Performs port scanning, service fingerprinting, and banner grabbing.
    *   Identifies software versions.
*   **Vulnerability Database:**
    *   Maintains a database of known vulnerabilities (e.g., CVEs) and associated information.
    *   Regularly updated with the latest vulnerability data.
*   **Vulnerability Assessment Engine:**
    *   Matches identified software versions and configurations against the vulnerability database.
    *   Uses signature-based detection to identify specific vulnerabilities.
    *   Employs dynamic analysis techniques (e.g., fuzzing) to uncover new vulnerabilities.
*   **AI/ML Engine (Vulnerability Prediction & Prioritization):**
    *   **Feature Extraction:** Extracts features from vulnerability data (CVE score, CVSS vector, exploit availability, attack complexity, etc.).  Also extracts features from the target environment (e.g., the criticality of the affected system, presence of compensating controls).
    *   **Machine Learning Model:**  Trains a model (e.g., Random Forest, Gradient Boosting, Neural Network) to predict the likelihood of exploitation and the potential impact of a vulnerability.  This helps refine the CVSS score and provide a more accurate risk assessment.  Retrains regularly as new exploits are discovered.
    *   **Priority Ranking:**  Ranks vulnerabilities based on the AI-powered risk assessment, taking into account factors like exploitability, impact, and business criticality.
*   **Reporting Module:**
    *   Generates reports in various formats (e.g., HTML, PDF, JSON).
    *   Provides detailed information on each vulnerability, including description, severity, affected systems, and remediation recommendations.
*   **User Interface (CLI and/or Web UI):**
    *   Allows users to configure scans, view results, and manage the system.
    *   Provides a dashboard with key security metrics.

**3. Technology Stack**

*   **Programming Language:** Go (for performance, concurrency, and cross-platform compatibility)
*   **Database:** PostgreSQL (for storing vulnerability data, scan results, and configuration information)
*   **ML Libraries:**  TensorFlow (Go bindings), Gorgonia (Go native), or integration with Python ML libraries (e.g., scikit-learn) via gRPC.
*   **Web Framework (Optional):** Gin or Echo (for a web UI)
*   **Networking Libraries:**  net/http, gopacket (for packet capture and analysis)

**4. Logical Operation Flow**

1.  **Configuration:**  User configures scan targets (IP addresses, domains, network ranges), scan profiles (types of tests to perform), and credentials.
2.  **Scanning:**
    *   The Scanner Module identifies live hosts and open ports.
    *   Service fingerprinting identifies running services and their versions.
3.  **Vulnerability Assessment:**
    *   The Vulnerability Assessment Engine compares identified software versions against the Vulnerability Database.
    *   Signature-based detection identifies known vulnerabilities.
    *   Dynamic analysis (optional) attempts to exploit potential vulnerabilities.
4.  **AI/ML Analysis:**
    *   Extracted features from the vulnerability and target environment are fed into the trained ML model.
    *   The model predicts the likelihood of exploitation and potential impact.
5.  **Priority Ranking:**
    *   Vulnerabilities are ranked based on the AI-powered risk score, combined with business criticality (e.g., a critical server is given higher weight).
6.  **Reporting:**
    *   A detailed report is generated, listing vulnerabilities, their severity, affected systems, remediation recommendations, and the AI-based priority ranking.

