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Key takeaways
- The White House Accord on Super Intelligence establishes a voluntary framework for AI governance, controls, evaluations and oversight that could influence expectations across the entire AI value chain.
- Demonstrable AI trustworthiness can become a competitive advantage. Providers that substantiate their safety, engineering and control practices may reduce friction with buyers, gain access to more consequential workloads and strengthen preferred-supplier relationships.
- AI governance, controls and assurance should be tailored to risk. The appropriate level of oversight depends on what an AI system is capable of doing and the potential consequences of its failure or misuse.
- Building trust in AI requires continuous monitoring and credible evidence. Organizations should design operating environments that generate evidence of effective safeguards as systems function, making that evidence useful to customers, boards and other stakeholders.
The Trump administration convened six AI industry leaders on September 29, 2026 to address the risks associated with increasingly powerful frontier models. What emerged is a one-page framework, White House Accord on Super Intelligence, Joint Commitment on Frontier Responsibilities. The accord establishes a four-layer system of controls, evaluations, and audits designed to help make frontier AI models operate as intended and to enable faster identification and mitigation of safety issues.
Although voluntary, the agreement is noteworthy because six major tech companies have endorsed a familiar regulatory approach from the financial reporting context—establish internal controls, subject them to independent testing, and provide board-level oversight. The agreement is already influencing new expectations across the AI value chain—and its impact will be felt beyond those six signatories.
The accord arrives at an important moment in AI’s development. Providers continue to compete on model capability, speed, cost, and increasingly sophisticated applications. But as AI becomes embedded in sensitive business processes, enterprise customers have growing reasons to scrutinize how the technology is developed, governed, and operated. They need credible evidence that providers have appropriate safeguards and can respond when something goes wrong.
Despite its narrow scope, the accord could influence expectations across the AI value chain. The accord’s principles apply to providers of general-purpose models and the platforms that enable access, as well as enterprises that deploy models and agents as part of their own products and services. Those that can demonstrate how they manage AI risks may reduce friction with buyers, gain access to more consequential workloads, and strengthen preferred-supplier relationships.
For all organizations across the AI ecosystem, the opportunity is to help shape these emerging expectations while making demonstrable trustworthiness a competitive advantage.
How does the White House AI accord differ from SOX?
While sometimes referred to as SOX for AI—the accord borrows a similar accountability architecture—management controls, independent internal challenge, external evaluation, and board oversight, the implications are wider and different. The comparison is useful, but a number of significant factors unique to this ecosystem will shape how expectations for AI governance and assurance evolve.
First, the accord is voluntary. Second, it leaves substantial questions unanswered about implementation. It doesn’t prescribe a comprehensive control framework, standards for internal or external control testing, or standards for evaluating models. Nor does it establish a uniform reporting format, cadence, or explicit requirement for a publicly available independent assurance opinion.
SOX, by contrast, operates within an established financial reporting and capital markets ecosystem with defined responsibilities, reporting requirements, and assurance expectations. The accord doesn’t currently provide comparable machinery and notably doesn’t address much of the ecosystem at play. How much specificity will emerge through subsequent government action, industry standards, or market practice remains uncertain.
Rather than waiting for more prescriptive requirements, now is the time for AI providers and other ecosystem participants to take the lead in defining how trustworthiness is demonstrated. Organizations should establish what credible evidence looks like—and what will give customers, boards, and other stakeholders confidence that AI is being developed, deployed, and operated responsibly. Over time, convergence may occur, but in early forms, it allows for risk-based, tailored approaches that reflect the unique role of an organization and its own AI strategy.
Distinct roles, shared opportunity
For AI providers, the opportunity is to make demonstrable trustworthiness a competitive advantage.
For enterprise deployers, it’s to establish clearer expectations for the safeguards, transparency, and evidence they require from technology partners. Both have a role in shaping how trust is demonstrated across the AI ecosystem.
The accord’s most consequential contribution may be the opportunity it creates for the market to establish expectations for credible, decision-useful evidence of AI governance, safety, and security.
