From Risk to ROI: How to extract the most value out of your confidential data

A company’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 intellectual property make it extremely difficult to share and extract value from this data, even when doing so clearly benefits the business.

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.

To understand the real-world challenges and opportunities of working with confidential data, let’s consider a fictional case study. Imagine two companies in the insurance industry:

• Insurance Provider A (IPA) specializes in home loans
• Insurance Provider B (IPB) specializes in car loans

Let’s assume both IPA and IPB have each built up rich, proprietary data on their customers’ behavior purchase patterns, claims history, credit behavior, and more. They’ve realized they can improve their fraud detection algorithms if they can cross-reference each other’s datasets. For instance:

• IPA could benefit from knowing if a car loan customer from IPB has a history of making suspicious claims.
• IPB could use IPA’s insights to flag risky customers applying for car insurance.

Each provider holds valuable insights that the other could use but concerns around security, privacy, and regulatory compliance (like GDPR, HIPAA, and industry-specific policies) prevent them from sharing data directly.

In addition, IPA and IPB are potential competitors. While their current offerings differ, both operate in the broader financial services space and may expand into overlapping markets. This competitive dynamic increases the risk of sharing strategic data, even when collaboration could be mutually beneficial.

This scenario highlights a common problem across industries: organizations are unable to fully unlock the value of their most sensitive data, even when collaboration could lead to better outcomes for everyone involved.

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

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 industries like insurance, nuclear energy, healthcare, biotech, pharmaceuticals, and finance, this risk is often unacceptable.

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, where subtle anomalies and real-world variability are crucial, synthetic data often fails to provide the fidelity needed to produce meaningful results.

Some organizations 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 business value is substantial enough to justify the time and cost for both parties involved. In many cases, that bar is simply too high so valuable opportunities remain out of reach.

What if IPA and IPB 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 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 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 insurance scenario, this means that only code approved by IPA and IPB can access the unencrypted data to perform the necessary actions that generate business value. At the same time, it is ensured and provable with cryptographic signatures that no unauthorized person, code, or organization can access or manipulate the sensitive data. With confidential computing IPB could submit a query that is processed against IPA’s data inside a TEE. The environment returns the result of that computation, such as a risk score or fraud indicator, without ever revealing the individual data points that generated the result. Likewise, IPA can analyze patterns in IPB’s dataset without ever seeing the full customer records.

This approach provides the best of both worlds: the insight that comes from 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 insurance, energy, healthcare, finance, or any other industry where data confidentiality is critical, confidential computing provides a practical and responsible path to innovation.

Confidential data doesn’t have to remain untapped. With confidential computing, organizations 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 industries can now derive actionable insights from sensitive data, accelerating innovation 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.

Platforms like Opaque Systems have developed useful tooling that leverage confidential computing to enable secure data collaboration easily. We will discuss more about how you can get started with using Confidential Computing in our future blogs, stay tuned.

For 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.