No Hidden Trade-Offs: Why Measuring False Positives & Negatives Is the Only Way to Assess AI Bot Protection

Some bot protection vendors want you to believe in magic, promising zero false positives without showing the data to back it up.

At DataDome, we don’t do magic. We do math. Science. Accountability.

We commit to a 0.01% false positive rate—far lower than most—because precision matters. It protects users without disrupting them.

Even though it’s ultra-low, it’s not zero. And that difference matters. Here’s why.

We measure everything because that’s what science demands

If you want to build something with AI or machine learning, you first need data. And not just any data—real-time, streaming data tied to actual KPIs.

For us, two KPIs matter most:

1. False positives – when legitimate users are challenged
2. False negatives – when malicious bots or fraud slip through

These KPIs are not optional. They are how we define success. And they must be balanced together. You can’t minimize one and ignore the other. For example, you can have zero false positives by blocking nothing, but then you’ve let in every attacker. Or you can block everything and have zero false negatives—but you’ve just locked out your customers.

That’s why every decision we make is grounded in these two metrics. We track them in real time, and we act on them in real time. That’s the only way to maintain the highest standard of protection without compromising user experience.

If someone tells you their false positive rate is zero, ask to see the math

Let me be blunt: A false positive rate of 0% is impossible when working with real AI. 

Anyone making that claim is either:

• Not tracking it at all
• Not using AI at all
• Letting a ton of bots and fraud slip through 
• Or…not being honest

Why? Because any ML model that classifies requests will make occasional mistakes. That’s how AI works. Even OpenAI’s ChatGPT hallucinates. But contrary to LLM hallucinations, we can measure it very precisely.

At DataDome, we measure our false positive rate continuously. It’s consistently below 0.01%, or 1 in 10,000 legitimate requests. And we prove that with real customer traffic, live on our dashboard. If that rate starts to climb, our models auto-adjust. When a request is uncertain, meaning the system can’t determine with certainty whether it is a bad bot or not, we quietly validate it using technology like Device Check, which analyzes device-specific signals without disrupting the user with a CAPTCHA challenge, or worse, hard blocking the request. This discreet, privacy-compliant protection feeds directly back into our AI models for smarter detection over time.

Real-time feedback loops are a requirement of real AI 

Every robust AI system depends on continuous feedback loops, and at DataDome, those loops are engineered into the core of our AI detection engine. Our models are powered by automated, real-time feedback from both customer-side business signals and platform-level detection telemetry.

• On the detection side, we continuously analyze behavioral patterns, time series anomalies, invisible challenge-response outcomes (like Device Check passes), and aggregate traffic behavior across 5 trillion daily signals. These inputs feed into our multi-layered AI engine, including signature-based models, supervised learning, genetic algorithms, and anomaly detection, which are automatically retrained and redeployed into production. 
• From the customer side, we collaborate closely to collect anonymized business metrics—such as login denial rates, cart abandonment, bounce rates, and traffic anomalies. These signals provide high-context insight into how our detection decisions impact real users and help us ensure that protection doesn’t come at the expense of user experience.

This feedback loop is largely automated and operates at scale, enabling continuous learning and rapid adaptation to emerging threats. While not all signals can be real-time, we’re actively expanding our feedback channels with customers to improve coverage and responsiveness. This approach helps us stay ahead of evolving attacker tactics while maintaining false positive rates consistently below 0.01%.

Why this matters for your business: precision = profit

When your detection engine is powered by real-time feedback and low-latency AI, your business sees the results immediately, not just in threat mitigation but in actual outcomes:

Lower friction = higher conversions
By keeping false positives under 0.01%, your real customers move through registration, login, and checkout flows without interruption. That means more sales, more signups, and fewer support tickets.

Reduced fraud = real cost savings
Fewer false negatives means fewer chargebacks, fewer stolen credentials, and fewer attacks to clean up after. Our customers often recoup their investment in DataDome through savings in fraud-related charges and time. 

Higher ROI = smarter security investment
Our ROI calculator shows that enterprises using DataDome can save money by eliminating tool sprawl, reducing manual investigation time, and avoiding fraud losses and fines before they happen.

And because we track all of this in real time, you can see the business impact, not just the traffic patterns, right from your dashboard.

Why transparency matters 

No system is perfect. But transparency builds trust. We invest heavily in making our AI explainable and our performance visible. Our customers know exactly what’s happening and why. That alignment makes us better partners because we’re working toward the same goals, and we’re using the same data to get there. It’s why we have over 179 customer reviews on G2 with 4.8 star rating. 

This transparency also creates accountability. 

What should you ask your bot protection vendor?

If you’re evaluating solutions, here’s what I recommend asking:

• What’s your false positive rate? How do you measure it?
• How do you measure false negatives or catch unknown threats?
• Do you have real-time feedback loops in place? 
• Are the feedback loops used to update your models in real time?
• Can I see live data on your performance against my traffic?

If they can’t answer these questions or promise you perfection without evidence, walk away. That’s not science. That’s magic. 

The future belongs to intent-based detection

Traditional defenses like CAPTCHAs and browser checks aren’t enough anymore. Bots can now solve CAPTCHA. Humans can now use bots, including agentic AI. So the line between good and bad isn’t about the tool. It’s about intent.

That’s why the future of bot protection is intent-based detection, powered by adaptive AI models. It’s not about binary rules or reactive playbooks. It’s about understanding why a user is doing something, not just what they’re doing.

That’s where DataDome is headed—and that’s why measuring false positives and false negatives will remain the cornerstone of our platform.

Because you can’t protect what you don’t measure, and you shouldn’t trust what you can’t see.

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