AI Compliance Standards

NIST AI RMF, the EU AI Act, and ISO/IEC 42001 require auditable, continuous evaluation of AI systems. SigRank provides governed operator evaluation — public, content-free, and continuous.

Compliance requires evaluation. Evaluation requires the operator layer.

NIST AI RMF calls for continuous, auditable evaluation. SigRank delivers it for the operator layer — governed by MO§ES, privacy by design.

NIST AI RMF

A voluntary framework for managing AI risks. Four functions: Govern, Map, Measure, Manage.

Relevance: the Measure function requires continuous evaluation
SigRank role: governed operator evaluation under MO§ES
Status: voluntary, non-regulatory

EU AI Act

A regulatory framework for AI in the European Union. Risk-based classification with obligations for high-risk systems.

Relevance: high-risk systems require continuous monitoring and evaluation
SigRank role: content-free telemetry avoids data processing risks
Status: binding regulation

ISO/IEC 42001

An AI management system standard. Provides a certifiable framework for AI governance.

Relevance: requires documented evaluation processes
SigRank role: auditable, public evaluation records
Status: certifiable standard

MO§ES Framework

The governance framework behind SigRank. Defines measurement, privacy, and accountability for operator evaluation.

Scope: operator-layer measurement and governance
Privacy: content-free by design — token counts only
Accountability: public, auditable results

SigRank and the NIST AI RMF Measure function

The NIST AI RMF Measure function calls for "quantitative analysis of AI system performance and risk." SigRank directly supports this for the operator layer. It provides quantitative, continuous, auditable measurement of how effectively humans use AI — using Yield (Υ) = (cache_read × output) / input², computed from four token pillars: input, output, cache-read, cache-write.

The measurement is governed by MO§ES, which defines the specification, privacy boundaries, and public accountability requirements. This makes SigRank suitable for organizations that need to demonstrate ongoing, auditable evaluation of their AI operators as part of their compliance program.

Υ = (cache_read × output) / input²

AI Operator Standard

The Upsilon measurement specification for the operator layer.

Public Operator Evals

The public evaluation layer for AI operators — ranked by Yield (Υ).

AI Evaluation Frameworks

From NIST AI RMF to SigRank — the landscape of evaluation frameworks.

Privacy-Preserving AI Telemetry

How content-free token telemetry enables evaluation without privacy risk.

FAQ

What are AI compliance standards?: AI compliance standards are frameworks and regulations that require organizations to assess, document, and manage AI system risks. Key examples include the NIST AI Risk Management Framework (AI RMF), the EU AI Act, and ISO/IEC 42001. These standards require auditable, continuous evaluation of AI systems. SigRank supports compliance by providing governed, public, continuous operator evaluation under the MO§ES framework.
How does SigRank help with AI compliance?: SigRank supports the Measure function of the NIST AI RMF by providing governed, continuous, auditable operator evaluation. The MO§ES framework defines the measurement specification, privacy boundaries, and public accountability requirements. SigRank's evaluation is public (auditable), content-free (privacy-compliant), and continuous (not one-time). This makes it suitable for compliance contexts where organizations must demonstrate ongoing evaluation of their AI operators.
What is the NIST AI RMF?: The NIST AI Risk Management Framework (AI RMF) is a voluntary, non-regulatory framework for managing risks associated with AI systems. It defines four functions: Govern (establish governance), Map (identify risks), Measure (assess and evaluate), and Manage (prioritize and mitigate). The Measure function calls for continuous, auditable evaluation. SigRank directly supports the Measure function for the operator layer.
How does SigRank handle privacy compliance?: SigRank is privacy-preserving by design. It collects token counts only — the four pillars of input, output, cache-read, and cache-write. It never collects prompt content, response text, source code, or conversation. Yield (Υ) = (cache_read × output) / input² is computed entirely from numeric telemetry. This means SigRank can evaluate operators without creating privacy risks or requiring data processing agreements for sensitive content.

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SigRank and the NIST AI RMF Measure function

Υ = (cache_read × output) / input²

FAQ

What are AI compliance standards?

AI compliance standards are frameworks and regulations that require organizations to assess, document, and manage AI system risks. Key examples include the NIST AI Risk Management Framework (AI RMF), the EU AI Act, and ISO/IEC 42001. These standards require auditable, continuous evaluation of AI systems. SigRank supports compliance by providing governed, public, continuous operator evaluation under the MO§ES framework.

How does SigRank help with AI compliance?

SigRank supports the Measure function of the NIST AI RMF by providing governed, continuous, auditable operator evaluation. The MO§ES framework defines the measurement specification, privacy boundaries, and public accountability requirements. SigRank's evaluation is public (auditable), content-free (privacy-compliant), and continuous (not one-time). This makes it suitable for compliance contexts where organizations must demonstrate ongoing evaluation of their AI operators.

What is the NIST AI RMF?

The NIST AI Risk Management Framework (AI RMF) is a voluntary, non-regulatory framework for managing risks associated with AI systems. It defines four functions: Govern (establish governance), Map (identify risks), Measure (assess and evaluate), and Manage (prioritize and mitigate). The Measure function calls for continuous, auditable evaluation. SigRank directly supports the Measure function for the operator layer.

How does SigRank handle privacy compliance?

SigRank is privacy-preserving by design. It collects token counts only — the four pillars of input, output, cache-read, and cache-write. It never collects prompt content, response text, source code, or conversation. Yield (Υ) = (cache_read × output) / input² is computed entirely from numeric telemetry. This means SigRank can evaluate operators without creating privacy risks or requiring data processing agreements for sensitive content.