DLP, Traffic Replay, and the Missing Link to Software Quality

Executive Summary

In Part 1 and Part 2 we explored why testing modern software is so difficult. Production data is the most valuable input for testing, but it’s locked away because it contains PII and sensitive context. Traditional Synthetic Data Generation (SDG) was built for batch databases, not streaming systems. And AI coding agents amplify every weakness in existing test strategies because they need current, realistic data or they generate buggy code based on outdated assumptions.

This final post ties everything together. The missing piece is Data Loss Prevention (DLP) applied directly to production traffic, enabling safe observability and realistic traffic replay. When combined, these capabilities allow teams to test how systems actually behave without compromising security or compliance.

The real problem we’re solving

The challenge facing modern engineering teams isn’t a lack of tools. It’s a lack of safe realism. Every team wants production-grade test coverage, confidence in releases, and faster iteration cycles. But they are forced to choose between real data and safe data. Most settle for safety, accepting lower-quality tests as the cost of compliance.

That tradeoff is no longer necessary.

What Data Loss Prevention actually means in practice

DLP is often thought of as a compliance checkbox: prevent sensitive data from leaking. In modern systems, DLP must do more. It needs to identify sensitive data across protocols and payloads, decode formats like JSON, Protobuf, and Base64, understand tokens like JWTs beyond simple replacement, and apply consistent, policy-driven transformations.

Effective DLP doesn’t just remove data. It preserves structure, behavior, and intent while eliminating risk.

How DLP unlocks deeper observability

PII is the reason observability tools stop short of showing real payloads. When DLP is applied to traffic, developers can safely inspect request and response bodies. Tokens and claims can be understood without exposure. Edge cases and failure modes become visible, and debugging shifts from guessing to knowing.

DLP doesn’t replace observability. It makes observability useful again.

Why traffic replay changes testing fundamentally

Traffic replay captures how systems behave, not just what data they store. With replay, teams can reproduce production incidents deterministically, test real request sequences and timing, validate changes against real-world traffic patterns, and load test with realistic payloads and distributions.

This moves testing from “did we think of this case?” to “did production already do this?”

Why DLP or SDG alone don’t increase velocity

Neither DLP nor SDG, by itself, increases development velocity. SDG provides static datasets. DLP provides safety guarantees. Velocity comes from feedback loops: seeing how changes behave under realistic conditions and fixing issues before release. Traffic replay creates that loop. DLP makes it safe.

This is especially critical for AI coding agents. When agents work with stale or synthetic data that doesn’t reflect current production behavior, they make assumptions that lead to bugs. They might generate code that handles yesterday’s API format but breaks on today’s traffic patterns. Fast, automated data refresh isn’t optional when AI agents are part of your development workflow. It’s the only way to prevent the cascade of subtle bugs that come from outdated context.

Traffic replay requires DLP to be safe

Replaying production traffic without DLP is irresponsible. Production traffic contains user identities, credentials and tokens, confidential business data, and third-party secrets. DLP ensures that sensitive data is transformed consistently, compliance requirements are met automatically, replay environments behave realistically without risk, and teams can scale testing without manual review.

DLP is not an add-on to traffic replay. It’s a prerequisite.

Introducing the Speedscale DLP Engine

Speedscale’s DLP Engine is built specifically for traffic replay in modern systems. It inspects live traffic across APIs, gRPC, and databases, decodes binary and encoded payloads, applies policy-driven transformations per technology, and preserves behavioral fidelity while eliminating sensitive data.

As part of Speedscale’s traffic replay solution, the DLP Engine enables teams to safely record, replay, and test real production behavior, continuously. This is how testing keeps up with modern software.

Building your own DLP and traffic replay system

For teams interested in building their own DLP and traffic replay solution, here’s a technical overview of what’s involved. This is a complex undertaking, but understanding the architecture helps clarify why commercial solutions like Speedscale exist.

Traffic Recording

The first step is capturing network traffic. Several tools can handle this:

• mitmproxy: An open-source interactive HTTPS proxy that can intercept, inspect, and record HTTP/HTTPS traffic. It supports HTTP/1, HTTP/2, WebSockets, and provides both command-line (mitmdump) and interactive interfaces.
• proxymock: A transparent proxy tool that automatically records traffic without code changes, supporting HTTP, gRPC, databases, and cloud services.
• Wireshark: The industry-standard network protocol analyzer for capturing packets at the network layer. It captures raw packets but requires additional processing to extract application-level data.

Each tool has tradeoffs: mitmproxy is excellent for HTTP traffic, proxymock handles multiple protocols automatically but is not designed for remote collection and Wireshark provides the deepest visibility but requires more processing.

Protocol Translation and Normalization

Raw network captures aren’t immediately useful. You need to convert traffic into a normalized format, typically JSON, that can be processed programmatically.

This is where complexity increases significantly. Different protocols require different parsers:

• gRPC: Binary Protocol Buffers that must be decoded using .proto definitions
• Kafka: Binary message format with headers, keys, and values
• RabbitMQ: AMQP protocol with exchanges, routing keys, and message properties
• Postgres/MySQL: SQL queries and binary result sets
• HTTP/2: Multiplexed streams with HPACK header compression

Each protocol needs a translation layer that understands its wire format, extracts meaningful data, and converts it to a common JSON structure. This translation must preserve relationships (which request led to which response) and timing information.

