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Mohammad Waseem
Mohammad Waseem

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Securing Legacy Test Environments: Using Rust to Eliminate PII Leaks

#rust #security #legacy #pii

In modern development workflows, safeguarding sensitive data, particularly Personally Identifiable Information (PII), is paramount—especially when dealing with legacy codebases. A common but often overlooked vulnerability occurs when PII leaks occur in test environments, potentially exposing user data and compromising compliance.

As a Lead QA Engineer, I faced this challenge firsthand. Our legacy monolithic system stored PII across multiple modules, many of which lacked proper data sanitization and exit points. Traditional approaches, such as refactoring or adding runtime checks in old languages like Java or Python, were either impractical or risky due to tight release schedules and fragile dependencies.

To address this, I opted to implement a robust, safe, and low-overhead solution using Rust—a language renowned for safety and performance. Rust’s ownership model and strict compile-time checks make it an ideal candidate for intercepting and sanitizing data before it leaves test environments.

Step 1: Identifying PII Exposure Points

The first step involved cataloging all the points where PII could be exposed. We mapped out data flow, pinpointing APIs, logging, and third-party integrations. Once identified, the goal was to create a wrapper around these data flows that would inspect and sanitize data proactively.

Step 2: Developing a Rust-Based Sanitizer

Given the impact of legacy code and the need for minimal intrusion, I developed a Rust library that acts as a shadow or proxy for data transmission points. This library inspects outgoing data streams, detects potential PII patterns, and masks or removes sensitive information.

Here's a simplified example of how the sanitizer looks:

use regex::Regex;

fn sanitize_pii(text: &str) -> String {
    // Define patterns for common PII like email, SSN, phone numbers
    let email_regex = Regex::new(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}").unwrap();
    let ssn_regex = Regex::new(r"\b\d{3}-\d{2}-\d{4}\b").unwrap();
    let phone_regex = Regex::new(r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b").unwrap();

    // Mask PII patterns
    let result = email_regex.replace_all(text, "[REDACTED_EMAIL]");
    let result = ssn_regex.replace_all(&result, "[REDACTED_SSN]");
    let result = phone_regex.replace_all(&result, "[REDACTED_PHONE]");

    result.to_string()
}

// Usage
fn log_data(data: &str) {
    let sanitized = sanitize_pii(data);
    println!("Logging sanitized data: {}", sanitized);
}
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This library can be compiled into a static library and linked with legacy code via FFI, or used as a standalone process that intercepts data flows through IPC mechanisms.

Step 3: Integrating into Legacy Systems

Integrating Rust into a legacy environment involves wrapping existing data channels—such as network sockets, file outputs, or logging libraries—with your sanitizer. Using Rust’s FFI, we create C-compatible functions that the C or Java code can call before transmitting or logging data.

For example:

#[no_mangle]
pub extern "C" fn process_data(input: *const c_char) -> *mut c_char {
    let c_str = unsafe { CStr::from_ptr(input) };
    let data = c_str.to_str().unwrap();
    let sanitized = sanitize_pii(data);
    CString::new(sanitized).unwrap().into_raw()
}
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This approach ensures that no PII leaves the environment unprocessed. The sanitizer acts as a safeguard, and its implementation via Rust guarantees zero-cost safety with minimal performance overhead.

Outcome and Best Practices

Implementing this Rust-based sanitizer successfully eliminated PII leaks during testing, bringing us into compliance with data protection standards like GDPR and HIPAA. Additionally, the approach provided a reusable pattern for other sensitive data flows.

Key takeaways include:

  • Leverage Rust’s safety to patch legacy vulnerabilities without refactoring.
  • Use regex-based pattern matching for flexible PII detection.
  • Integrate via FFI for minimal intrusion.
  • Continuously update detection patterns to adapt to new PII formats.

While this method is highly effective, ongoing maintenance and regular audits are crucial to ensure comprehensive coverage, especially as data formats evolve.

By embracing Rust's capabilities, the QA team protected user data, minimized risk, and enhanced trust, all within a legacy environment that previously lacked such safeguards.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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