Here we outline six actions that can help organizations prepare for—and potentially influence—those expectations.
1. Make trust part of the product
Technical capability remains essential to AI competition. But as enterprise adoption expands, model providers face the additional challenge of demonstrating that their technology can be deployed and operated with confidence.
Enterprise buyers need credible answers about how providers protect sensitive data, understand model limitations, monitor deployed systems, and respond to incidents. They also need clarity about accountability and independent evaluation. Providers that can supply reliable evidence of these practices may reduce the effort customers spend assessing risk, making their technology easier to approve, deploy, and expand.
When providers offer comparable technology, those that can substantiate their safety, engineering, and control practices may have a competitive advantage. That differentiation could become increasingly important as the cost of switching between models decreases.
Earlier waves of technology often prioritized capability, leaving security, privacy, resilience, and governance to catch up. AI providers have an opportunity to begin to address these considerations from the outset, incorporating demonstrable trustworthiness into product development and go-to-market strategy.
2. Build trust across the AI value chain
The accord is framed around companies training and deploying frontier models. Its influence, however, reaches a much broader ecosystem.
AI depends on an interconnected network of semiconductor manufacturers, cloud and infrastructure providers, model developers, application providers, energy providers, and other technology and service organizations. Enterprise customers ultimately depend on multiple participants to deliver AI systems that operate reliably and securely.
Although only six organizations signed the accord, AI providers and deployers more broadly may face pressure to adopt comparable practices as customers begin to expect greater transparency and evidence of AI governance. Organizations outside the frontier-model category may encounter similar demands as well as customers seek confidence in the entire chain of technology and services supporting their AI deployments.
There’s a precedent for this downstream influence. SOX helped shape expectations beyond publicly traded companies, including the use of SOC 1 reports by service organizations whose controls affect their customers’ internal control over financial reporting. AI could produce an even broader effect because of the ecosystem’s complexity and interconnectedness.
AI providers and deployers more broadly may face pressure to adopt comparable practices as customers begin to expect greater transparency and evidence of AI governance.
An enterprise deploying an AI application, for example, may depend on a foundation model, cloud platform, third-party data services, and software and security providers. Its ability to assess risk depends partly on the evidence each participant can provide about its responsibilities and safeguards. Similar considerations apply to applications built on open-weight models, where responsibility for deployment and ongoing operation may be distributed differently.
Organizations across the value chain should clarify their accountability and related responsibilities, identify risks that depend on other providers, and determine what evidence they need to demonstrate trustworthiness. Engaging with customers, suppliers, and other ecosystem participants can help establish shared expectations for governance, transparency, and assurance.
Two sides of the trust equation
AI providers: Clarify which safeguards and controls you own, where you depend on other ecosystem participants, and what evidence you can provide to customers.
Enterprise deployers: Understand where accountability sits across your AI technology stack, identify gaps in third-party oversight, and establish expectations for the evidence you need from providers.
3. Match controls and assurance to risk
Organizations should tailor governance, controls, evaluation, and assurance to the risks associated with different AI technologies and use cases.
A uniform approach would be difficult to justify. A constrained component with limited access and autonomy presents a different risk profile from a highly capable system connected to sensitive data and authorized to act within a critical business process.
The appropriate level of oversight depends on two factors—what an AI system is capable of doing and the potential consequences of its failure or misuse. Together, these dimensions can help your enterprise calibrate its controls and assurance activities.
This framework provides a more useful basis for determining safeguards than treating “frontier” as a simple yes-or-no classification. A narrow model operating within a high-consequence healthcare, financial, or industrial process may require rigorous controls. A broadly capable model in a tightly sandboxed environment may warrant extensive technical evaluation even when the immediate consequences of failure are limited.
NIST’s AI Risk Management Framework supports a context-sensitive approach to managing risks throughout the AI life cycle. As capability and potential consequence increase, your organization should consider more rigorous controls, evidence, and independent challenge.