**5. Go Code Structure (Illustrative - Conceptual)**

```go
package main

import (
    "fmt"
    "log"
	"time"

    // Import necessary libraries
    // "github.com/lib/pq"  // PostgreSQL driver
	"os/exec"
)

// Vulnerability represents a security flaw
type Vulnerability struct {
    ID          string
    Name        string
    Description string
    Severity    string
    CVSSScore   float64
    ExploitabilityScore float64
    ImpactScore float64
    Priority    string // AI-assigned priority
    AffectedSystems []string
    Remediation string
}

// Scanner performs network scans
type Scanner struct {
    Target string
}

func (s *Scanner) Scan() ([]string, error) {
    // Placeholder for network scanning logic
    fmt.Println("Performing network scan on", s.Target)

	// Example of running a command:
	cmd := exec.Command("nmap", s.Target) // Or your preferred scanning tool
	out, err := cmd.Output()
	if err != nil {
		log.Printf("Error running nmap: %v", err)
		return nil, err
	}

	// Process the output of the scanning tool
	scanResults := string(out)
	fmt.Println(scanResults)
	// ... Parse and process the output from Nmap or other scanning tool
    time.Sleep(2 * time.Second) // Simulate scanning delay
    return []string{"192.168.1.10", "192.168.1.11"}, nil // Simulated live hosts
}

// VulnerabilityDatabase stores known vulnerabilities
type VulnerabilityDatabase struct {
    Vulnerabilities []Vulnerability
}

// LoadVulnerabilities loads vulnerabilities from a source (e.g., file, API)
func (db *VulnerabilityDatabase) LoadVulnerabilities(source string) error {
    // Placeholder for loading vulnerability data
	fmt.Println("Loading vulnerabilities from", source)
	// This would involve reading from a file, API, or database.
	// For example, reading CVE data from a JSON file.
    db.Vulnerabilities = []Vulnerability{
        {
            ID:          "CVE-2023-1234",
            Name:        "Example Vulnerability",
            Description: "A sample vulnerability",
            Severity:    "High",
            CVSSScore:   7.5,
			ExploitabilityScore: 8.0,
			ImpactScore: 6.0,
            Priority:    "Medium",
            AffectedSystems: []string{"All"},
            Remediation: "Apply Patch XYZ",
        },
    }
	time.Sleep(2 * time.Second)
    return nil
}

// AssessmentEngine assesses vulnerabilities
type AssessmentEngine struct {
    DB *VulnerabilityDatabase
}

// AssessSystem checks a system for vulnerabilities
func (ae *AssessmentEngine) AssessSystem(system string) ([]Vulnerability, error) {
    // Placeholder for vulnerability assessment logic
    fmt.Println("Assessing system", system)
    vulnerabilitiesFound := []Vulnerability{}
    for _, vuln := range ae.DB.Vulnerabilities {
        if contains(vuln.AffectedSystems, "All") || contains(vuln.AffectedSystems, system) {
            vulnerabilitiesFound = append(vulnerabilitiesFound, vuln)
        }
    }
	time.Sleep(2 * time.Second)
    return vulnerabilitiesFound, nil
}

// contains helper function
func contains(arr []string, str string) bool {
    for _, a := range arr {
        if a == str {
            return true
        }
    }
    return false
}

// AIMLEngine performs AI/ML-based analysis
type AIMLEngine struct {
    // ML model and related configurations
}

// PredictRisk predicts the risk of a vulnerability
func (ai *AIMLEngine) PredictRisk(vuln Vulnerability) float64 {
    // Placeholder for AI/ML-based risk prediction
    fmt.Println("Predicting risk for vulnerability", vuln.ID)

	//Here you would load your trained model and use the
	//vulnerability's features to predict a risk score.
	//This is a simplified example.
    time.Sleep(1 * time.Second)
	return vuln.CVSSScore * (vuln.ExploitabilityScore/10) * (vuln.ImpactScore/10)
}

// RankVulnerabilities ranks vulnerabilities based on risk
func (ai *AIMLEngine) RankVulnerabilities(vulnerabilities []Vulnerability) []Vulnerability {
    // Placeholder for vulnerability ranking logic
    fmt.Println("Ranking vulnerabilities based on risk")
    // In a real implementation, you would use the predicted risk scores to sort the vulnerabilities.

	for i := range vulnerabilities {
		vulnerabilities[i].Priority = "High" // Default priority
		if ai.PredictRisk(vulnerabilities[i]) < 5 {
			vulnerabilities[i].Priority = "Medium"
		}
		if ai.PredictRisk(vulnerabilities[i]) < 2 {
			vulnerabilities[i].Priority = "Low"
		}
	}

    return vulnerabilities
}

// ReportingModule generates reports
type ReportingModule struct {
}

// GenerateReport generates a security report
func (r *ReportingModule) GenerateReport(vulnerabilities []Vulnerability) {
    // Placeholder for report generation logic
    fmt.Println("\nGenerating Security Report:")
    for _, vuln := range vulnerabilities {
        fmt.Printf("Vulnerability: %s\n", vuln.Name)
        fmt.Printf("  Severity: %s\n", vuln.Severity)
        fmt.Printf("  Priority: %s\n", vuln.Priority)
        fmt.Printf("  Description: %s\n", vuln.Description)
        fmt.Printf("  Remediation: %s\n", vuln.Remediation)
        fmt.Println("---")
    }
}

func main() {
    // 1. Configure the scanner
    scanner := Scanner{Target: "192.168.1.0/24"} // Example: Scan a network range

    // 2. Perform the scan
    liveHosts, err := scanner.Scan()
    if err != nil {
        log.Fatalf("Scan failed: %v", err)
    }

    fmt.Println("Live hosts:", liveHosts)

    // 3. Load the vulnerability database
    db := VulnerabilityDatabase{}
    err = db.LoadVulnerabilities("cve_data.json") // Simulated data source
    if err != nil {
        log.Fatalf("Failed to load vulnerabilities: %v", err)
    }

    // 4. Create an assessment engine
    assessmentEngine := AssessmentEngine{DB: &db}

    allVulnerabilities := []Vulnerability{}
	// 5. Assess each system
    for _, host := range liveHosts {
        vulnerabilities, err := assessmentEngine.AssessSystem(host)
        if err != nil {
            log.Printf("Assessment failed for %s: %v", host, err)
            continue
        }
		allVulnerabilities = append(allVulnerabilities, vulnerabilities...)
        fmt.Printf("Vulnerabilities found on %s: %d\n", host, len(vulnerabilities))
    }

    // 6. Perform AI/ML-based analysis
    aiEngine := AIMLEngine{}
    rankedVulnerabilities := aiEngine.RankVulnerabilities(allVulnerabilities)

    // 7. Generate a report
    reportingModule := ReportingModule{}
    reportingModule.GenerateReport(rankedVulnerabilities)
}
```

