Secure Third Party Data Access: Why Your Fintech Stack Is Your Biggest Breach Risk

Your fintech stack is not a set of vendors. It is a set of breach surfaces you do not control.

Every fraud detection platform, every credit decisioning engine, every customer analytics tool, every AI-powered underwriting model in your technology stack requires customer data to function. And under standard encryption, that means those platforms hold decryptable copies of your most sensitive customer information. When any one of them falls, that data falls with it.

The Snowflake Breach: A Financial Services Wake-Up Call

In May 2024, attackers used stolen credentials to access Snowflake customer environments at approximately 165 organizations. Santander Bank had 30 million customer records across three countries exposed. AT&T lost call logs for 109 million customers and paid an undisclosed ransom. The Snowflake platform itself was not breached. The attackers walked in through customer accounts using credentials harvested by infostealer malware months earlier. No zero-day exploit. No sophisticated technical attack. Stolen credentials and an absence of MFA.

That last part is important. The entry point was a credential problem. The damage was a data problem. Santander and AT&T could have had perfect MFA in place and still faced the same exposure if their data was readable at the point of access.

MFA controls who gets in. It does not control what they find when they do.

The Fintech Ecosystem Is a Data Exposure Problem

Financial services organizations have spent the last decade building a fintech ecosystem. Fraud analytics. Real-time credit decisioning. Customer behavior modeling. Regulatory reporting automation. AI-powered underwriting. Every capability that gives a financial institution competitive advantage runs on customer data that must flow to third-party platforms to function. Every one of those platforms represents a data exposure your security team does not control and your vendor agreements do not eliminate.

Secure third party data access in financial services means the data your fintech vendors process is never in a form they can lose. Not contractually; architecturally. The framework for how this works is covered in How to Share Sensitive Data Securely. The financial services application is specific and consequential.

The Regulatory Layer Is Raising the Bar

The question regulators are now asking is not just whether you assessed your vendors. It is whether you took available technical steps to ensure your vendors could not lose readable customer data. Vendor risk questionnaires and contractual indemnification answer the first question. They do not answer the second.

The cost picture for organizations that get this wrong is in How Much Does a Data Breach Cost? Financial services averaged $5.56 million per incident in 2025. The Snowflake-affected organizations paid more than averages.

What Secure Third Party Data Access Actually Looks Like

The answer is not a better vendor security questionnaire. It is not stronger contractual indemnification. Both of those address what happens after a breach. Neither prevents the exposure that makes the breach damaging in the first place. The only control that changes the outcome is ensuring the data your vendors process is never readable by them to begin with. That requires a different architecture.

The problem with standard encryption in a fintech ecosystem is structural. Every platform in the stack must decrypt customer data to process it. Donoma Seshat is a continuous encryption platform that changes that structure.

When customer data flows to a fintech vendor through a Seshat-protected environment, the vendor processes encrypted data. Their fraud models run on ciphertext. Their credit algorithms execute on encrypted records. Their analytics pipelines process data that never decrypts. The underlying customer data; account numbers, transaction histories, credit scores, behavioral patterns; never exists in readable form at the vendor.

A breach at the vendor yields nothing usable. No ransom demand has leverage. No notification obligation triggers. No class action plaintiff has damages to allege. The Snowflake breach pattern breaks entirely because the data that was accessed was never readable to begin with.

Donoma Seshat deploys at the application layer without requiring changes to your fintech vendors’ infrastructure. It runs on standard CPUs with no specialized hardware requirement. It operates at native speed so real-time fraud detection and credit decisioning run without latency penalty. And it is post-quantum ready; financial data carries long-term sensitivity that extends well beyond the current threat landscape.

The Question Worth Asking Before Your Next Vendor Review

Pull the list of fintech vendors who process your customer data. For each one, ask a single question: if their systems were accessed by an attacker with valid credentials, what would that attacker find?

If the answer is readable customer data, your vendor security program is managing the entry point. It is not managing the data exposure. The Snowflake breach confirmed that managing only the entry point is not enough.

The technology is ready. The business case is clear. The time for action is now.

If you want to see how Donoma Seshat protects customer data across your fintech vendor ecosystem, at native speed, on your existing infrastructure, without specialized hardware, book a solution briefing with the Donoma team.

Frequently Asked Questions

What is secure third party data access and why does it matter in financial services?

Secure third party data access means enabling fintech vendors, analytics platforms, and other third parties to process your customer data without ever holding a decryptable copy of it. It matters in financial services because every fraud detection platform, credit decisioning engine, customer analytics tool, and AI underwriting model requires customer data to function. Under standard encryption, each of those vendors holds readable customer data. A breach at any one of them exposes that data regardless of how secure your own systems are.

What did the Snowflake breach reveal about financial services vendor risk?

The Snowflake breach demonstrated that vendor ecosystem risk is not theoretical. Approximately 165 organizations had customer data exposed when attackers used stolen credentials to access their Snowflake environments. Santander Bank lost 30 million customer records across three countries. AT&T lost call logs for 109 million customers. Snowflake’s own platform was not compromised. The data was exposed because it was readable at the point of access. Better access controls at the vendor would have reduced the entry point. Continuous encryption would have eliminated the damage.

How does MFA differ from data-layer protection in vendor risk management?

MFA controls who can authenticate to a vendor system. It does not control what an authenticated user finds when they get in. An attacker with stolen credentials who clears MFA is an authenticated user. Under standard encryption, that authenticated attacker finds readable customer data. Continuous encryption changes what they find: ciphertext with no operational value. MFA and continuous encryption address different problems. Both are necessary. MFA alone is not sufficient.

What regulatory requirements govern financial institutions’ third-party data sharing?

The SEC’s cybersecurity disclosure rules require material breach disclosure within four business days of determining materiality. GLBA’s Safeguards Rule requires financial institutions to assess third-party service provider risk and implement appropriate controls. OCC and FDIC guidance on third-party risk management increasingly scrutinizes data sharing practices. Regulators are now asking not just whether vendors were assessed, but whether available technical steps were taken to ensure vendors could not lose readable customer data. Continuous encryption is the technical answer to that question.

Can Seshat protect data processed by AI and machine learning platforms?

Yes. AI and machine learning workloads including fraud detection models, credit scoring algorithms, and customer behavior analytics can operate on Seshat-encrypted data without requiring decryption. Models train on encrypted records. Inference runs on encrypted inputs. Results return as encrypted outputs. The underlying customer data never exists in readable form at the AI platform. This enables financial institutions to deploy advanced analytics capabilities without creating the data exposure that typically accompanies them.