Mrinal Manohar is CEO of Prove AI, an AI governance solution offering certifiable, tamper-proof auditing for organizations using AI models.
California’s AI regulation SB 1047, which Governor Gavin Newsom vetoed in September 2024, has inspired renewed discussion over the appropriate role for regulators to play in ensuring AI evolves safely and securely. The proposed bill sought to introduce safety testing, built-in threat controls and third-party audits for any companies operating in California developing large-scale models costing over $100 million to develop or $10 million to fine-tune.
By focusing on model size and cost, the bill faced resistance, with Newsom suggesting that a risk-based framework focusing on the function and sensitivity of AI models rather than cost alone would better address the evolving challenges AI systems pose.
As Newsom turns to AI leaders to help shape practical AI guardrails, it’s essential to note that despite opposition from some major AI companies like OpenAI, Meta and Anthropic, it isn’t just California voters who generally continue to support the bill. Many business leaders—and even AI leaders—across the globe remain supportive of strong AI regulation.
To wit, the debate surrounding SB 1047 has reignited the global conversation about the role of regulation in shaping responsible AI practices. While California voters and some major AI companies may differ in their views, one point of agreement is a growing call for clarity.
In the summer of 2024, we commissioned a report titled, “The Essential Role of Governance in Mitigating AI Risk,” which Zogby Analytics conducted. In surveying over 600 CEOs, CIOs and CTOs of large companies across the U.S., U.K. and Germany, we found that 82.4% of these leaders support an executive order mandating AI governance strategies. This strong backing underscores a growing demand for regulatory clarity and oversight in the private sector. Rather than resisting regulation, many leaders are actively welcoming it as a way to reduce uncertainty and foster safer AI adoption.
Even in the absence of SB 1047, businesses are feeling a growing urgency to prioritize responsible AI governance—driven by the rising legal and reputational risks associated with unregulated AI development. In today’s fast-paced business landscape, uncertainty hinders progress. Without clear compliance guidelines and frameworks, the journey toward effective AI adoption remains fraught with challenges.
To shape effective future AI legislation, both the private and public sectors need to focus on three critical areas: data transparency, robust AI governance and tamper-proofing mechanisms that enable reliable oversight without stifling innovation.
Data Transparency: The Key To AI Explainability
As AI’s complexity grows and its inner workings remain somewhat enigmatic, businesses must ensure stakeholders can verify and trace AI’s decision-making processes. With longstanding concerns around the processing and use of data in AI systems, delivering transparency is a prerequisite for building trust, safeguarding brand reputation and preparing for impending regulations.
With a clear view of the data used in AI systems, businesses can ensure the explainability of AI outcomes, protect themselves against regulatory risks and build a strong foundation for consumer confidence.
Explainability has become a critical priority in preventing unethical or inaccurate AI outcomes and engendering the trust needed to support adoption. Yet we found that despite 95% of executives naming explainability as a key factor in deciding to integrate AI into their operations, only 5% of organizations have implemented comprehensive AI governance frameworks.
Governance: A Strategic Imperative For AI
For AI to reach its full potential, strong data transparency and governance must reinforce explainability, as it’s expected to significantly impact organizations’ AI adoption decisions. Achieving true AI explainability requires foundational components—a fully auditable database, tamper-proof data stores and secure third-party data access.
These elements not only support trusted, explainable AI decision-making and outcomes within organizations, but they also build confidence among customers in critical industries like healthcare, manufacturing and food safety where data validation and human oversight are essential. Despite this pressing need, explainability remains elusive due to the slow adoption of comprehensive AI governance frameworks.
A Blockchain Backbone For Tamper-Proof AI Governance
As AI regulation grows, the need for organizations to prove the “who, what, where and when” of data access is paramount. Proving compliance requires secure, tamper-proof tracking of data activity. Originally adopted in areas like supply chain and finance, blockchain has the ability to create immutable, auditable records.
Many of today’s blockchain-powered solutions offer the ease of SaaS applications, abstracting away technical complexity for seamless adoption. To effectively transition, organizations should assess compliance needs and establish clear access controls through smart contracts. Once implemented, blockchain can enable automated enforcement of compliance standards, freeing teams to focus on innovation rather than regulation.
Transparency is not only a regulatory necessity—it’s a competitive advantage. As the private and public sectors shape the future of responsible AI, blockchain offers a powerful foundation for transparency, accountability and control. With these capabilities in place, organizations can turn AI from a compliance risk into a driver of sustainable growth and innovation.
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