HAIECDocumentation
Learn how to define AI systems, connect evidence sources, understand AI actions and access, run evaluations, review bounded Assurance, and verify Decision Receipts.
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Foundational guides for the HAIEC evidence-bound assurance workflow.
What is HAIEC?
Evidence-bound assurance for consequential AI systems
Getting Started
Define, connect, collect evidence, map actions, evaluate, assure, verify
AI Systems
Define and govern bounded AI systems
System Constellation
See what AI systems can reach, change, and trigger
Evidence
Organization-wide evidence library
Assurance
Canonical ALLOW / REVIEW / BLOCK dispositions
Decision Receipts & Verification
Public verification of decision receipt integrity
Developers
Developer security packages and integration guides.
AI Security Scanner
Static code analysis for AI attack surfaces
AI AppSec (Open Source)
Semgrep-backed static source-code analysis package
MCP Tenant Isolation
Tenant isolation boundary checks
LLMVerify
LLM input/output verification
CI/CD Integration
GitHub App, CI/CD, and CLI setup
GitHub Integration
GitHub App setup and repository signals
Evidence Sources & Connections
Supported evidence producer categories. Registered does not mean connected; connected does not mean evaluated.
Standards & Frameworks
Framework and regulation documentation. Framework mapping is not Assurance and not certification.
Reference
Evidence semantics, coverage limitations, assurance dispositions, and verification.
Ready to Get Started?
Define your first AI system and start collecting evidence.