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Research Framework for AI Safety Governance

# Structural AI Safety
for Strategic Leaders

A governance framework designed to help organizations navigate AI risks,
compliance requirements, and safety challenges through architectural controls.

Assess Your Organization Read Documentation

## Strategic Challenges Tractatus Addresses

Organizations deploying AI systems face regulatory, technical, and reputational risks.
Tractatus offers a structural approach to mitigation.

### Regulatory Compliance

€35M
EU AI Act Maximum Fine

- Designed to align with EU AI Act requirements
- Architectural controls for risk management
- Auditable decision-making processes

CORE VALUE

### Technical Risk Mitigation

5 Agents
Governance Components

- BoundaryEnforcer for values alignment
- CrossReferenceValidator for consistency
- ContextPressureMonitor for session management

### Early-Stage Research

Open
Research Framework

- Development framework for AI governance
- Proof-of-concept for LLM safety patterns
- Foundation for future governance systems

## AI Governance Readiness Assessment

Before implementing governance frameworks, organizations need honest answers to difficult questions.
This assessment helps identify gaps, risks, and organizational readiness challenges.

### Current AI Tool Inventory

Do you have clear visibility into what AI systems are already in use?

- □ Have you catalogued all AI tools currently used across departments (ChatGPT, Claude, Copilot, internal LLMs, etc.)?
- □ Do you know which employees are using AI tools for customer-facing communications, code generation, or decision support?
- □ Can you identify which AI interactions involve proprietary data, customer information, or confidential business intelligence?
- □ Do you have visibility into shadow AI usage (employees using personal accounts for work tasks)?
- □ Have you documented which third-party vendors are using AI in services they provide to you?

### Strategic AI Deployment Plans

What are you planning to build, and have you assessed the governance implications?

- □ Have you prioritized AI initiatives by risk level (customer-facing vs. internal, high-stakes vs. low-stakes)?
- □ For each planned AI system, have you identified who is accountable when it makes a mistake or causes harm?
- □ Do you have criteria for determining which decisions should remain human-controlled vs. AI-assisted vs. fully automated?
- □ Have you evaluated whether your planned AI deployments fall under EU AI Act "high-risk" categories?
- □ Can you articulate what "safe failure" looks like for each planned AI system?

### Workflow & Process Integration

How will AI fit into existing processes, and what breaks when it fails?

- □ Have you mapped out which human roles will shift from "doer" to "reviewer/validator" of AI output?
- □ Do you have processes to detect when employees are blindly accepting AI recommendations without validation?
- □ Can your organization sustain critical operations if AI systems become unavailable for hours or days?
- □ Have you considered the handoff points between AI-generated work and human-controlled processes (e.g., draft→review→approval)?
- □ Do you know which workflows will require sequential AI operations, and how errors compound across multiple AI steps?
- □ Have you assessed whether introducing AI will create new bottlenecks (e.g., senior staff spending all day reviewing AI output)?

### Decision Authority & Boundaries

Who decides what AI can and cannot do, and how are those boundaries enforced?

- □ Have you defined which types of decisions AI systems are prohibited from making (even with human oversight)?
- □ Do you have a governance board or designated owner responsible for AI safety and compliance decisions?
- □ Can you enforce AI usage policies technically (not just via policy documents employees may ignore)?
- □ Have you established clear escalation paths when AI systems encounter edge cases or ambiguous situations?
- □ Do you have audit mechanisms to detect policy violations or unauthorized AI usage patterns?

### Incident Preparedness

What happens when AI systems fail, hallucinate, or cause harm?

- □ Do you have incident response procedures specifically for AI failures (separate from general IT incidents)?
- □ Can you trace AI-generated content or decisions back to specific prompts, model versions, and responsible parties?
- □ Have you war-gamed scenarios where AI provides plausible-sounding but incorrect information that leads to business harm?
- □ Do you have kill switches or rollback procedures to disable AI systems that are behaving unpredictably?
- □ Have you assessed your liability exposure if AI systems discriminate, leak data, or violate regulations?

### Human & Cultural Readiness

Is your organization culturally prepared for the messy reality of AI governance?

- □ Have you addressed employee fears about job displacement or skill obsolescence honestly?
- □ Do your teams have the skills to critically evaluate AI output, or do they lack domain expertise to spot errors?
- □ Are employees empowered to challenge or override AI recommendations without career risk?
- □ Have you created incentives that reward thoughtful AI use over speed or cost savings alone?
- □ Does your organization have realistic expectations about AI limitations, or is there pressure to treat it as infallible?
- □ Have you allocated time and resources for governance work, or is it expected "on top of" existing responsibilities?

### What Your Answers Reveal

If you checked most boxes: You're ahead of most organizations but likely uncovering how complex AI governance truly is. The hard work is implementation and cultural change.

If you checked some boxes: You have awareness but significant gaps. These gaps represent risk, but also clarity about where to focus governance efforts.

If you checked few boxes: You're in good company—most organizations are here. The challenge is building governance capability while AI deployment accelerates around you.

Note: This assessment is designed to provoke strategic thinking, not to sell you a solution. Effective AI governance requires organizational commitment, not just technology purchases. Tractatus is a research framework exploring architectural approaches to some of these challenges—it is not a comprehensive answer to all the questions above.

## How Tractatus Works

Five integrated components work together to provide structural AI governance

### BoundaryEnforcer

Prevents AI systems from making values decisions without human approval. Ensures critical decisions remain under human control.

### InstructionPersistenceClassifier

Maintains organizational directives across AI interactions. Helps reduce instruction drift and inconsistency over time.

### CrossReferenceValidator

Validates AI decisions against established policies. Designed to detect potential conflicts before they occur.

### ContextPressureMonitor

Tracks AI session complexity and token usage. Helps prevent context degradation and maintains decision quality.

### MetacognitiveVerifier

Validates reasoning quality for complex operations. Designed to improve decision coherence and reduce errors.

## Stakeholder Considerations

How different leadership roles may evaluate Tractatus

### CEO / Founder

- • Structural approach to AI risk management
- • Potential competitive differentiation through governance
- • Framework for responsible AI deployment

### CFO

- • Designed to help reduce regulatory fine risk (€35M max)
- • May reduce AI project failure costs (42% industry avg)
- • Research-stage framework (ROI not yet established)

### CTO / Engineering

- • Architectural patterns for LLM safety controls
- • Reference implementation for governance agents
- • Adaptable to organizational tech stacks

### CISO / Security

- • Controls for AI decision boundaries
- • Audit trails for AI actions
- • Risk mitigation through architectural controls

### Legal / Compliance

- • Designed for EU AI Act alignment
- • Auditable decision-making framework
- • Documented governance processes

### Product / AI Teams

- • Framework for safer AI feature deployment
- • Designed to reduce failure modes
- • Patterns for responsible AI innovation

## Explore the Framework

Technical documentation and implementation resources

### Framework Documentation

Complete technical specifications, architecture guides, and implementation patterns

### Implementation Guide

Step-by-step integration guide for developers and technical teams

### Research & Case Studies

Academic foundations, failure mode analysis, and governance research

### About Tractatus

Core values, principles, and the philosophy behind the framework

### Business Case Template

Structured assessment template for evaluating AI governance needs with your organization's data

Download PDF Template

## Questions About Your Organization?

Start with honest assessment of where you are, not aspirational visions of where you want to be

Review Assessment Questions Our Approach & Values