Applying this framework also requires distinguishing among three risk objectives.
- Enterprise trustworthiness: Whether customers and stakeholders can rely on technology to operate within expected business, security, privacy, and risk management objectives.
- High-consequence safety: Whether failures, misuse, or loss of control could significantly harm an organization, its customers, or other stakeholders.
- Catastrophic safety: Whether sufficiently capable technology could enable or cause harm at significant societal scale.
These categories clarify risk management objectives rather than establish a rigid classification system. A specialized chip provider, narrow-model developer, enterprise application provider, and frontier-model developer may have very different responsibilities depending on their capabilities, roles, and potential consequences.
The assurance objective should follow the risk management objective. For lower-risk AI systems, this typically involves demonstrating reliable operation within defined boundaries. But for higher-consequence systems, assurance may require stronger evidence of the ability to detect, contain, and recover from dangerous behavior. Systems capable of catastrophic harm may warrant substantially more rigorous evaluation, containment, and independent challenge. The form of transparency reporting is an outcome, not an objective.
Organizations should also distinguish between evaluating model behavior and providing assurance over governance and controls. Both contribute to confidence in AI systems but they address different questions and may require different methods and expertise.
Begin by identifying the failures and misuse scenarios that your controls and model evaluations need to address. Use those risks to determine the appropriate level of testing, monitoring, independent evaluation, transparency, and assurance.
Applying a risk-based approach
AI providers: Calibrate safeguards, testing, and independent evaluation to the capabilities of your technology and the potential consequences of failure or misuse.
Enterprise deployers: Evaluate AI systems in the context of how they’ll be used, recognizing that even narrowly capable technologies can introduce significant risks in high-consequence business processes.
4. Implement an operating model for continuous trust
The accord’s accountability architecture provides a useful starting point, but both AI providers and enterprise deployers need an operating model that governs AI throughout its life cycle and sets responsibilities reflecting their roles. That model should function at two complementary levels—the enterprise-wide environment for governing AI and the controls applied to individual technologies, models, and systems.
Enterprise trust environment. Your leadership teams need a consistent approach to AI governance and risk management across products and use cases. This includes clear governance, accountability, risk appetite, management monitoring, escalation, incident response, third-party responsibilities, independent challenge, and board oversight. These capabilities should extend to new AI technologies as they’re developed and deployed.
Technology, model, and system controls. At the technology level, you need safeguards and monitoring capabilities tailored to the capabilities and risks of particular models and systems.
Depending on the use case, these may include model evaluations, security controls, data protection, red teaming, behavior and drift monitoring, deployment gates, access restrictions, change management, containment, rollback, and human intervention. The particular design of each of these controls should address identified risks and evolve alongside changes in technology and emerging threats.
Defense in depth is particularly important for high-consequence AI. Because individual safeguards can fail, organizations need overlapping controls that enable them to detect problems, limit their impact, intervene promptly, and recover safely.
A two-level architecture for trust
Enterprise trust environment |
Governance, accountability, risk appetite, monitoring, independent challenge, incident management, third-party management, escalation, and board oversight.
Key question: Does my organization have the capabilities and controls to develop and operate trustworthy AI |
Technology, model, system controls |
Evaluation, red teaming, data and access controls, security, drift and behavior monitoring, deployment gates, change control, containment, rollback, and intervention.
Key question: Is this technology operating within its intended boundaries? |
The two levels should reinforce each other. Enterprise governance establishes responsibilities and expectations, while tech-specific controls address risks associated with individual systems. Information about performance, exceptions, and incidents should flow between them, providing the foundation for credible evidence of how AI is governed and operated.
5. Make trust observable through persistent evidence
A well-designed operating model provides the foundation for trust in AI. The next challenge is demonstrating that safeguards remain effective as models, data, permissions, and threats change.
AI systems can increasingly generate evidence of their own operation, including model evaluations, security events, anomalous behavior, configuration changes, and interventions. Organizations can use this information to identify emerging problems, support independent evaluation, and give stakeholders greater confidence in how their technology is governed.
