How major technology companies govern AI — responsible AI frameworks, safety teams, model cards, red teaming, voluntary commitments, and internal governance structures.
Last updated: February 2026 10 Sections Industry Practices
Corporate AI governance has become a critical dimension of the broader AI governance landscape. As companies develop increasingly powerful AI systems, their internal governance structures, safety practices, and ethical commitments shape the technology’s trajectory as much as — or more than — government regulation.
Common Corporate Governance Elements
AI Principles: Published ethical guidelines for AI development
Responsible AI Teams: Dedicated teams for ethics, safety, and fairness
Review Boards: Internal or external committees reviewing high-risk AI applications
Model Cards / System Cards: Documentation of AI model capabilities, limitations, and evaluations
Red Teaming: Adversarial testing for safety, security, and misuse potential
Safety Evaluations: Pre-deployment testing including dangerous capability assessments
Acceptable Use Policies: Rules governing how products may be used
2. Google / DeepMind
2.1 AI Principles (June 2018)
Google was the first major tech company to publish formal AI principles, following internal controversy over Project Maven (DoD drone AI).
Be socially beneficial
Avoid creating or reinforcing unfair bias
Be built and tested for safety
Be accountable to people
Incorporate privacy design principles
Uphold high standards of scientific excellence
Be made available for uses that accord with these principles
2.2 Governance Structure
Responsible AI & Human-Centered Technology team: Centralized team conducting fairness, safety, and ethics reviews
DeepMind Safety team: Dedicated frontier AI safety research
AI Principles Review: Mandatory review for products and research touching sensitive areas
Model cards: Published for major models (Gemini, PaLM) documenting capabilities and evaluations
3. Microsoft
3.1 Responsible AI Standard (v2, 2022)
Microsoft’s internal standard translates its AI principles into concrete engineering requirements:
Privacy & Security: Data minimization; privacy reviews; threat modeling
Inclusiveness: Accessibility reviews; diverse user testing
Transparency: System cards; disclosure to users
Accountability: Human oversight; appeals processes; governance reviews
3.2 Governance Structure
Office of Responsible AI: Sets policy, governance rules, and processes company-wide
Aether Committee: Senior advisory body on responsible AI issues
Sensitive Uses review: Mandatory review for high-risk AI applications before deployment
Responsible AI Dashboard: Tooling for engineers to assess fairness and performance
4. OpenAI
4.1 Charter & Structure
OpenAI operates under a unique capped-profit structure with a nonprofit parent. Key governance elements:
Board of Directors: Nonprofit board retains control over safety decisions; authority to slow or stop deployments
Safety Advisory Group: Cross-functional team advising on deployment decisions
Preparedness Framework (2023): Risk assessment for frontier models across CBRN, cybersecurity, persuasion, and model autonomy
System cards: Published for GPT-4, GPT-4o, o1, DALL-E 3 detailing evaluations and limitations
4.2 Safety Practices
Red teaming: External red team assessments before major releases
Iterative deployment: Gradual rollout to learn from real-world use
Usage policies: Detailed acceptable use policy prohibiting specific harmful applications
Safety research: Alignment research team; superalignment initiative
5. Anthropic
5.1 Responsible Scaling Policy (RSP)
Industry First: Anthropic’s Responsible Scaling Policy (September 2023) was the first public commitment to tie model deployment decisions to concrete capability evaluations. It defines “AI Safety Levels” (ASL-1 through ASL-4) with escalating safety requirements as model capabilities increase.
5.2 AI Safety Levels
Level
Capabilities
Required Safeguards
ASL-1
No meaningful uplift for catastrophic risks
Basic safety measures
ASL-2
Current large models (some uplift possible but not beyond existing info)
Current security and deployment practices
ASL-3
Substantially increases risk of catastrophic misuse
Constitutional AI: Alignment method using principles to guide model behavior
Long-Term Benefit Trust: Corporate structure designed for long-term safety focus
Model cards: Published for Claude models with detailed capability and safety evaluations
6. Meta
6.1 Open-Source Approach
Meta’s AI governance is distinctive due to its commitment to open-weight model releases (Llama series), which raises unique governance questions about downstream use control.
6.2 Governance Elements
Responsible AI team: Cross-functional team embedded across product groups
Community standards: Acceptable use license for Llama models (restricts certain applications)
Safety evaluations: Red teaming and safety testing before model release
Transparency reports: Regular reporting on content moderation AI decisions
Open innovation: Argument that open release enables broader safety research and avoids AI concentration
7. Other Major Companies
Company
AI Principles
Key Governance Features
Notable Commitments
Amazon
Responsible AI policy (2023)
AI service cards; fairness tools (Clarify); safety reviews
White House commitments; NIST AI RMF alignment
Apple
Integrated into privacy principles
On-device processing emphasis; differential privacy; ML review
Privacy-focused AI governance; limited public disclosure
IBM
Principles for Trust and Transparency (2018)
AI Ethics Board; AI FactSheets; AI Fairness 360 toolkit
Early advocate; voluntary commitments; NIST partnership
Samsung
AI Ethics Principles (2022)
AI Ethics Committee; internal review process
Gauss model safety evaluations
Nvidia
Trustworthy AI principles
Red teaming for NeMo models; safety guardrails (NeMo Guardrails)
Deep synthesis compliance; algorithm registration (China)
Huawei
AI ethics principles
AI security framework; privacy by design
Chinese regulatory compliance; international standards participation
8. Voluntary Commitments
8.1 White House Voluntary AI Commitments (July 2023)
15 companies signed voluntary commitments on AI safety, security, and trust:
Commitment Area
Specific Actions
Safety
Internal and external security testing before release; sharing safety information with governments and other companies; investing in cybersecurity and insider threat safeguards
Security
Earning public trust through transparency; reporting on capabilities, limitations, and domains of appropriate/inappropriate use
Trust
Developing technical mechanisms for users to know when content is AI-generated (watermarking); investing in research on societal risks; deploying AI to help address society’s greatest challenges
8.2 Frontier Model Forum
Founded by Anthropic, Google, Microsoft, and OpenAI (July 2023), the Frontier Model Forum facilitates AI safety research and best practice sharing among leading AI developers. Expanded membership includes additional companies. Key outputs include red teaming standards and safety benchmark sharing.