From Risk to ROI: How to Extract the Most Value Out of Your Confidential Data

An organization’s most valuable data is often its most sensitive and confidential data. Paradoxically, it is often the least used data as well. This is because legal, regulatory, and compliance constraints around confidential government records make it extremely difficult to share and extract value from this data, even when doing so can clearly benefit the public.

In this blog, we explore a technique that enables you to derive insights, cut through red tape, and achieve your goals in days rather than months when working with confidential data while ensuring full compliance with operational policies. By the end, we hope you’ll see the potential of this approach and how it can unlock value within your organization.

The Setup: A Fictional Case Study

To understand the real-world challenges and opportunities of working with confidential data, let’s consider a fictional case study involving the Internal Revenue Service (IRS) and (Department of Housing and Urban Development) HUD, two federal agencies with access to extensive, sensitive information about citizens’ income, housing, and financial activity.

They’ve realized they can improve fraud detection and program targeting if they can cross-reference each other’s datasets. For instance:

• HUD could benefit from knowing if a housing subsidy applicant has under reported income on their tax filings.
• IRS could use HUD’s data to flag inconsistencies in reported residency or address-related claims.

Each agency holds valuable insights that the other could use, but concerns around security, privacy, and regulatory compliance, (like the Privacy Act, FISMA, and other federal data protection standards), prevent them from sharing data directly.

In addition, IRS and HUD operate under different legal mandates and data governance structures. While both are part of the federal government, there are strict limitations on inter-agency data use, especially when sensitive personal information is involved. This creates institutional and legal risk even when collaboration could lead to better public outcomes.

This scenario highlights a common problem across government sectors: agencies are unable to fully unlock the value of their most sensitive data, even when collaboration could lead to better services, reduced fraud, and improved decision-making.

Traditional Solutions and Challenges

Let’s explore the options that agencies like IRS and HUD have traditionally used to solve this data collaboration problem while remaining compliant with strict legal, regulatory, and privacy requirements.

Solution #1: Data Anonymization

A common approach is to anonymize or mask sensitive data before sharing it. This involves removing personally identifiable information (PII) and other sensitive attributes to reduce risk.

While this sounds effective in theory, it rarely holds up in practice. Anonymization is expensive, time-consuming, and often unreliable because it typically requires manual data review, complex tooling, and ongoing validation to maintain privacy standards. Sophisticated methods can still re-identify individuals by correlating anonymized datasets, creating legal and compliance exposure. For regulated sectors like federal agencies, defense, public health, and social services, this risk is often unacceptable.

Solution #2: Synthetic Data

Another option is to use synthetic data that simulate real-world data patterns. This can reduce privacy risk, but it comes at the cost of accuracy. In use cases like fraud detection or eligibility verification, where subtle anomalies and real-world variability are crucial, synthetic data often fails to provide the fidelity needed to produce meaningful results.

Solution #3: Secure Data APIs

Some agencies try to collaborate through tightly controlled APIs or secure data environments. While these can help limit exposure, they often introduce new challenges around governance, oversight, and liability. Managing who has access to what data and under what conditions becomes a significant operational burden.

These traditional solutions share a common flaw: they increase the complexity, cost, and duration of data collaboration projects. Legal reviews, compliance audits, and risk mitigation strategies balloon project timelines and budgets. As a result, initiatives can only move forward when the expected value is substantial enough to justify the time and cost for all stakeholders involved. In many cases, that bar is simply too high so valuable opportunities remain out of reach.

A Modern Solution: Confidential Computing

What if IRS and HUD could collaborate on insights without ever exposing their raw data to one another? That is precisely what confidential computing promises. It offers a secure way for government organizations to perform computations on encrypted data, ensuring that sensitive information remains protected throughout the entire process.

Confidential computing is built around the concept of a Trusted Execution Environment (TEE). A TEE is a secure and isolated part of a processor (CPU or GPU) that ensures the confidentiality and integrity of both data and code. Data can only be decrypted and processed once it is inside the TEE, and no one, including system administrators, cloud providers, or any collaborating party, can access the information inside the TEE. The processor provides a cryptographic proof of the integrity of the environment.

In the context of our case study, this means that only code approved by IRS and HUD can access the unencrypted data to perform the necessary actions that generate public value. At the same time, it is ensured and provable with cryptographic signatures that no unauthorized person, code, or agency can access or manipulate the sensitive data. With confidential computing HUD could submit a query that is processed against IRS’s data inside a TEE. The environment returns the result of that computation, such as a fraud risk score or eligibility signal, without ever revealing the individual data points that generated the result. Likewise, IRS can analyze patterns in HUD’s dataset without ever seeing the full citizen records.

This approach provides the best of both worlds: the insight that comes from inter-agency collaboration and the assurance that no confidential data has been exposed or compromised. Unlike anonymization, which carries the risk of re-identification through cross-referencing or inference, confidential computing ensures that raw data is never exposed outside the secure enclave. This eliminates the possibility of re-identifying individuals from shared outputs, offering a fundamentally different and more robust privacy guarantee.

Confidential computing is a significant shift from traditional data-sharing methods. It eliminates the need for anonymization, data masking, or synthetic data creation. Since the data never leaves its encrypted state outside the secure environment, legal and compliance concerns are greatly reduced. Confidential computing makes it possible to share and analyze data that was previously considered too sensitive to use, enabling new forms of collaboration while still meeting strict regulatory requirements. Whether you’re in public health, taxation, defense, transportation, or any other sector where data confidentiality is critical, confidential computing provides a practical and responsible path to innovation.

Conclusion

Confidential data doesn’t have to remain untapped. With confidential computing, agencies can finally break through long-standing barriers to collaboration without sacrificing privacy, security, or compliance. As we’ve seen through the fictional case study, even highly regulated sectors can now derive actionable insights from sensitive data, accelerating public impact, and unlocking value that was previously out of reach.

By enabling secure computation on encrypted data, confidential computing offers a fundamentally new way to work with information that was once too risky to share. It replaces months of legal negotiation and technical workarounds with a faster, safer, and more scalable solution.

Now it is your turn to rethink what’s possible with your most sensitive data. If you need experts to help introduce confidential computing in your infrastructure, please reach out to us at hello@cloudwithsms.com.