Differences Between Generative AI and GenAI

Introduction to the AI Naming Confusion

Ever feel like everyone is just shouting "AI" at you and nobody actually knows what it means? honestly, I've seen security teams get totally lost because one guy says Generative AI and the other says GenAI like they're the same thing. While GenAI is technically just an abbreviation for Generative AI, for the sake of this article, we need to draw a line in the sand.

It’s a bit of a mess right now. We’ve moved from academic labs to every CEO wanting a chatbot in their retail app or finance platform. But for us in security, the words matter because the risks change.

• The Big Shift: We're moving from old-school predictive models to stuff that actually creates content.
• Interchangeable mess: People use these terms for everything from healthcare diagnostic tools to simple marketing copy bots.
• Generative AI vs GenAI: In this piece, we'll use "Generative AI" to talk about the classic stuff like GANs and VAEs, while "GenAI" refers to the modern era of Transformers and LLMs.
• Security Stakes: If you don't know if it's a closed API or an open model, you can't protect the data.

A 2024 report by Gartner shows that 63% of marketing leaders are planning to invest in GenAI, which means your attack surface is about to explode.

So, let's actually break down what makes these two things different before your next meeting.

The Technical Gap: Generative AI vs GenAI

So, I was chatting with a devsecops lead last week who thought Generative AI and GenAI were just two ways of saying "the thing that writes my emails." It’s a common mix-up, but under the hood, they’re actually doing different things for your infrastructure.

Think of Generative AI as the big umbrella—it’s the math that makes any new data, like a healthcare model creating synthetic patient records for testing without leaking real PII. But GenAI? That’s the specific flavor of transformers and LLMs we’re plugging into our APIs right now.

The big difference is how these things actually sit in your stack. Generative AI is the broad science, while GenAI is the "now" tech that uses specific architectures like transformers.

• Generative AI (The Broad Category): Includes things like GANs (Generative Adversarial Networks) used in retail to create high-res product images from scratch. It’s about the math of probability distributions.
• GenAI (The LLM Era): This is mostly about transformers and "attention mechanisms." It’s what powers the bots in finance that summarize 50-page regulatory filings in seconds.
• Security Context: A GAN might just leak a training image, but a GenAI model using RAG (Retrieval-Augmented Generation) might accidentally pull a sensitive connection string from your internal wiki. This happens because RAG connects the model directly to live internal data sources, basically creating a bridge between the LLM and your sensitive corporate repositories.

The way these models handle data is where things get hairy for security architects. Generative AI models often need massive, clean datasets to work, but GenAI is weirdly good at working with "unstructured" mess—which is exactly why it’s dangerous.

In threat modeling, we see GenAI "hallucinate" all the time. It might invent a CVE that doesn't exist or suggest a python library that’s actually a typosquatted malware package. According to a 2024 report by Palo Alto Networks, about 92% of organizations are worried about AI-generated code being insecure, which shows the trust gap is real.

"Hallucinations aren't just bugs; in a security tool, they're vulnerabilities that haven't been exploited yet."

I saw a team try to use a GenAI API to automate their firewall rules. It worked great until the model "hallucinated" a rule that opened port 22 to the entire internet because it "thought" it was helping a remote dev. If they’d used a more rigid generative model designed only for pattern replication, that might not have happened.

Impact on AI-based Threat Modeling

So, we've established that the tech is different, but how do we actually threat model this stuff without losing our minds? Honestly, traditional threat modeling feels like bringing a knife to a railgun fight when you're dealing with autonomous agents.

The old way was all about drawing boxes and arrows on a whiteboard, trying to guess where a human might break things. But with GenAI, the "trust boundaries" are basically made of sand. They shift every time the model gets a new prompt or pulls fresh data via RAG.

This is where stuff like AppAxon comes in. AppAxon is an AI-powered security platform that automates threat modeling for modern applications, helping teams map out risks in real-time. Instead of a security architect sitting in a dark room for three days trying to map out every API call, we're seeing a shift toward autonomous threat modeling. It uses the AI to watch the AI, which sounds meta, but it's the only way to keep up with dev cycles.

