AI OSI™ is an independent governance architecture for making AI decisions traceable, auditable, and defensible.
It defines the minimum evidence required to reconstruct and defend AI-assisted decisions later — often long after the system, team, vendor, or context has changed.
Architecture, not policy. Evidence, not aspiration.
The Accountability Gap
The Governance Problem AI Has Created
Artificial intelligence now operates as institutional infrastructure.
It makes decisions, evolves over time, aggregates sensitive data, and influences material outcomes across healthcare, finance, public administration, security, and critical infrastructure.
Yet when AI decisions are examined later — by auditors, regulators, courts, or boards — most organizations cannot answer basic questions:
What decision was actually made?
Who was accountable for it?
What inputs and constraints were relied on at the time?
Was the decision still valid when it was used?
Existing tools — model cards, ethics reviews, risk rubrics, policy attestations — do not create durable evidence.
They document intent. They do not preserve accountability over time.
The Core Insight
Decisions Outlive Their Context
Most AI failures are not caused by obviously bad decisions.
They are caused by decisions that outlived the assumptions under which they were made.
Teams change.
Models update.
Vendors rotate.
Regulations evolve.
But the decision remains in force — undocumented, unexamined, and invisible — until something goes wrong.
AI governance fails not because standards are missing, but because decision memory is missing.
The Architecture
What AI OSI Is
AI OSI™ (Artificial Intelligence Open Standard Interconnection) is a layered governance architecture for AI systems.
It separates mandate, data, models, instruction, reasoning, deployment, and disclosure into explicit layers of accountability, each with defined obligations and evidence outputs.
At its foundation is a simple but critical requirement:
If a decision matters, it must leave a record.
If a record still matters, it must still be valid.
This structure allows institutions to:
Identify where a failure occurred
Demonstrate oversight to regulators and auditors
Contain risk before it propagates
Preserve accountability across time, turnover, and system change
AI OSI is not related to the ISO OSI networking model.
It applies the principle of layered systems to governance and evidence.
What AI OSI Is Not
AI OSI is not:
A regulator, standard, or certification body
A compliance guarantee or legal service
A product, platform, or enforcement mechanism
An operational control system
AI OSI provides governance architecture and evidence structures — not approvals, outcomes, or authority.
More details:
AI OSI Core
The Minimal Unit of Accountability
At the center of AI OSI is AI OSI Core — a minimal, executable decision record standard.
AI OSI Core defines the smallest possible “receipt” that still allows a decision to be reconstructed later:
What decision was made
Who was accountable
What system was used
What inputs mattered
What constraints applied
How long the decision was valid
No ethics baked in.
No enforcement.
No scoring.
No guarantees.
Just inspectable, time-bounded evidence that the decision happened.
AI OSI Core is implemented today as open documentation and local-first tools that anyone can run, fork, or audit.
Why Governance Breaks Down
Why AI Governance Fails in Practice
Most AI governance efforts focus on principles, policies, and frameworks.
What they lack is infrastructure.
Documentation is fragmented.
Reasoning is ephemeral.
Accountability depends on post-hoc narratives assembled under pressure — often after systems have changed and people have moved on.
When scrutiny arrives, organizations are forced to explain AI behavior using incomplete records, informal memory, or reconstructed logic that does not survive serious examination.
AI governance fails quietly — and ambiguously.
Ambiguity is the enemy of accountability.
The AI OSI Stack
How AI OSI Works
The AI OSI Stack defines a multi-layer governance backbone for AI systems.
Each layer specifies:
Its scope of authority
What decisions occur there
What evidence must exist
The Stack is implementation-neutral and compatible with existing AI toolchains, cloud environments, and model providers.
Governance is embedded upstream of execution, ensuring accountability survives system upgrades, vendor churn, staff turnover, and shifting legal regimes.
Intended Stewardship Roles
Who This Is For
AI OSI is designed for people who cannot afford “we don’t know” as an answer:
Board directors and audit committees
General counsel and legal oversight teams
CISOs and cyber-risk leaders
Regulators, auditors, and oversight bodies
Public-sector and high-risk AI operators
It is for environments where decisions must be explainable after the fact — not just reasonable at the time.
Regulatory Compatibility
Standards & Regulatory Alignment
AI OSI is jurisdiction-agnostic by design.
Its architecture is aligned with:
EU AI Act (including Annex IV documentation requirements)
ISO/IEC 42001
NIST AI Risk Management Framework
Alignment is architectural, not declarative. AI OSI specifies what must be provable — independent of jurisdiction, vendor, or deployment model.
Download Canonical Documents
Canonical Briefings & Technical Papers
AI OSI is defined through versioned, archived, reference-grade documentation.
Canonical specifications, alignment notes, executive briefings, and development records are published as part of a permanent governance record, with independent timestamping and verifiable version history.
Project Status and Scope
Status, Independence, and Direction
AI OSI is an independent governance architecture initiative.
It is not a regulator, standards body, certification authority, or commercial AI product.
The work is published for evaluation on its architectural and evidentiary merits, independent of institutional affiliation or endorsement.
Inquiry and Correspondence
Contact
For inquiries, correspondence, or exploratory conversations, please use the Contact page.
Informal, off-the-record discussions are welcome. Engagement does not imply endorsement, adoption, or public attribution.
Institutional, academic, and public-sector inquiries are particularly encouraged.