Gulf banks push AI with guardrails: data control and regulatory governance take center stage

Gulf banks are accelerating generative AI adoption but face a core tension: how to use the technology without exposing sensitive customer data. Executives and vendors are pushing data governance frameworks, auditable disclosure controls, and regulatory compliance as prerequisites for deployment. For AI builders targeting financial services, the lesson is clear: trust and data control are the product, not just the model.

What happened

Gulf banks are exploring AI tools that can speed up routine tasks, analyze documents, and improve productivity, but many are still trying to answer a basic question: how can they use AI without putting sensitive customer information at risk? Najla Ibrahim Al-Mutawa, Executive Vice President of Strategy and Business Development at QNB, argues that AI deployment must protect trust, safeguard data, and meet regulatory expectations.

Sami Mian, CEO of Blade Labs, notes that banks are comfortable with AI systems themselves but remain concerned about what information those systems can access. "The AI tool may be approved. The cloud may be approved. But the bank still needs to control what the AI is allowed to see." Blade Labs has developed a platform called ZeroH Disclosure, which aims to automatically limit the information shared with AI systems while keeping a record of what data was disclosed and why.

The same thinking is being applied in Islamic finance, where product approvals involve legal teams, compliance departments, auditors, and Shariah scholars. Blade Labs is also developing Ask Ali, an AI assistant focused on Islamic finance to help professionals research standards, review documents, and navigate Shariah-related questions while maintaining human oversight.

Regulators in the Gulf are pushing digital transformation while also strengthening rules around data protection, cybersecurity, and AI governance.

Why AI builders should care

For AI builders, the Gulf banking sector represents a high-stakes proving ground for data governance. AI and digital transformation adviser Alina Timofeeva says "in banking, trust is the product". The question moves from where data is stored to who can access it, how it is used, and who is accountable if something goes wrong.

Banks are becoming more selective about how they use AI. Low-risk experimentation is treated differently from applications involving customer data, confidential internal information, financial crime controls, risk models, and proprietary business information. This creates a clear market signal: AI tools that embed data-control into the workflow, rather than relying on manual sanitization, will unlock broader adoption.

Practical implications

Rather than relying on staff to manually remove sensitive details from documents, controls can be built directly into the process, allowing only authorized information to be disclosed while creating an audit trail of what was shared. This platform-based approach reduces exposure and increases accountability.

Mian predicts that institutions that solve data-control first will be able to use AI more freely, while those that cannot prove control will remain stuck in pilots, restrictions, and internal approvals. For product teams building AI for regulated industries, this means investing in auditable data access layers and policy engines is not optional.

Caveats

The planning and execution details of ZeroH Disclosure and Ask Ali are described at a high level in the source; specific technical implementations, timelines, and deployment scales are not provided. Regulatory expectations may vary by jurisdiction and are evolving, so real-world deployments will depend on local specifics and vendor capabilities.