SEPANTA Launches GATE to Bridge the AI Governance Gap in Financial Services

Sepanta AI has launched GATE, an AI-governance operating system designed to bring regulated firms into compliance with frameworks like those set by the Financial Conduct Authority. By embedding oversight directly into the technology, the platform addresses a critical gap where financial institutions often deploy AI models without fully understanding their underlying decision-making processes.

What was announced

GATE is designed as a system of rules, processes, and frameworks to guide AI usage within highly regulated environments. The product launch follows research from the Bank of England indicating a significant knowledge gap in the sector; while 84% of UK financial firms have appointed an individual accountable for AI, 46% of these firms only partially understand the AI tools they currently employ. This discrepancy highlights a growing need for governance that is integrated into the technology rather than treated as a secondary administrative task.

The research also identified that four of the five primary risks associated with AI in finance involve data—specifically privacy, protection, quality, security, and bias. GATE functions as a "gate" rather than a retrospective reporting tool. It provides a permanent, unalterable record for every AI-assisted decision, documenting who approved the action, which specific policy was applied, and what evidence supported the choice. The system is built to check controls continuously, flagging any "drift" from approved policies in real-time. This level of human oversight is increasingly necessary as 55% of AI use cases in financial services now involve some degree of automated decision-making. By governing AI before it acts, the system aims to ensure transparency and accountability across the entire lifecycle of a model, aligning with standards set by the Financial Conduct Authority (FCA).

"AI adoption is growing rapidly in enterprises but CEOs need to ask who or what is governing the AI? AI governance cannot be something organisations bolt on after deploying a model."

Omid Pakseresht

The companies involved

Sepanta AI is a technology provider focused on creating what it describes as a human-first AI operating system. The company’s core mission involves building scalable systems that deliver measurable business growth through advanced automation and intelligence. Led by CEO Omid Pakseresht, the firm positions its technology as a foundational layer for enterprises, particularly those operating within the strict boundaries of regulated industries like financial services.

The company operates on the principle that AI governance must be built into the technology from the outset, rather than being "bolted on" after a model has been deployed. This approach is intended to bridge the gap between rapid technological adoption and the stringent requirements of regulatory bodies such as the Financial Conduct Authority. By providing tools that manage the lifecycle of AI deployments, the company seeks to mitigate the risks of bias and security failures that often accompany large-scale automated systems. Sepanta AI utilizes its own GATE product to oversee its internal delivery and development processes, ensuring that all AI-assisted decisions within the company are recorded and checked against established policies before they are executed.

What this means

The launch of GATE signals a shift in the fintech sector from experimental AI deployment toward "compliance by design." As the Bank of England and the FCA increase their scrutiny of automated systems, the era of "black box" AI in finance is likely coming to an end. Firms that cannot provide a clear audit trail for automated decisions will face mounting regulatory pressure and potential reputational damage. This announcement places traditional consultancy-led governance under pressure, suggesting that manual oversight is no longer sufficient for the speed of modern AI. The central question for the industry remains whether software-based governance can truly keep pace with the evolving complexity of generative models.