What Is AI Security—and Why It’s Not the Same as Traditional Cybersecurity

Let’s face it—AI is everywhere now. From smart assistants to fraud detection to generative tools (yes, like this one!), AI is becoming deeply embedded in how businesses operate and how people interact with technology.

But as powerful as AI is, there’s something we don’t talk about nearly enough:

How do we keep AI safe?

I’m not just talking about locking down servers or encrypting data. I’m talking about protecting the actual intelligence—the algorithms, the training data, the models that learn from our behaviors, and sometimes even make decisions on our behalf.

This is where AI Security steps in. And trust me—it’s not the same old cybersecurity with a shiny new name.

So… What Is AI Security Anyway?

Let’s break it down.

AI security is all about protecting the systems that create, run, and interact with AI models. That includes:

• The models themselves – Think of this as the "brain" of an AI system. It can be reverse-engineered, stolen, or fooled.

• The data used to train models – If someone tampers with this data, the AI could “learn” the wrong things.

• The inputs and outputs during inference – Even after deployment, AI models can be tricked into making dangerous decisions.

• The surrounding infrastructure – APIs, storage, versioning, access control, and pipelines all need protection.

The point is, AI systems are not static. They evolve, adapt, and sometimes act unpredictably. That means traditional security strategies often fall short.

How Is AI Security Different from Traditional Cybersecurity?

Let’s look at a real-world comparison.

Traditional Cybersecurity is like securing a building. You lock the doors, monitor who comes in and out, and install alarms. You’re guarding infrastructure—servers, networks, devices, apps.

But AI Security is like guarding a person’s mind. You're protecting how it thinks, what it learns, and how it makes decisions. That’s a much messier—and more nuanced—challenge.

Here’s a quick breakdown:

Traditional Cybersecurity AI Security Defends systems from known threats (e.g., malware, phishing, DDoS). It defends models from novel threats (e.g., adversarial inputs, data poisoning). It focuses on static systems and dynamic, evolving systems. It uses clear rules and signatures, dealing with probabilistic models and hidden behaviors. Protects data and infrastructure. Protects data, models, and decision-making integrity

Why Should You Care About AI Security?

If your company is using AI (and let’s be honest, who isn’t these days?), here are a few reasons to take AI security seriously:

1. AI Can Be Tricked

Ever seen an AI mistake a turtle for a rifle just because of a pattern on its shell? That’s called an adversarial attack. A small, almost invisible tweak to input data can lead to catastrophic results.

Imagine this happening in:

• Self-driving cars misreading traffic signs

• Healthcare systems are giving wrong diagnoses

• Fraud detection systems approving scams

2. AI Can Be Stolen

Models cost money, data, and time to build. But they can be reverse-engineered or copied if exposed via APIs—this is called model extraction.

3. AI Can Be Biased or Poisoned

If your training data is flawed or intentionally corrupted (a data poisoning attack), your AI might learn things it shouldn’t—like denying loans unfairly or making unethical hiring recommendations.

What Can We Do About It?

Here’s where things get practical. Protecting AI systems isn’t just about firewalls and encryption. It’s about embedding security throughout the AI lifecycle.

Some key practices:

• Secure your training data – Use trusted sources and audit for tampering or bias.

• Test for adversarial vulnerabilities – Run red-team simulations to see how your model behaves under attack.

• Limit model exposure – Don’t give away full access to your models via open APIs.

• Monitor behavior post-deployment – AI continues to learn and adapt, so monitoring never stops.

• Build explainability into models – If you can’t explain how your model works, you can’t really secure it.

And most importantly, AI security isn’t just a job for data scientists. It’s a team sport involving engineers, security teams, compliance, legal, and leadership.

Final Thoughts: It’s Time We Rethink “Security”

In the age of AI, security means more than stopping hackers. It means building trustworthy, resilient systems that respect privacy, fairness, and integrity.

If your AI is powerful but vulnerable, it’s not really a competitive advantage.

So ask yourself (or your team):

“If someone tried to attack our AI today, would we even know?”

Let’s secure it together.

-DPK