Baseball has always been a game of tradition, filled with unwritten rules, iconic moments, and a human element that includes the players on the field and the umpires calling the game. As the world changes, so does baseball. Technology, and more recently artificial intelligence (AI), is becoming an integral part of the sport. From AI-powered pitch-tracking systems to automated strike zones, AI is redefining the role of umpires and how the game is played.

But baseball isn’t the only place AI is stepping in as a decision-maker. Companies everywhere are integrating AI into their operations—whether it’s streamlining processes, providing customer support, or analyzing complex datasets. Just as in baseball, though, not all AI systems are created equal. There’s a crucial aspect of trust that often gets overlooked: data security.

The Umpire’s Dilemma: Human Judgment vs. AI

In baseball, umpires have long been the final authority on balls, strikes, and plays at the plate. However, with the rise of AI and systems like the automated strike zone, some of that human judgment is now being outsourced. While this technology offers precision, it also raises questions about transparency, trust, and potential failure points. What happens if the system malfunctions during a critical game?

Similarly, in the business world, AI platforms are making important decisions, sometimes with access to vast amounts of sensitive data. Many organizations rely on AI systems like ChatGPT or other industry-specific third parties to help manage tasks, analyze customer data, or engage with clients. But what happens when those systems—like the AI umpires—are not held to the same standards of security? Just like in baseball, a single bad call or data breach can have significant consequences.

A Homerun, or Was It? The AI Triple that Missed the Wind

Imagine a baseball game in the bottom of the ninth inning. The batter swings and sends the ball flying towards the outfield fence. The crowd cheers as the ball sails over for a homerun—game over, victory secured! But wait. According to the AI-powered tracking system, that wasn’t a home run. Based purely on the speed, trajectory, and height of the ball, the AI calls it a triple.

What the AI didn’t account for, though, was the gust of wind that took the ball just beyond the outfield wall. The wind gave the ball an extra push, but because the AI system was limited to the data it was trained on, it missed this crucial factor. The human umpires, watching the game unfold, rightfully called it a home run. This scenario highlights the limits of AI—external conditions that can’t always be predicted by algorithms.

Now imagine this same situation, but instead of using live, real-time data, the AI system is relying on legacy data or information that has been pulled from multiple sources outside of the system. In this scenario, the AI is even more disconnected, working with outdated information or data that’s no longer relevant. Just like missing the wind that helped the ball over the fence, the AI system operating on legacy data would struggle to make accurate decisions based on the current reality of the game.

This is what happens with many AI platforms today: they work with disconnected, external data that are often outdated or fragmented, resulting in decisions that miss the mark. Legacy systems or external data integrationscan lead to errors, as these systems may not have access to the latest data within the secure environment of a self-contained platform. The more data moves outside the system, the greater the chances of inaccuracy and security vulnerabilities.

Why a Self-Contained System Makes a Difference

This brings us to the fundamental advantage of a self-contained AI system. When data stays within the same secure environment, as it does in a system like ours, accuracy improves significantly. The platform is always working with the most up-to-date information because the data hasn’t left the system to be processed externally. This is like having an umpire call the game from right behind the plate instead of relying on a delayed replay from a different ballpark.

By staying within the system, the AI can process data in real-time and account for real-world variables like the wind in the home run scenario. More importantly, this data stays secure. When platforms move data outside their systems, they introduce risk—data can be intercepted, modified, or delayed. This creates the same problem that occurs when AI misjudges the game: inaccurate decisions and lost opportunities.

Data Security: A Growing Concern for AI Users

More companies are realizing that many AI platforms today are not held to the highest security standards. There’s a growing awareness that many of these platforms, while powerful, may not be SOC 2 compliant or handle sensitive data securely. This creates a significant risk, especially in the event of a data breach or security failure.

However, businesses are increasingly looking for AI platforms that prioritize data security. When presented with the idea of a secure, closed AI platform—one that offers end-to-end encryption, controlled access, and SOC 2 Type 2 compliance—there’s immediate interest. As data breaches become more frequent and costly, security becomes not just a nice-to-have but a core requirement.

Umpires vs. AI: The Human Element and the Need for Security

Umpires bring a human element to baseball—a mix of experience, intuition, and judgment. AI systems, on the other hand, are built on algorithms and data. While they can be faster and more accurate in some instances, they lack the nuance and adaptability that comes with human oversight. But with the use of AI, whether in baseball or business, there comes a need for trust.

AI systems like ChatGPT or other third-party solutions are only as good as the security measures behind them. In baseball, when an umpire gets a call wrong, it’s visible and can sometimes be corrected. When an AI platform mishandles sensitive data, the consequences are often hidden until it’s too late. This is why security measures like SOC 2 compliance, multi-factor authentication (MFA), and regular vulnerability testing should be non-negotiable.

The CINC Difference: Setting the Standard for Secure AI

Much like how baseball is evolving with technology, so too is the business world. Companies are waking up to the fact that not all AI platforms are created equal. At CINC, the focus is on creating a secure, closed AI platform that puts data security first. This ensures that businesses using AI don’t get caught off guard when the stakes are high.

In baseball, the umpire’s role is to ensure the game is played fairly and by the rules. In AI, it’s our responsibility to ensure that our systems protect sensitive data and operate transparently, free from the risks of breaches or unauthorized access. With features like SOC 2 Type 2 compliance, data backups, and access controls, CINC’s platform goes beyond what many AI providers offer today.

Just Like in Baseball, the Game is Changing

As AI becomes more integrated into everyday operations, from the boardroom to the ballpark, the need for security grows. Clients may not always be aware of the risks they’re taking when using open, unsecured AI platforms. But much like a missed call in a high-stakes game, those risks can come back to hurt when least expected. That’s why companies should focus on platforms that prioritize security and compliance—because in today’s game, it’s not just about being fast or accurate, it’s about being safe.

In the end, whether it’s calling a ball or strike in a baseball game or safeguarding a company’s data, one thing is clear: trust and security are non-negotiable. And just like umpires who earn respect through consistency and fairness, AI platforms need to prove their worth by ensuring the safety of the data they manage.