Secure AI for financial services

Artificial intelligence (AI) has significant potential across financial services, but its value depends on whether organizations can use it without weakening the controls surrounding sensitive information. Investment banks, private equity firms, corporate development teams and other financial institutions routinely work with confidential financial statements, transaction documents, customer information and strategic data.

That makes secure AI more than a cybersecurity consideration. It is an information governance challenge.

Organizations need to understand what information AI can access, where that data is processed, whether existing permissions remain intact and how outputs can be traced back to their sources. The objective should be to gain the speed and analytical benefits of AI while maintaining the security architecture required for high-stakes financial workflows.

Build AI around existing information controls

Financial services organizations already invest heavily in controlling access to sensitive information. AI should extend those controls rather than create an alternative route around them.

This means evaluating AI applications based on how they interact with existing permissions, document controls and audit requirements. If an employee does not have permission to access a document, an AI assistant should not provide information derived from that document.

Traceability is equally important. Generative AI can summarize information and answer complex questions quickly, but financial professionals need the ability to investigate important outputs. Connecting answers to underlying source material makes it easier to verify information before using it in diligence or decision-making.

These principles form the foundation of a secure deal management platform. AI should operate within the same governed environment as sensitive financial information rather than forcing teams to create disconnected workflows.

Bring AI to sensitive financial data

Another consideration is how information moves between systems.

Uploading confidential documents into separate AI applications can create additional copies of sensitive information and introduce new governance questions. Financial institutions should consider architectures that allow approved AI applications to interact with information without requiring employees to repeatedly download, transfer and re-upload documents.

This approach effectively brings AI to the data rather than moving the data to AI.

It can also support a broader AI strategy. Different financial workflows may benefit from different specialized or general-purpose AI applications. Organizations should be able to adopt those tools while maintaining consistent permissions, governance and auditability around the underlying information.

This model is central to secure AI connectivity for dealmaking, where external AI applications can work with permissioned deal information while the transaction platform remains the system governing access.

Apply secure AI with DealCentre AI

SS&C Intralinks DealCentre AI™ brings AI-powered intelligence into a secure environment designed for strategic financial transactions.

Its proprietary AI engine, Link, supports workflows including document summarization, critical data extraction, sensitive-information identification and Q&A. Ask Link allows authorized users to ask natural-language questions across applicable deal documents and receive responses with references to the underlying sources. Ask Link only uses documents that the user currently has permission to view.

DealCentre Connect extends this model to external AI applications. The DealCentre Connect Secure Gateway is designed to allow compatible AI tools to work with live deal data without requiring teams to export sensitive documents. Existing data room permissions apply to AI access and outputs, while governance and auditability remain part of the workflow.

This architecture addresses an important challenge for financial institutions: AI adoption does not need to mean abandoning established information controls.

Secure AI for financial services should ultimately be measured by more than model performance. Financial institutions need intelligence that operates within defined permissions, preserves control over sensitive information, provides traceability and supports human verification. When AI is incorporated into the existing security and governance architecture, organizations can pursue automation and faster analysis without separating innovation from the controls required for sensitive financial work.