# AI-Enabled Compliance for GxP Software

# Simplify change management and accelerate development across your AI lifecycle.

For many teams, internal processes and tools are the main limiting factors to releasing AI-driven innovations faster. Implementing AI/ML in regulated environments requires pharma and biotech teams to overcome these challenges while maintaining compliance with GxP standards.

AI Regulatory Compliance

### To use AI/ML in GxP environments, teams must scale innovation while staying compliant.

Advanced AI and ML models can enhance drug discovery, clinical trials, and manufacturing processes. But how do you integrate your AI/ML models into your existing validated systems and quality frameworks? Recent advancements in AI regulation provide opportunities for pharma companies to innovate responsibly.

## Enable your data scientists to release frequently and use open-source AI/ML packages.

Scale your machine learning models to real-world demands.

AI Governance

### Innovate and scale faster without sacrificing quality though better AI governance

Built-in enforcement gives your AI Governance Committee or CoE transparency and control.

Release controls gate release until all approvers have signed.

Part 11-compliant signatures ensure approvals follow QMS procedures.

Maintain a full audit history of how raw data is pre-processed for model training and validation.

### Accelerate AI compliance in software development and deployment while monitoring model drift and maintaining data integrity

Maintain control over AI/ML models and subsystems to ensure regulatory compliance

### Stay compliant with evolving AI regulations while accelerating innovation

Ensure AI/ML models remain compliant with FDA, EMA, and ICH GxP guidelines for AI in drug development, manufacturing, and clinical applications.

Built-in release gates ensure models have been validated before every deployment.

Transform data into specifications and leverage the specification approval process.

Robust validation and verification processes ensure high quality.

### Reduce the complexity of risk control and validation in AI-driven systems

Regulatory agencies require that any modifications to AI/ML models and subsystems undergo rigorous validation due to their potential impact on patient safety, efficacy, and product quality.

Enforce validation techniques to assess AI/ML model performance in regulated environments.

Ensure models perform well on new data through automated tests in your CI/CD pipeline.

Continuously monitor AI performance and risks in real-time to refine models based on new data.

### Establish traceability for AI/ML workflows in GxP environments

Enable state-of-the-art AI solutions while all work is documented automatically.

Automatically document your model development process as you build it.

Maintain traceability and visibility with an always up-to-date trace matrix.

Maintain a history of how raw data is pre-processed for model training and validation.

Connect to DataOps tooling to ensure traceability between model requirements and risks.

## Accelerate development across your AI lifecycle with Ketryx.