DACS Specification

Can your organization defend the evidence behind its most important decisions?

Enterprises invest heavily in data governance, analytics, and AI. Yet when a material decision is questioned, technical lineage, dashboards, and approval records may not establish why the underlying evidence was appropriate and authorized for that specific use.

The Defensible Analytics Control Specification (DACS) defines 70 evidence-based controls for closing this accountability gap. It establishes how organizations test decision evidence, record control results, determine permitted use, and preserve the information needed to reconstruct and defend material decisions.

Why executives should read this specification

• Expose hidden decision risk. Understand how ambiguous business definitions, incorrect identities, unsupported relationships, and ungoverned AI outputs can undermine important decisions.
• Make accountability measurable. See how explicit Pass/Fail controls replace assumptions of trust with verifiable evidence.
• Establish clear decision boundaries. Understand how Gates determine whether evidence use may Proceed, requires Caution, must Stop, or remains Quarantined.
• Strengthen AI accountability. See how AI-generated evidence, recommendations, and agentic actions are evaluated against governed context, authority, and permitted use.
• Start without replacing existing systems. Apply DACS to one important decision path before expanding adoption.

What this specification does not claim

DACS does not guarantee correct decisions, certify products or organizations, establish legal compliance, or replace existing governance and technology. It defines the mandatory controls for determining whether evidence is ready for a specific decision and stated use.