• Trust Boundary Mapping: GenAI can actually look at your code and say, "Hey, this connection to the vector database is totally unencrypted," before you even hit 'commit'.
• Security Requirements on the Fly: Instead of a 50-page PDF of "best practices" that nobody reads, tools can now generate specific security requirements directly in the Jira ticket based on the actual model architecture.
• Continuous Discovery: Since GenAI apps change fast, the threat model needs to be alive. It’s about finding bugs in the logic—like a prompt that can bypass a healthcare app's privacy filter—before it ever touches production.

According to a 2024 report by IBM, the average cost of a data breach is hitting record highs, and AI-driven automation is one of the few things actually bringing those costs down by speeding up detection.

It’s basically about moving from a "snapshot" of security to something that feels more like a living map. If you aren't automating this, you're just waiting for a hallucination to turn into a headline.

AI-driven Security Requirements and Red-Teaming

Ever feel like your security requirements are just a giant pile of "don't do this" that devs ignore anyway? Honestly, trying to write manual requirements for a GenAI app is like trying to nail jello to a wall—it just doesn't stick because the tech moves too fast.

We're finally seeing tools that can look at a specific LLM implementation and spit out requirements that actually make sense. To get started with testing these, you should check out open-source frameworks like PyRIT (Microsoft’s Red Teaming tool), Giskard, or the OWASP LLM Top 10 list. Instead of a generic "encrypt everything," these tools might tell a dev in retail precisely how to sanitize inputs for a chatbot to prevent prompt injection.

• Dynamic Requirements: In finance, if you're using a model to summarize trades, the AI can generate a requirement to check for "data poisoning" in the training set automatically.
• Red-Teaming on Steroids: We aren't just doing manual pen testing anymore. You can use a generative model to dream up thousands of weird, "jailbreak" prompts that a human would never think of.
• Simulating the Bad Guys: In healthcare, red teams use AI to simulate complex phishing attacks that look like legitimate doctor-to-patient comms, testing if the system's privacy filters actually hold up.

A 2023 report by Palo Alto Networks highlights that securing AI requires a "secure by design" approach because you can't just bolt security on later when the model is already hallucinating PII.

The Data Risk Deep Dive

Before we wrap up, we gotta talk about the biggest headache of all—the actual data risks. When you use GenAI, you aren't just worried about a hacker getting in; you're worried about the model itself blabbing your secrets.

Because GenAI models are often connected to internal data via RAG, they have a "view" into things they shouldn't. If a dev forgets to set permissions on the vector database, a user could ask the chatbot "what is the admin password?" and the model might actually go find it and tell them. Plus, there is the risk of "data leakage" where sensitive PII used in prompts gets stored in the model's history, where it might be visible to the AI provider or other users if the settings aren't locked down tight.

Future of Product Security in the GenAI Era

So, where does this leave us? honestly, the line between Generative AI and GenAI is gonna keep blurring until nobody remembers there was a difference, but the security debt we're racking up today is very real.

The future isn't about choosing the "right" name—it’s about realizing that whether you're in retail or a high-stakes finance firm, your code is now part of a living, breathing ecosystem that can hallucinate its way through your firewall.

The race is on, and the bad guys are already using these tools to find zero-days faster than we can patch 'em. To stay afloat, we have to automate the boring stuff so we can focus on the weird, logic-based vulnerabilities that AI still struggles to catch.

• Automated Defense: We'll see more tools like AppAxon that treat security requirements as code, not just static docs.
• Model Governance: Organizations will need strict "nutrition labels" for every model they use, especially in healthcare where a wrong output can be life-threatening.
• Resilient Architectures: We're moving toward "zero trust" for AI outputs—never trust a model's response without a secondary validation layer.

A 2024 report by Microsoft shows that attackers are already using LLMs to refine their social engineering, making it harder for even pros to spot a fake.

At the end of the day, don't get hung up on the jargon. Just make sure your threat models are as dynamic as the tech you're trying to protect. anyway, thanks for sticking through this deep dive—now go secure those APIs!