Software Engineering & Security Practices

GMLP Principle 2. Bring software engineering rigor, cybersecurity discipline, and quality management to the AI/ML lifecycle the way you bring them to your firmware.

Overview

AI/ML codebases that started in research notebooks rarely have the engineering discipline that medical devices require. Code review, version control, dependency management, and reproducibility need explicit attention.

We assess the gap and stand up the practices: IEC 62304-aligned where applicable, cybersecurity baseline embedded, quality management hooks tied to your QMS.

Our Process

  1. 1

    Engineering posture review

    Code review, CI/CD, dependency management, reproducibility.

  2. 2

    Cybersecurity baseline

    Threat modeling, secure SDLC, dependency scanning.

  3. 3

    Quality management hooks

    Integration with your QMS for change control and validation.

  4. 4

    Risk management linkage

    ISO 14971 alignment with software risk.

  5. 5

    Continuous improvement cadence

    Quarterly review of practices.

Frequently Asked Questions

IEC 62304 for AI/ML?

Yes, interpreted for ML lifecycle realities.

Cybersecurity standards alignment?

FDA premarket cybersecurity 2023, NIST SP 800-218.

How does this integrate with our existing QMS?

We hook into your existing change control, design control, and CAPA processes.

Can we do this incrementally?

Yes, most teams stage maturity over 6-12 months.

Treat the ML codebase like a medical device.

Send us your current engineering posture. We will return a gap analysis within four weeks.

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