10 Best Practices to Fix Insufficient Logging and Monitoring in TypeScript ERP

In today’s cybersecurity landscape, insufficient logging and monitoring in TypeScript ERP systems pose critical challenges. Enterprises often overlook this vital security aspect, leaving systems vulnerable to potential breaches. This blog will explore why logging and monitoring are essential, demonstrate real-world coding examples, and provide insights into fixing these vulnerabilities in your ERP application.

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What Is Insufficient Logging and Monitoring?

Insufficient logging and monitoring refer to the failure to record and monitor critical application events adequately. This shortfall allows attackers to exploit your system undetected. Key symptoms include:

• Missed log details for unauthorized access attempts.
• Delayed incident detection.
• Poor integration with monitoring tools.

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Impacts of Insufficient Logging in TypeScript ERP

• Data Breaches: Undetected intrusions may compromise sensitive information.
• Compliance Issues: Lack of adequate logs violates regulations like GDPR or HIPAA.
• Increased Resolution Time: Delayed responses lead to extended downtimes.

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Best Practices to Address Insufficient Logging and Monitoring

1. Implement Comprehensive Logging

Use libraries like Winston to capture detailed logs. Here’s an example:

import winston from 'winston';

const logger = winston.createLogger({
  level: 'info',
  format: winston.format.json(),
  transports: [
    new winston.transports.File({ filename: 'error.log', level: 'error' }),
    new winston.transports.File({ filename: 'combined.log' }),
  ],
});

// Logging events
logger.info('User logged in');
logger.error('Unauthorized access attempt detected');

2. Enable Real-Time Monitoring

Integrate monitoring tools like ELK Stack or Prometheus for real-time alerting.

3. Utilize Secure Logging Mechanisms

Avoid exposing sensitive data in logs. Example: Mask user credentials before logging.

function logUserEvent(event: string, user: { id: string; email: string }) {
  const maskedEmail = user.email.replace(/(.{2}).+(@.+)/, '$1***$2');
  console.log(`Event: ${event}, User: ${maskedEmail}`);
}

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Common Challenges and Fixes

4. Ensure Log Retention Policies

To ensure log retention, you can configure your TypeScript application to store logs in cloud storage (e.g., Amazon S3 or Azure Blob Storage) with appropriate lifecycle policies. Here’s an example of using AWS SDK to upload logs to an S3 bucket:

Example: Uploading Logs to S3 with Retention Policy

import AWS from 'aws-sdk';
import fs from 'fs';

// Configure AWS SDK
const s3 = new AWS.S3({
  accessKeyId: 'your-access-key',
  secretAccessKey: 'your-secret-key',
  region: 'your-region',
});

// Function to upload logs
async function uploadLogToS3(logFileName: string, bucketName: string) {
  const logData = fs.readFileSync(logFileName);

  const params = {
    Bucket: bucketName,
    Key: `logs/${logFileName}`, // Save logs in a "logs" folder
    Body: logData,
  };

  try {
    await s3.upload(params).promise();
    console.log(`Log file uploaded successfully: ${logFileName}`);
  } catch (error) {
    console.error(`Failed to upload log file: ${error}`);
  }
}

// Usage
uploadLogToS3('error.log', 'your-s3-bucket-name');

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5. Leverage AI for Anomaly Detection

AI-based anomaly detection helps identify unusual patterns in logs. Libraries like TensorFlow.js can be used to build a basic anomaly detection system in TypeScript.

Example: Simple Anomaly Detection with TensorFlow.js

import * as tf from '@tensorflow/tfjs-node';

// Sample log data (normal and abnormal events)
const logs = [
  { id: 1, eventType: 'login', responseTime: 200 },
  { id: 2, eventType: 'dataFetch', responseTime: 300 },
  { id: 3, eventType: 'unauthorizedAccess', responseTime: 1000 }, // Abnormal
  { id: 4, eventType: 'logout', responseTime: 250 },
];

// Convert log data to tensors
const logTensor = tf.tensor(logs.map(log => [log.responseTime]));

// Define a threshold for anomaly detection
const threshold = 500;

// Detect anomalies
logTensor.data().then(responseTimes => {
  responseTimes.forEach((time, index) => {
    if (time > threshold) {
      console.log(`Anomaly detected in log ID ${logs[index].id}: Response time = ${time}`);
    }
  });
});

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Additional Resources

Want to learn how to secure your APIs? Visit our blog on How to Fix API Vulnerabilities in OpenCart.

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6. Integrate with SIEM Tools

Use Security Information and Event Management (SIEM) tools for centralized log analysis. Example:

function sendLogsToSIEM(log: string) {
  // Send logs securely to SIEM
  console.log(`Sending log to SIEM: ${log}`);
}

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Related Blog Posts for Further Learning

1. Fix Weak Password Policies in TypeScript
2. Prevent Clickjacking in TypeScript
3. Fix API Vulnerabilities in TypeScript-Based ERP
4. Mitigate IDOR in RESTful APIs
5. Explore more topics at CybersRely Blogs.

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7. Enable Error Tracking

Use tools like Sentry for real-time error tracking in TypeScript applications.

Example Integration:

import * as Sentry from '@sentry/node';

Sentry.init({ dsn: 'your-dsn-here' });

// Capture an error
Sentry.captureException(new Error('Unauthorized action detected'));

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8. Regularly Test Logs for Accuracy

Conduct periodic audits to ensure logs capture necessary information.

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Conclusion

Addressing insufficient logging and monitoring in TypeScript ERP is not just a technical necessity—it’s a business imperative. Implement these 10 best practices to safeguard your applications, ensure compliance, and enhance security.

By applying these measures, you can confidently navigate today’s complex cybersecurity challenges.

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