AX1000A AI Software Integrity Builder

AI Assurance Approached in a Lifecycle

The AI Software Integrity Builder introduces a novel, lifecycle-based approach to AI Assurance, designed to transform how AI-enabled systems are validated and maintained in safety-critical environments such as automotive. As AI development grows in complexity and regulatory pressure intensifies, this integrated solution delivers transparent, adaptable, and data-driven validation across the entire AI lifecycle.

AI systems operate as complex, dynamic entities, yet many of their decisions remain hidden from developers and users. This black-box nature and corresponding lack of transparency creates significant challenges for industries where safety and compliance are non-negotiable. Standards such as ISO/PAS 8800 and emerging regulations like the EU AI Act demand AI explainability and validation of safety in deployment but provide limited guidance on how to achieve this. Fragmented toolchains and siloed workflows further increase risk and effort, leaving gaps in regulatory conformance.

Keysight’s AI Software Integrity Builder addresses these challenges by providing holistic AI Assurance capabilities, enabling teams to answer the critical question: What happens inside my black box, and how do I ensure a trustworthy AI deployment?

Integrated Software Framework Supporting AI Development & Maintenance

The solution helps to deliver the safety evidence required for regulatory conformance, empowering teams to validate, explain, adjust, and continuously improve AI systems. Unlike fragmented toolchains that address isolated aspects of AI testing, Keysight’s integrated framework spans the entire lifecycle: from dataset analysis and model validation to inference-based testing in real-world environments.

Core capabilities include:

• Dataset Analysis: Analyzes data quality using statistical methods to uncover biases, gaps, and inconsistencies that may affect model performance.
• Model-Based Validation: Explains model decisions and uncovers hidden correlations, enabling developers to understand the patterns and limitations of an AI System.
• Inference-Based Testing: Goes beyond static validation by analyzing how the model performs in real-world environments, detecting deviations from training behavior, and recommending improvements for the next iteration.

This unified approach ensures traceability, explainability, and compliance by design, empowering engineering teams to deploy AI systems that are not only performant but also auditable.

Function, System and Domain Validation for Trustworthy AI Deployment

AI failures often occur due to system-level interactions or domain-level gaps, not just model errors. Holistic validation goes beyond the AI model itself. It must consider the model’s integration into the overall system and the adaptation to the Operational Design Domain (ODD).

Keysight’s AI Software Integrity Builder provides a unified AI assurance approach with metrics and tools to evaluate AI at the function, system, and domain level, ensuring they perform reliably and safely within complex and safety-critical environments.

By validating AI at all levels, Keysight delivers a comprehensive AI assurance framework throughout development and maintenance, ultimately enabling safer, more effective AI-enabled solutions and trustworthy AI deployment in conformance with regulatory requirements.

Ensure compliance, reliability, and trustworthiness in every step of your AI lifecycle. Contact Keysight to build a future-proof AI assurance approach for your organization.