**Explanation of the Code:**

*   **Data Structures:** Defines structures for `Vulnerability`, `Scanner`, `VulnerabilityDatabase`, `AssessmentEngine`, `AIMLEngine`, and `ReportingModule`.  These represent the core components of the system.
*   **Scanner:**  The `Scanner` struct and its `Scan` method are placeholders for actual network scanning logic.  In a real implementation, you would use libraries like `nmap` command line execution or `gopacket` to perform port scanning, service fingerprinting, and OS detection. The example uses `nmap` execution to scan, you need to install `nmap` to make it works.
*   **Vulnerability Database:** The `VulnerabilityDatabase` struct and its `LoadVulnerabilities` method are placeholders for loading vulnerability data from a source like a CVE database or a file.  You would typically use a database like PostgreSQL to store and manage this data.
*   **Assessment Engine:** The `AssessmentEngine` compares the identified software versions against the vulnerability database to find potential vulnerabilities.  This is a simplified example and would need to be expanded to include more sophisticated detection techniques.
*   **AI/ML Engine:** The `AIMLEngine` represents the AI/ML component of the tool.  The `PredictRisk` method is a placeholder for a machine learning model that predicts the risk of a vulnerability based on its features and the characteristics of the target environment.
*   **Reporting Module:** The `ReportingModule` generates a report listing the identified vulnerabilities, their severity, and remediation recommendations.
*   **Main Function:** The `main` function orchestrates the entire process, from scanning to reporting.

**6. Real-World Implementation Considerations**

*   **Scalability:** Design the system to handle large networks and a high volume of scan requests.  Consider using distributed scanning and asynchronous processing.
*   **Accuracy:**  The accuracy of vulnerability detection is critical.  Regularly update the vulnerability database and use multiple detection techniques.  Implement a feedback loop to correct false positives and improve the AI/ML model.
*   **Performance:** Optimize the scanning and assessment processes to minimize the impact on target systems.  Use efficient data structures and algorithms.
*   **Integration:**  Provide APIs and integration points to allow the tool to work with other security tools and systems (e.g., SIEM, ticketing systems).
*   **Security:**  Secure the tool itself against attacks.  Implement proper authentication, authorization, and input validation.
*   **Compliance:** Ensure the tool complies with relevant security standards and regulations (e.g., PCI DSS, HIPAA).
*   **Training Data for AI/ML:**  Gather a large, high-quality dataset of vulnerability information, exploit data, and system characteristics to train the ML model.  This is a crucial step for improving the accuracy and effectiveness of the tool.
*   **Continuous Learning:**  The AI/ML model should continuously learn and adapt to new threats and vulnerabilities.  Implement a retraining pipeline to update the model with the latest data.
*   **Ethical Considerations:**  Use AI responsibly and ethically.  Avoid bias in the data and algorithms, and ensure transparency in the decision-making process.

**7.  Example ML Model Considerations (Beyond the basic example)**

*   **Model Type:** Random Forest or Gradient Boosting (XGBoost, LightGBM) are good starting points for tabular data.  Neural networks could be considered for more complex feature relationships.
*   **Input Features:**
    *   CVSS Score (Base, Temporal, Environmental)
    *   CWE (Common Weakness Enumeration)
    *   Exploit Availability (from exploit databases like Exploit-DB)
    *   Attack Complexity
    *   Authentication Required
    *   Impact Metrics (Confidentiality, Integrity, Availability)
    *   Target Environment Features (e.g., Server Type, OS, Business Criticality)
    *   Vulnerability Age (time since discovery)
*   **Output:**  A risk score (e.g., a probability of exploitation) or a priority level (High, Medium, Low).
*   **Training Data:**  CVE data from NVD, exploit data from Exploit-DB, and vulnerability reports from various sources.  Also, data about your specific infrastructure and applications.

**8. Deployment and Infrastructure**

*   **Deployment Options:**
    *   On-premise (install on your own servers)
    *   Cloud-based (deploy to a cloud provider like AWS, Azure, or GCP)
    *   SaaS (Software as a Service) - You host and manage the tool for users.
*   **Infrastructure Requirements:**
    *   Servers to run the scanning and assessment engines
    *   Database server to store vulnerability data and scan results
    *   Web server (if providing a web UI)
    *   Load balancer (for scalability)
    *   Firewall and intrusion detection/prevention systems

**In Summary**

Building an AI-enhanced security audit tool is a complex project requiring a deep understanding of security vulnerabilities, network scanning, vulnerability assessment, and machine learning.  The Go code provided offers a starting point, but a production-ready tool would require significantly more development effort, including robust error handling, comprehensive testing, and ongoing maintenance. Remember the AI/ML component is only as good as the data used to train it, so data quality and continuous learning are essential for success.