/read the white paper/

AI Systems Assurance

From Point-in-Time Testing to Continuous Evidence

Read the paper to understand why the trust model that worked for conventional software breaks down for production AI, and what it takes to continuously assure probabilistic AI systems in production.

The paper explores the shift from testing AI before deployment to continuously assuring it in production, covering:

• The limits of traditional testing for probabilistic AI systems.
• Why AI systems change the trust equation: from non-determinism to model, prompt and data drift.
• Tokenomics and Big-T, a new way to think about AI consumption and cost.
• Continuous AI Systems Assurance, turning production behavior into evidence.
• The assurance lifecycle, from governed calls and evidence to baselines, signals and system health.
• Continuous response and reporting, using assurance evidence to drive replay, action and signed assurance.

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Who it is for: Anybody interested in production AI

Format: 17 pages. Free PDF download.