Overview
Founded in the early 2010s, this global FinTech company delivers payment infrastructure and financial services to businesses worldwide. As AI adoption accelerated across the business, hundreds of AI agents, copilots, models, and autonomous workflows became part of everyday operations.
The Security organization needed a scalable way to govern enterprise AI without slowing adoption.
The Challenge
As AI adoption accelerated, new AI agents, copilots, and AI-powered workflows appeared across the organization - often without Security being involved upfront.
Security could identify AI activity, but every governance review still required manually tracing ownership, data access, connected models, and business purpose before deciding whether an AI agent could be approved.
Security teams needed answers to questions like:
- Which AI agents accessed sensitive customer or financial data?
- Who owned each AI agent and what business purpose did it serve?
- Which models, vendors, and MCP servers were involved?
- How did AI agents interact with other agents and business systems?
- Which AI required immediate review, and which could be safely approved?
Answering those questions meant jumping between multiple tools, documentation, and stakeholders before every governance decision.
Why the Fintech Company Chose Mine
The company evaluated several AI discovery solutions but found they all answered the same question:
What AI exists?
None helped Security understand which AI should be investigated first, which could be approved, or how risk propagated across connected AI systems.
Mine combined AI discovery with governance intelligence, allowing Security to evaluate AI using ownership, sensitive data access, policy compliance, vendor relationships, and agent lineage - not technical discovery alone.
The Security team could finally answer questions that discovery alone couldn't.
The platform enabled the team to:
- Identify AI accessing regulated financial data
- Detect unapproved AI vendors and models
- Trace AI execution across connected agents and MCP servers
- Prioritize governance using business risk - not discovery volume
- Trigger assessments and remediation automatically
- Continuously monitor posture as AI environments changed
Solution & Implementation
Mine partnered with the company's Security, GRC, and AI Governance teams to integrate AI governance into existing security operations.
Rather than introducing another dashboard or review process, Mine automatically connected every discovered AI asset with the business and governance information Security needed to assess it. As new AI agents appeared or existing ones changed, governance records stayed current, allowing Security teams to review AI using up-to-date context instead of manually collecting information from multiple sources.
Throughout the deployment, Mine's Security specialists and Customer Success team worked closely with stakeholders to refine governance policies, optimize workflows, and provide ongoing strategic guidance and 24/7 support.
The implementation enabled the company to:
- Review every AI asset with ownership, data access, and business context already available
- Prioritize investigations based on business risk rather than discovery alone
- Understand how AI agents, models, and MCP servers interacted across the organization
- Keep governance records aligned as AI environments evolved
- Trigger governance workflows for high-risk AI automatically
“The hardest part wasn't discovering AI — it was understanding whether it actually introduced risk. Mine helps us identify which AI assets need attention, without spending hours manually tracing their access, ownership, and connections. Our team can focus on investigating real risks instead of starting every review from scratch.”
Senior Manager, AI Security
The Results:
AI Adoption Without Security Blind Spots
Security teams no longer started every review from scratch.
When a new AI asset was discovered, ownership, business purpose, data access, connected systems, policy status, and lineage were already available. Instead of spending time gathering information, analysts could immediately assess risk, investigate exceptions, and make governance decisions faster.
Leadership also gained a continuously updated view of AI adoption across the organization, making it easier to understand where new risk was emerging.
Business outcomes:
- 300+ AI assets continuously governed
- High-risk AI surfaced automatically for review
- Faster governance decisions with business context available from discovery
- Complete lineage visibility across connected AI workflows
- Continuous oversight as AI environments evolved
- Greater confidence in enterprise AI adoption
Conclusion
As the business continued to grow, governance no longer depended on teams remembering to involve Privacy or manually coordinating every new initiative.
By partnering with Mine, the company embedded governance into the way work already happened. New products, vendors, AI initiatives, and internal projects automatically entered the governance process with the context Privacy needed to begin reviewing them. As initiatives evolved, assessments remained current, reducing rework and enabling faster, more informed decisions.
Today, the Privacy team spends less time tracking down information and coordinating stakeholders, and more time helping the business move forward with confidence. Governance has become a scalable operational capability that supports innovation rather than slowing it down.
