Can AI Eliminate Human Error? Lessons from Coinbase, CrowdStrike, and Tesla

Human error is costly—sometimes catastrophically so. From Cybersecurity breaches to Regulatory missteps and Automotive accidents, mistakes in Human Judgment, Oversight, or Process can ripple across industries. Artificial Intelligence (AI), with its data-driven precision, offers a promising solution to minimize these risks. Let’s explore some high-profile cases that underscore the cost of human error and AI’s transformative potential in cybersecurity, policy, and automotive safety.

1. Coinbase’s Insider Threat Breach (May 2025)

Just a few days ago, in May 2025, Coinbase, a leading cryptocurrency platform, suffered a US$400 million loss. Although this incident was widely reported as a Cyberattack by the media, it was actually due to an insider threat. Inadequately vetted overseas employees were bribed to leak sensitive system data to outside parties. This breach, stemming from lapses in internal controls, shook market confidence in the crypto industry. The resulting erosion of trust caused COIN’s stock to drop over 8% in less than two hours and 7% at the close of trading. Even tech giants falter when human oversight fails.

AI could have mitigated this by continuously monitoring employee behavior for anomalies—such as unusual access patterns—and flagging risks in real-time. Robust AI-driven vetting processes could also strengthen hiring practices, reducing the likelihood of such betrayals.

2. CrowdStrike’s Global IT Outage (July 2024)

In July 2024, a flawed CrowdStrike software update caused an outage affecting 8.5 million Windows servers and workstations worldwide. The technical culprit was a logic error in a kernel-level driver, which had boot-start privileges.

But why was this driver included in the Windows OS kernel layer?

A 2009 EU antitrust settlement, reinforced by the 2022 Digital Markets Act, required Microsoft to grant third-party vendors access to the Windows OS kernel. This policy, designed to promote competition, inadvertently enabled a single error to trigger a global IT meltdown. While CrowdStrike’s quality assurance bears responsibility, human-driven regulatory decisions—made without fully anticipating cybersecurity risks—set the stage for disaster.

AI could help by simulating the long-term impacts of policies, modeling how open kernel access might interact with software updates. Such foresight could guide regulators to balance competition with security.

3. Tesla’s AI-Driven Safety Revolution

AI’s potential shines in Tesla’s Full Self-Driving (FSD) technology. According to Tesla’s Q1 2025 Safety Report, FSD-equipped vehicles have accident rates up to five times lower than the U.S. human average. Unlike human drivers, AI doesn’t tire, misjudge distances, or act impulsively.

This rigor could transform other domains:

• Cybersecurity: AI can automatically validate code, catching logic errors before deployment.

• Insider Threats: Real-time anomaly detection can identify suspicious employee actions.

• Policy Design: AI simulations can predict how regulations, like kernel access rules, might impact IT ecosystems.

A Path Forward

These cases—Coinbase’s breach, CrowdStrike’s outage, and Tesla’s safety gains—reveal a pattern: human decisions spark errors, while AI offers consistent, data-driven solutions. Of course, AI isn’t perfect. Biases in training data, design flaws, and the need for human oversight remain challenges. Yet, unlike humans, AI systems can’t be bribed or emotionally swayed, making them powerful allies in risk management.

By integrating AI into cybersecurity, regulatory planning, and operational processes, industries can build a more secure, efficient future. Can your industry also leverage AI technology to curb costly human errors?

Remember Murphy's Law of Human Error

• "Anything that can go wrong, will go wrong."

• "If there are two or more ways to do something and one of those results in a catastrophe, then someone will do it that way."

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