Zero-Budget Data Sanitization in TypeScript: A Security Researcher’s Approach

In the realm of security research, clean data is the foundation for accurate analysis and threat detection. Yet, many researchers face the challenge of handling dirty or malicious data without the luxury of dedicated tools or extensive budgets. This article explores how a security researcher can leverage TypeScript, a powerful yet accessible language, to develop an effective data cleaning pipeline with zero budget.

Understanding the Problem

The core task here is to sanitize incoming data streams, which may include malformed entries, SQL injection attempts, or embedded malicious scripts. Traditional solutions might involve expensive tools or libraries, but with a strategic approach using TypeScript, it's possible to create a lightweight, maintainable, and robust cleaning process.

Setting Up the Environment

Since the goal is zero budget, we'll rely solely on open-source tools. Initialize a basic TypeScript project:

mkdir data-cleaner
cd data-cleaner
npm init -y
npm install typescript ts-node @types/node --save-dev
npx tsc --init

This setup ensures you can run TypeScript code directly with ts-node, perfect for quick iteration.

Core Data Cleaning Strategies

1. Basic Validation and Type Checks

Start by validating the shape and type of each data point:

interface RawData {
  id: any;
  name: any;
  email: any;
}

function validateData(data: RawData): data is { id: number; name: string; email: string } {
  return (
    typeof data.id === 'number' &&
    typeof data.name === 'string' &&
    typeof data.email === 'string'
  );
}

This ensures only properly typed data proceeds further.

2. Removing Malicious Scripts

Sanitizing string inputs, especially for email or name fields, prevents script injections:

function sanitizeString(input: string): string {
  return input.replace(/<script[^>]*>?[\s\S]*?<\/script>/gi, '')
              .replace(/[<>]/g, '');
}

Use this function to cleanse inputs before storage or processing.

3. Detecting and Rejecting Anomalies

Implement simple pattern checks and blacklists:

const suspiciousPatterns = [/\b(select|insert|delete|update)\b/i, /\/\*.*\*\//, /--/];

function isSuspicious(input: string): boolean {
  return suspiciousPatterns.some(pattern => pattern.test(input));
}

function cleanData(data: RawData): RawData | null {
  if (!validateData(data)) return null;
  if (isSuspicious(data.email)) return null; // Reject suspicious data
  return {
    id: data.id,
    name: sanitizeString(data.name),
    email: sanitizeString(data.email),
  };
}

This simple check works well against common injection attempts.

Enhancing and Scaling

While this approach is lightweight, security researchers can extend it by integrating heuristics, password hashing, and more sophisticated pattern matching, all within TypeScript. The key is modular functions that can be combined or upgraded as needed.

Conclusion

Handling dirty data without budget constraints demands ingenuity and a solid understanding of both security threats and programming techniques. TypeScript offers a flexible, type-safe environment to build resilient, customizable data cleaning pipelines. By combining validation, sanitization, and anomaly detection strategies, security researchers can ensure their data’s integrity, laying the groundwork for more accurate threat analysis and decision-making.

Adopting these practices doesn't just save money — it enforces a disciplined, transparent approach to data hygiene that benefits security posture overall.

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