Design evidence into operations. Traditional compliance and assurance processes often rely on periodic testing, sample-based testing, and evidence assembled after the fact. These approaches may continue to play a role, but the pace of change in AI environments creates a need for more timely information.
Wherever possible, design engineering and operating environments to generate relevant evidence as systems function, including telemetry, evaluations, exceptions, interventions, and remediation status.
The emphasis should be on evidence that addresses meaningful risks. Determine which indicators demonstrate that safeguards are effective, how evidence will be validated, and how exceptions will be escalated.
Persistent evidence can also make independent evaluation more effective. Evaluators may be able to examine changes, exceptions, and patterns over time rather than reconstructing the control environment at periodic intervals. This can support more timely oversight without requiring continuous issuance of formal assurance opinions.
Make evidence useful to stakeholders. Evidence becomes commercially valuable when customers can use it to inform purchasing and deployment decisions. Providers should make relevant information accessible while protecting intellectual property, sensitive data, and security controls.
A graduated approach can help.
- Public layer: Make the governance approach, accountability model, meaningful trust indicators, and appropriate incident information visible enough to establish a credible baseline.
- Customer layer: Provide deeper, decision-useful evidence to enterprise procurement, security, and vendor-risk teams when they’re deciding whether to select, deploy, or expand the technology.
- Governance and assurance layer: Make detailed telemetry, testing results, exceptions, and remediation information available to boards, independent evaluators, and appropriate regulators, subject to relevant access and confidentiality requirements.
These layers should draw from a consistent body of evidence, with different levels of detail according to stakeholder needs.
SOC reports, certifications and periodic assurance reports may continue to play an important role. But their value will depend on whether they address the risks that matter to stakeholders and remain connected to how the technology operates.
Providers that make credible evidence more accessible, current, and useful may reduce the burden on customers to reconstruct their governance and control environment during procurement and oversight.
Making trust tangible for customers
AI providers: Make relevant evidence of governance, safeguards, and system performance accessible to customers without compromising sensitive information.
Enterprise deployers: Define what evidence you need to evaluate providers, support procurement decisions, and monitor risks as AI systems evolve.
6. Put demonstrable trust to the test
AI providers in particular can begin strengthening how they demonstrate AI trustworthiness without waiting for comprehensive standards.
Consider an open-door test. If a major customer could examine how your organization develops, operates, and governs AI, would what they see increase their confidence in your technology?
The assessment should extend beyond policies, presentations, and general commitments. The evidence required will depend on the objective. Is it reliable enterprise operation, protection against high-consequence failures, catastrophic safety, or some combination of these?
Five questions can help organizations assess their readiness.
- Accountability: Can we demonstrate who owns AI risk and how significant decisions and issues are escalated?
- Safeguards: Can we show that our safeguards operate effectively?
- Monitoring: Can we demonstrate how we know when technology moves outside intended boundaries?
- Intervention and recovery: Can we show how our organization contains problems, intervenes and recovers when safeguards fail?
- Credible evidence: Can we substantiate our claims with reliable evidence, including appropriate independent evaluation or assurance?
The answers can reveal gaps in governance, controls, monitoring, and transparency. Use them to prioritize improvements based on the capabilities and consequences associated with their technologies, incorporating these practices into product development, customer relationships, and ongoing operations.
How can AI governance support competitive advantage?
The accord offers a starting point for a broader conversation about how AI providers demonstrate trustworthiness. As expectations evolve, organizations across the AI value chain have an opportunity to help shape the standards and assurance practices that enterprise customers will likely increasingly expect.
By acting early, you can strengthen your position by building appropriate safeguards, calibrating controls to capability and consequence, and generating credible evidence of how your technology operates. Making that evidence accessible to customers and other stakeholders can reduce barriers to adoption, support preferred-supplier relationships, and help earn the confidence needed to deploy AI in more consequential settings.
The opportunity is to make demonstrable trustworthiness an integral part of your product and its value proposition.
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