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Presented by EDB
As synthetic data reshapes decision-making, business leaders must reassert control over what’s real, what’s generated — and what can be trusted.
In the 1983 film WarGames, Matthew Broderick’s character nearly triggers nuclear war — not with weapons, but with synthetic data. The fictional WOPR system misinterprets simulated war-game inputs as real-world threats. It’s only when humans call a target base and confirm there’s no actual strike that they realize the system has gone rogue.
Forty years later, the stakes are no less existential — only now, synthetic data underpins much of our decision-making. AI-generated models, forecasts, and simulations are embedded into healthcare, finance, marketing, cybersecurity, and increasingly into the very operating fabric of modern enterprises. But who verifies the verifier? And how do we maintain sovereignty over decisions made with — or by — synthetic data?
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The synthetic data surge
Synthetic data — AI-generated information that mimics real-world datasets — is powering everything from new pharmaceutical protocols to predictive customer models. Its value is undeniable: faster iteration, reduced privacy concerns, and the ability to model the improbable. In many domains, it’s the only scalable way to train large, complex systems.
But synthetic data isn’t neutral. It is manufactured on assumptions, trained on biased inputs, and tuned to reflect a world that may or may not exist. And as generative AI increasingly produces both the questions and the answers, we risk building a feedback loop in which AI becomes the only entity capable of making sense of the data it generates.
This is more than a technical challenge — it’s a leadership one.
The decision-making dilemma
Three questions now define the modern leader’s data dilemma:
- When should synthetic data drive decisions ahead of human judgment?
- How do we balance real-world signals with synthetic simulations?
- Where does human instinct still matter — and how do we know when to trust it?
This isn’t theoretical. It’s already playing out in AI-enabled customer relationship management (CRM) tools that suggest next-best actions, in predictive models that set prices or assess risk, and in algorithms making hiring or lending decisions. Synthetic data can increase efficiency — but without rigorous oversight, it can also entrench bias, manufacture false confidence, and obscure critical signals.
This is especially dangerous in environments moving at machine speed. If AI systems are constantly producing and modifying data, the very notion of truth begins to erode. Without clear controls and interpretability, we risk losing our ability to verify anything at all.
Thomas Koulopoulos, chair at Delphi Group, author, and leading “digital futurist,” warns that the rise of AI-generated data raises profound questions about trust and truth in decision-making:
“If AI is constantly producing and modifying data, there’s the notion of would its truth even hold anymore? It gets a bit philosophical, but it’s relevant. We will see this new sort of data inflation where human discretion and discernment is no longer enough to really extract meaningful insights from the data. AI becomes the only entity capable of making sense of the data that it generates. So the philosophical and the ethical implications of this are the critical ones.”
His point underscores the urgency for leaders to define boundaries, not just between real and synthetic data but between delegation and abdication of judgment.
Sovereignty is the new differentiator
The solution isn’t to reject synthetic data — it’s to govern it.
Sovereignty over your data and AI systems means having the architecture, observability, and human expertise to inspect, challenge, and contextualize machine-generated insights. This requires:
- Data provenance: Knowing where your data comes from and how it was constructed
- Model transparency: Understanding how AI systems reach conclusions
- Decision rights: Defining when final authority rests with the machine, the human, or both
Enterprises that build sovereign data and AI platforms — platforms they control, audit, and evolve on their own terms — will be best positioned to harness the power of synthetic data without falling victim to its blind spots.
Human insight is the arbitrage
Even in the most advanced AI-driven systems, human discernment is the missing link. Real-world experience, gut instinct, and contextual knowledge sit between raw synthetic input and actionable decision-making.
Just as in WarGames, the most critical intervention isn’t technical — it’s human: a phone call, a question, a gut check that breaks the machine’s logic loop.
As AI grows more capable, humans must grow more curious, more probabilistic in their thinking, and more comfortable with ambiguity. The future belongs to those who can navigate the grey space between synthetic and real — between simulation and truth.
Synthetic data offers extraordinary potential, but unchecked automation won’t save us from poor decisions. Sovereignty, governance, and human insight must remain at the core of every AI strategy. Otherwise, we may not notice the moment we let machines confuse the war game for the real one.
Robert Feldman is Chief Legal Officer at EDB.
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