The Growing Role of AI in Business and the Need for Stronger Safeguards
In recent years, the Big Four accountancy firms have started offering audits to verify that organizations’ AI products are compliant and effective. Insurance companies have also begun providing AI liability cover to protect businesses from potential risks. These developments indicate that AI is maturing and that customer-facing use cases are becoming more common. There is also a clear desire among organizations to protect themselves amid changing regulations and concerns about their reputation.
However, audits and insurance alone cannot solve the core issues surrounding AI. While they serve as an effective safety net and provide an extra layer of protection against AI-related errors, by the time an issue is detected by auditors or an insurance claim is made, the damage may already be done. In many cases, data and infrastructure continue to hinder organizations from using AI safely and effectively, making it a challenge that must be addressed head-on.
AI Amplifying Data Issues
Large organizations handle massive amounts of highly sensitive data—ranging from payroll records and customer information to intellectual property. Maintaining oversight of this data is already a significant challenge. As AI adoption spreads across teams and departments, the associated risks become more distributed. It becomes increasingly difficult to monitor and govern where AI is being used, who is using it, what it is being used for, what it produces, and how accurate its outputs are. Losing visibility over any one of these areas can lead to serious consequences.
For example, data could be leaked through public AI models, as seen during the early stages of GenAI deployment. AI models can also end up accessing data they shouldn’t, generating outputs that are biased or influenced by information that was never meant to be used. These issues highlight the growing risks that organizations face.
Regulatory Pressure and Customer Trust
The risks for organizations are twofold. First, customers are unlikely to trust companies that cannot demonstrate that their AI systems are safe and reliable. Second, regulatory pressure is increasing. Laws like the EU AI Act are already in effect, with other regions expected to introduce similar regulations in the coming months and years. Falling short of compliance not only damages a company’s reputation but can also result in major financial penalties that could impact the entire business. For instance, the EU has the authority to impose fines of up to €35 million or 7% of an organization’s global turnover—whichever is higher—under the AI Act.
While AI liability insurance might help recover some of the financial losses from AI errors, it cannot win back lost customers. Audits may identify potential governance issues, but they cannot undo past mistakes. Without proper guardrails, organizations are essentially gambling with AI risk, introducing fragility and unnecessary complexity that distorts outcomes and erodes trust in AI-driven decisions.
Protection via Private AI
One way to protect against AI-related errors is to regain control through private AI. This approach allows organizations to build and run AI models, applications, and agents entirely within their chosen environment—whether on-premises or in the cloud—ensuring that data remains secure and contained. Private AI safeguards two critical assets: proprietary data that is unique to the business and intellectual property that gives it a competitive edge.
Open-source AI models form the foundation of private AI, meaning organizations can avoid relying on potentially risky public models and build their own trusted versions, which are trained exclusively on their data. However, for private AI to deliver accurate and trustworthy outcomes, it must be fed a complete set of proprietary data; otherwise, the results will be distorted by the subset of data used.
To make this possible, organizations need a modern data architecture underpinned by a unified data platform. This ensures that private AI has access to the full range of data it requires. It also enables consistent governance across all environments—wherever the data resides—helping organizations stay compliant as regulations evolve.
Audits and Insurance as a Backstop
The rise of AI audits and insurance cover signals that organizations are moving beyond experimentation and starting to deploy AI in real, customer-facing scenarios. It is a positive step—but with such high stakes, progress must be matched with proper oversight. Robust checks and balances are essential to ensure AI is deployed safely.
The Big Four firms and insurers can play a supporting role, but they are not responsible for delivering responsible AI—they are a backstop, not a solution. Ultimately, accountability for safe AI lies with the organizations building and using it. By putting the right data architecture in place to support private AI, businesses can strike the right balance between innovation and security.