PII Detection

Once traffic is normalized to JSON, you need to identify sensitive data. Two approaches work:

Pattern-based detection: Create a JSON walker that traverses the structure looking for common PII patterns:

• Email addresses (regex patterns)
• Credit card numbers (Luhn algorithm validation)
• Social Security Numbers (format matching)
• Phone numbers (country-specific patterns)
• IP addresses
• API keys and tokens (pattern recognition)

AI-based detection: Use pre-trained models for more sophisticated detection:

• HuggingFace PII models: Models like nvidia/gliner-PII detect 55+ categories of PII, or iiiorg/piiranha-v1-detect-personal-information which achieves 98%+ recall on PII tokens
• Google Cloud DLP API: A managed service that detects 100+ built-in infoTypes (names, emails, SSNs, credit cards, etc.) and supports custom detectors
• AWS Macie: Amazon’s managed data security service with PII detection capabilities

Cost consideration: Be aware that HuggingFace inference APIs, Google Cloud DLP API, and AWS Macie all charge per-use fees. Processing large volumes of traffic can become expensive quickly. HuggingFace models can be self-hosted to avoid API costs, but that requires infrastructure and model management. Pattern-based detection has no per-use costs but may miss edge cases.

AI models are more accurate but add latency and cost. Pattern matching is faster but misses edge cases. Many systems use both: patterns for common cases, AI for validation.

Decoding Embedded Payloads

Sensitive data often hides inside encoded fields. Your DLP system must decode Base64 (common in APIs), JWTs whose payload claims may include user IDs or emails, URL-encoded values, compressed data (gzip, deflate, brotli), and nested JSON objects that may contain PII at any depth.

This requires recursive decoding: decode Base64, parse the result, check if it’s JSON, parse that, and continue until you’ve extracted all nested structures.

Packaging Scrubbed Snapshots

After scrubbing, package the sanitized data for different use cases. CI/CD pipelines need lightweight snapshots that can be versioned and stored in repositories. Local debugging requires full-fidelity recordings developers can replay on their machines. AI training benefits from clean datasets without real user data.

Consider format choice: JSON is machine-readable and preserves structure, but if you plan to use LLMs for analysis, Markdown can be easier for models to parse and understand context.

Mock Server and Test Driver

To replay scrubbed traffic, you need a system that serves mock responses matching recorded requests, supports the original protocols (HTTP, gRPC, Kafka, etc.), handles protocol-specific requirements like gRPC service definitions and Kafka topic routing, and can modify network behavior for latency injection or error simulation.

This often requires protocol-specific implementations. For example, a gRPC mock server needs the original .proto files to generate valid responses. A Kafka replay system needs to publish to the correct topics with proper partitioning.

Value Synchronization

One of the most challenging aspects: when you replace PII with synthetic values, you must maintain consistency across services.

If an email address appears in an inbound API request, a database query response, an outbound API call to a third party, and a log message, all instances must use the same replacement value. Otherwise, tests fail because the system expects user@example.com but finds test-user-123@test.com.

This requires cross-service tracking to maintain a mapping of original to synthetic values, realistic generation so that synthetic values match expected formats (valid email domains, realistic names), and stateful replacement that remembers substitutions across the entire recording session.

Automation and Refresh

This entire process must be automated and run periodically. Production traffic patterns change, new endpoints emerge, and data structures evolve. Static snapshots become stale quickly.

The urgency of refresh increases dramatically when AI coding agents are involved. Agents trained or prompted with week-old traffic data will generate code that doesn’t match current production behavior. They’ll miss new error cases, misunderstand updated API contracts, and introduce bugs that only appear when real traffic hits. If you handle PII and want AI agents to be effective, you need automated DLP and traffic replay systems that refresh data continuously, not monthly snapshots that are outdated before they’re distributed.

Automation needs to trigger recordings on a schedule or event, process new traffic automatically, validate scrubbed data for completeness, and distribute updated snapshots to developers and CI systems. It also must handle failures gracefully so a processing error doesn’t break your builds.

Commercial Solutions

Building a production-grade DLP and traffic replay system requires significant engineering effort across protocol parsing, PII detection, data transformation, and replay infrastructure. Speedscale provides a commercial solution that handles all these complexities, but understanding the architecture helps teams evaluate whether to build or buy.

Closing: quality, speed, and confidence

Modern software quality isn’t about perfect test coverage. It’s about realistic confidence. When teams can observe real behavior safely, reproduce production scenarios reliably, and test changes against reality instead of assumptions, they release faster with less risk.

DLP makes data safe.
Traffic replay makes data useful.

Together, they close the gap between testing and production.

The AI agent imperative

If your team uses AI coding agents (and most teams do) this gap becomes critical. AI agents need current, realistic data to generate correct code. When agents work with outdated snapshots or synthetic data that doesn’t reflect production, they introduce bugs based on stale assumptions. They’ll generate code for API formats that changed last week, handle error cases that no longer occur, and miss edge cases that emerged in recent traffic.

The problem compounds because agents generate buggy code, which gets deployed, which creates new failure modes, which agents then misunderstand because they’re still working with old data. The cycle expands exponentially.

If you handle PII, you face a choice to invest in automated DLP and traffic replay systems that keep data current, or accept that AI agents will generate buggy code based on outdated information. There’s no middle ground. Manual data refresh is too slow, and static datasets become obsolete faster than you can distribute them.

Teams that invest in continuous, automated DLP and traffic replay don’t just get safer testing, they get AI agents that actually work. Agents can reason about current production behavior, generate code that matches real traffic patterns, and catch issues before they reach users. This isn’t a nice-to-have for teams using AI agents. It’s a prerequisite for quality.