About ATAL

Public Review Draft v0.9

1. Purpose

ATAL (AI Traceability & Accountability Ledger) is a comprehensive standard designed to make AI systems transparent, auditable, accountable, and safe.
Its goal is to ensure that every AI action — human-triggered or autonomous — is recorded in a way that supports regulatory scrutiny, forensic reconstruction, and operational governance.

ATAL provides a unified, structured, tamper-evident approach to documenting AI decisions across all model types, industries, and jurisdictions.

2. Why ATAL Exists

AI systems increasingly make decisions with material, legal, financial, or safety consequences.
However:

AI decisions are often opaque
tool and model chains are complex
evidence is hard to recreate
autonomy risks are rising
existing logs lack forensic integrity

ATAL provides a single answer to all of these challenges by defining:

what must be recorded
how it must be structured
how oversight must operate
how autonomy must be governed
how causality must be reconstructed

ATAL is not a monitoring tool or product.
It is a standard for accountability.

3. The 0th Law of AI Accountability

Any AI system capable of initiating or escalating actions without direct human instruction MUST be governed by an external, independent accountability layer that can observe, restrict, pause, override, or terminate those actions.

This is the central principle of autonomy governance and applies to any system with decision-making capabilities.

4. Scope of ATAL

ATAL covers:

human-initiated AI actions
autonomy-capable AI actions
per-decision evidence capture
model/toolchain provenance
self-modification tracking
emergent intent detection
full causal lineage representation
safety kernel enforcement
human- and autonomy-based risk tiers
governance and oversight structures

The full specification is structured into nine Parts (I–IX).
Consult the primer or the specification for details of each Part.

5. Regulatory Alignment

ATAL is designed to support compliance with:

DPDPA (India)
CERT-In Directions
EU AI Act
ISO/IEC 42001 (AI Management System)
sectoral frameworks (financial services, healthcare, critical infrastructure)

It provides regulators with an independent, forensic-ready foundation for overseeing AI systems.

6. Vendor-Neutral and Globally Applicable

ATAL does not prescribe:

which AI models to use
which cloud or vendor to adopt
how a product must be architected

Instead, it defines the mandatory evidence structures and governance requirements that any system must meet to be considered accountable.

Any organisation can implement ATAL on top of their existing infrastructure.

7. Relationship to Implementations

ATAL is the standard.
Implementations (including the reference implementation maintained separately by Elytra Security) must conform to ATAL.
This repository contains no implementation code.

The standard remains vendor-neutral, globally applicable, and open for public review.

8. Intended Beneficiaries

ATAL is designed for:

regulators
auditors
enterprises
policymakers
system architects
legal professionals
AI-risk and AI-safety researchers

It supports transparency and trust in all levels of AI deployment.

9. Evolution and Lifecycle

The ATAL Standard evolves through:

public review cycles
structured governance
editorial and steward oversight
versioned specifications

All changes are documented transparently.
The current release (v0.9) is open for public review.

End of document.

Last updated: 30 November 2025

Licensed under the ATAL Documentation License (see LICENSE.md).