There is nothing dramatic about the opening scene. No hooded hackers. No blinking command lines. No alarms. Just a corporate laptop, an external hard drive, and years of proprietary research quietly walking out the door.

That is how this story begins.

Not as a cyberattack, but as one of the most revealing cold cases in modern industrial cybersecurity. A case that has little to do with malware and everything to do with Data Loss Prevention that failed because, operationally, it was never truly in place.

Why Waymo Mattered

Waymo began as an internal Google project before becoming a standalone company under Alphabet. Its mission was clear and uncompromising: to build the most advanced autonomous driving system in the world.

At the center of that ambition was not just software. It was LiDAR. A laser based sensing technology that allows autonomous vehicles to perceive their surroundings with extreme accuracy.

There is no standard LiDAR.  Each manufacturer develops its own, shaped by years of experimentation, failed designs, and patented breakthroughs.

Waymo invested years and hundreds of millions of dollars to build what many considered one of the most advanced LiDAR systems on the market, valuable enough to be licensed to traditional automotive manufacturers. That LiDAR was not just engineering excellence.

It was competitive power.

The Insider: Anthony Levandowski

Anthony Levandowski was not a junior employee or a peripheral contributor. He was a senior engineer deeply involved in Waymo’s autonomous driving efforts. In 2016, he left the company. Shortly afterward, he founded Otto, a startup focused on autonomous trucking.

Within months, Otto was acquired by Uber for approximately 680 million dollars.

The speed of that acquisition raised immediate questions. Especially in a domain like LiDAR, where Uber, according to Waymo, did not possess a mature internal solution until just weeks earlier

The 14,000 Files No One Stopped

In 2017, Waymo filed a lawsuit against Levandowski in California, accusing him of trade secret theft.

According to court records and reporting by Wired, shortly before leaving Waymo, Levandowski transferred approximately 14,000 confidential files, totaling 9.7 gigabytes of data, from his corporate laptop to an external hard drive.

These were not incidental documents.

They included technical designs, schematics, system architectures, and specifications critical to building a competitive LiDAR platform.

A forensic analysis later confirmed the transfers. They happened. And they were substantial.

The Fallout: Termination, Settlement, Conviction

Following the lawsuit: Uber terminated Levandowski’s employment. Uber and Waymo reached a settlement in 2018.

As part of the agreement:

• Uber committed not to use Waymo’s technology

• Waymo received approximately 245 million dollars in Uber equity

Levandowski was later sentenced to 18 months in prison for trade secret theft, before receiving a presidential pardon. By that point, however, the damage had already occurred. And it extended well beyond financial loss.

The Real Cold Case Question

The critical issue in this case is not legal. It is not reputational. It is organizational and technological.

Levandowski did not exploit a vulnerability. He did not bypass authentication. He did not defeat perimeter defenses. He had legitimate access. And once copied, the files were fully readable. That reveals a fundamental truth: data protection was perimeter based, not file centric.

Operationally:

• Files were not persistently encrypted

• Sensitive documents were not automatically classified

• Download behavior was not correlated with risk context

• No real time alerts flagged abnormal mass transfers

• Once outside, data was no longer governed

If traditional DLP existed, it did not survive real world conditions.

Why Traditional DLP Fails Organizations

This case illustrates a broader industry reality. Organizations do not abandon DLP because it is unnecessary. They abandon it because it is often:

• Complex to deploy

• Expensive to maintain

• Disruptive to workflows

• Overloaded with false positives

• Easy to work around

The result is predictable.

Data is created unprotected and investigated only after it has already left the organization.

How This Story Would Have Played Out with CyberGrant

With CyberGrant, this story would not have started the same way. Because the objective is not to detect loss after the fact, but to prevent sensitive data from ever being born ungoverned.

Here, DLP is not an add on layer bolted onto existing infrastructure. It is native, file centric, and automatic.

Every technical document, every LiDAR design, every system specification enters a secure vault at creation. Encryption is transparent to users but permanent for the file.

The real shift happens even earlier.

A private AI engine analyzes document content at the moment of creation or modification. It recognizes intellectual property, trade secrets, and strategic assets. Based on that understanding, it automatically applies classification tags and enforces access and usage policies.

No manual labeling. No reliance on user behavior. Protection is embedded directly into the data.

From that point forward, the file carries its own rules. It knows who can access it, from which devices, using which applications, and under what conditions. Security is no longer tied to the corporate perimeter, but to data governance, enforceability, and provability.

Even if copied.

Even if downloaded.

Even if taken outside the organization.

What Would Have Changed for Waymo

Those 14,000 files would have remained encrypted and unreadable outside authorized devices and identities.  Mass download attempts, particularly in the period leading up to resignation, would have triggered immediate alerts, not forensic reconstruction months later.  Most importantly, the files would have lost what made them dangerous: their competitive value.

Not because theft would have been impossible, but because stolen data would have been unusable outside governed contexts, protected by quantum resistant encryption.

Complete traceability across access, download, and usage attempts would have produced immediate, defensible evidence for security, legal, and executive teams.  No assumptions. No reconstruction after the damage. Only facts.

The Core Lesson: DLP That Works Because It Aligns With Reality

With CyberGrant, this would not be a story about discovering a loss too late.

It would be a story about data that remains governed, enforceable, and provable, even outside the corporate boundary. CyberGrant demonstrates a principle many organizations overlook:

Security only works when it aligns with how people actually work.

No endless manual classification.

No year long deployment projects.

No unnecessary friction.

Protection follows the file.

Not the perimeter.

Not trust.

Not assumptions.

Final Takeaway for CISOs and Security Leaders

The Waymo Uber case is not history. It is a warning. An insider with legitimate access can remove years of innovation in minutes if sensitive data is born unprotected. The real question is not who can access a file. It is what that file can do once it leaves your control.

That is where modern DLP makes the difference. CyberGrant does not promise the impossible. It delivers something far more practical: 

Data protection that is enforceable, sustainable, and finally accessible at scale.