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Modern enterprises are collecting more sensitive data than ever, but much of it sits idle. Fears of misuse, compliance violations, and reputational harm often prevent companies from acting on their most valuable insights, leaving potential gold to decay into liability.
Jyothsna Santosh is a cross-industry AI and Data Science leader who has delivered measurable impact in regulated industries like healthcare at CVS Health and finance at Regions Bank. Her work focuses on building human-centered, trusted AI systems to drive growth and personalization. She spoke with CIO News about strategies leaders can use to reframe the problem, turning their riskiest data from a regulatory burden into a governed asset.
For Santosh, a key part of the solution lies in a well-architected digital twin that serves as a governed replica of a company’s most sensitive enterprise data. By tokenizing customer records and supplementing them with statistically valid synthetic data, the twin creates a safe but realistic mirror that organizations can use with confidence. "By building and using a digital twin, I’m able to go to market faster while keeping customer data safe."
The whole elephant: But she cautioned that the value is often lost in fragmented efforts. Many organizations try to build a digital twin only for a single domain, without connecting it to the rest of the enterprise. The result is an incomplete picture that keeps insights siloed. "If you're only looking at one part of the elephant, you miss the other side. It feels very siloed, and you don't get the holistic insight that you want," said Santosh.
A true digital twin is a pragmatic, hybrid architecture built for enterprise scale. A primary goal is to create a trustworthy and cost-effective mirror, one that blends full tokenization for core records with statistically validated synthetic data for everything else.
Governance as a feature: "You need to treat de-anonymization as a deliberate feature. This means ensuring only people with the right privileges can de-tokenize the data, that you have the proper logs to track it, and that all of that activity is being monitored."
Smarter, not bigger: "Since it's too expensive to replicate all enterprise data, the goal is to ensure the statistical distribution of your synthetic data matches the original. As long as those distributions are the same, and you have checkpoints to validate them, the data can be trusted for analysis."
That hybrid strategy is especially useful during mergers and acquisitions. In these scenarios, a pre-defined digital twin playbook can provide a clear path to immediate utility, helping companies avoid the typical delays of securing new data streams and turning a potential bottleneck into a day-one advantage.
M&A fast track: "In M&A, companies acquire new organizations and bring in more data, but they're often not able to immediately put it to use. If you have a predefined strategy for what to do when a data asset comes in, you can tokenize its PII and use that data to your advantage from day one." The model can also empower the data scientists on the front lines. It helps free them from the bottlenecks of access requests and can provide a rich, safe, and ready-to-use source of features, preventing them from being tasked with "unbuildable" AI projects from the start. "This approach is very helpful for researchers and data scientists. It's much easier for them because they don't have to request access to every data set when the digital twin is already anonymized and contains no PII."
But for Santosh, the biggest hurdles are cultural. She framed the digital twin not as an "instant fix" but as a strategic "roadmap" that requires clear communication, prioritization, and buy-in. According to Santosh, a key step is to build trust with an evidence-based plan that proves the value of the new approach. "Governance and risk are not blockers; they're accelerators. They protect us from the risks that we might otherwise run into."
But often the biggest hurdles aren't technical—they're cultural. She framed the digital twin not as an "instant fix" but as a strategic "roadmap" that requires clear communication, prioritization, and buy-in. According to Santosh, a key step is to build trust with an evidence-based plan that proves the value of the new approach.
Proof in the performance: "The way to build trust is to show value quickly. If you can, run parallel tests to show the KPIs. For example, you can demonstrate that you're able to go to market faster by targeting the right audience for a recommendation engine, all without compromising PII. Being able to demonstrate that value is key."
"Building trust and getting cultural buy-in across the board is the real block that needs to be cleared—more so than technology. That's where organizations fall short." The companies that seem best positioned to win are those looking beyond the short-term, optical wins of the current frenzy for AI. By building a foundation of trust and a clear roadmap, they can transform their most challenging data into a structured opportunity and a lasting competitive advantage.
