Cybersecurity frameworks can protect against traditional threats. But generative AI is another story.
The hard truth is that those frameworks weren’t built for Large Language Models (LLMs)—a fact many teams don’t discover until after deployment. They assume traditional controls will protect these new systems, and just as risky, they trust AI outputs like they would calculator answers.
Those assumptions create blind spots that attackers are already exploiting.
We see this pattern often at Quantum Rise. Generative AI is new, and teams bring a traditional software mindset that underestimates how LLMs actually behave.
The excitement is understandable. It also obscures risks with no real equivalent in conventional systems.
Here are the top generative AI blind spots we see and how to address them.
Be honest: how many times has an LLM convinced you of something that turned out not to be true?
It’s a real problem. These “hallucinations” can be funny in casual use, but in business, they’re dangerous. LLMs speak with confidence and authority, which makes it easy for users to stop questioning the output just because it sounds credible.
The results can be serious:
The fix: keep humans in the loop. LLMs are powerful tools, but they need oversight. Validate what they produce, require review before anything goes live, and treat outputs as drafts to be checked—not final answers to be trusted.
Your security tools know how to catch malware, suspicious traffic, and SQL injections. What they can’t see are the new attack vectors that slip in through natural language:
The fix: Treat what goes into and comes out of your model the same way you’d treat any other security risk. Put guardrails on the inputs, double-check the outputs before they hit your systems, and don’t give the model more access than it really needs. And for anything important, such as money, data, or customer accounts, make sure a person has the final say.
Even if your defenses are airtight, AI introduces exposure you can’t fully control. The moment you rely on outside models or data, you also inherit their risks.
Most systems depend on external datasets, pre-trained components, or cloud providers. When you bring in a vendor’s model, you also take on their vulnerabilities—without much visibility into how it was built, what data went in, or what weaknesses might be hiding inside. Many open-source AI tools, in particular, move fast and prioritize innovation over the kind of enterprise-grade security you’d expect from traditional software.
There’s also the issue of model theft. Attackers don’t always need direct access to your files. With enough queries, they can reverse-engineer a working copy of your model and walk away with what amounts to your competitive advantage.
The fix: Push vendors to be transparent about how their models are trained, tested, and secured, and set clear expectations in your contracts. On your side, monitor for suspicious query patterns that could point to extraction attempts, and protect your models as carefully as you protect your most valuable intellectual property.
It's easy to believe that the more data you have, the better your AI will perform. However, big data doesn't always equate to good data. If the information is biased, incomplete, or inaccurate, your model will learn from those flaws and replicate them at scale. This issue can lead to discriminatory decisions that violate regulations and damage your reputation. It can even surface memorized details that should have stayed private, from personal data to sensitive company information.
Worse, data isn't just vulnerable to mistakes. Attackers can deliberately poison it by slipping malicious examples into training sets, creating hidden backdoors that remain dormant until triggered. These "sleeper agents" survive standard safety measures. By then, you're dealing with an AI that looks trustworthy but can't be relied on.
The fix: Quality beats quantity. Work with data that's representative, documented, and clean. Strip out sensitive details where possible, add privacy protections, and keep track of where your data originated so you can trace problems back to their source.
Even if you avoid the most common blind spots, there’s one reality you can’t ignore: attackers are moving faster than traditional defenses can keep up. They’re already using LLMs to craft phishing emails that read like they came from your colleagues and spinning new malware variants at a pace that outstrips detection tools.
The challenge is far from evenly matched. While you’re deploying AI carefully, with governance and controls, attackers aren’t playing by the same rules. They only need to succeed once, while you have to remain vigilant and block every attempt. From poisoned data to disinformation and denial-of-service attempts, adversaries are exploring every angle.
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Bassam Arshad, Data Scientist
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