In the rapidly evolving world of artificial intelligence, one topic keeps surfacing in every boardroom, compliance meeting, and engineering sprint: data privacy. Yet, as companies rush to adopt machine learning, too few realize how deeply privacy testing must evolve in parallel.
To understand what’s next for privacy engineering, we spoke with Alexey Vakulin, an experienced Software Development Engineer in Test (SDET) who has worked at the crossroads of AI systems and data protection.
His core message is simple — and urgent:
“Every privacy department needs at least one SDET who understands how to test AI and how to apply AI for privacy testing.”
Why Privacy Teams Can’t Afford to Ignore Testing Anymore
“Traditionally,” Vakulin begins, “privacy was seen as a checklist. Encrypt this, anonymize that, comply with GDPR. But when AI enters the picture, privacy stops being static — it becomes dynamic and unpredictable.”
He explains that modern AI systems can unintentionally memorize sensitive data, even when anonymized. Models might reproduce details from training sets, or allow attackers to reverse-engineer information through indirect prompts.
“It’s not enough to trust your data pipeline anymore,” Vakulin says.
“You have to test how your model behaves — what it remembers, what it forgets, and what it might reveal.”
This is where a skilled SDET with AI knowledge becomes critical. Instead of only testing features, they test data behavior — how information flows, transforms, and sometimes leaks.
Testing AI: A New Frontier for SDETs
Vakulin describes a new type of testing engineer — one who can move beyond scripts and assertions to think like a data scientist.
An AI-aware SDET, he says, can:
- Build synthetic datasets to test privacy without touching real PII.
- Simulate model inversion or data extraction attacks.
- Validate differential privacy guarantees and data minimization principles.
- Automate privacy checks in CI/CD pipelines so every code push is privacy-safe by default.
“Testing privacy in AI systems is not just about pass or fail,” he explains. “It’s about measuring exposure risks quantitatively and continuously. You need metrics for data leakage just like you have metrics for accuracy.”
When AI Becomes the Tester
Interestingly, Vakulin believes AI itself is also becoming an ally in this process.
“AI can help test AI,” he says with a smile. “That’s the beauty of it.”
He outlines examples already in use:
- NLP models scanning system logs or repositories for hidden identifiers.
- Computer vision AI flagging potential PII in images.
- LLM-based tools auto-generating privacy test cases from policy documents or user stories.
“These tools don’t replace engineers,” he notes. “They amplify them. They make privacy testing faster, smarter, and more consistent.”
From Compliance to Confidence
Vakulin envisions privacy testing as a confidence layer for AI systems — not just a regulatory requirement.
“Organizations that integrate privacy testing early build trust automatically,” he says. “They can move faster because their safeguards are built in, not bolted on. It’s like having security testing integrated into DevOps — now we need PrivacyOps with intelligent testing.”
He pauses, then adds thoughtfully:
“AI will keep evolving. Privacy risks will keep evolving. The only sustainable response is intelligent, automated testing — led by people who understand both worlds.”
The Bottom Line
As companies deploy AI across every department, privacy assurance can no longer rely solely on policy teams or compliance officers. It needs technical partners — engineers who can turn privacy principles into testable, measurable frameworks.
And that, Vakulin believes, is the role of the modern SDET.
“AI will define the next decade of technology,” he concludes.
“But trust will define which companies survive it.
The bridge between the two is testing.”
About the Author:
Alexey Vakulin is an SDET and AI testing advocate focused on building privacy-resilient systems through intelligent automation. His work explores the intersection of privacy, trust, and machine learning in modern software engineering.
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