Generative AI Security Risks and How to Protect Yourself

By Dr. Andre Slonopas  |  01/27/2026

Almost every organization now uses generative artificial intelligence (AI) for customer service, writing code, analyzing data, and making decisions. These generative AI systems learn from a lot of data, respond quickly immediately to what people say, and provide findings that look trustworthy.

However, there are also generative AI security risks that come with the use of this powerful technology. Because of AI’s speed and the trust that organizations place it in, generative AI security issues need to be handled extremely seriously right now.

The Security Risks of Generative AI

In the past, software that someone built always operated in the same way. But generative AI systems that create things don’t.

Generative AI systems look for patterns in a lot of training datasets, adjust how users respond, and then utilize probability, not certainty, to make choices. However, the same human inputs may lead to different AI outputs that may change from one encounter to the next.

That lack of determinism is strong. As a result, that should change the way we think about security risks. Frequent security risks for AI include:

• Data leakage
• Data poisoning
• Hallucination
• AI model theft
• Prompt injection and subtle modification of AI-generated content
• Social engineering attacks

Threat actors use these flaws to create attack paths and:

• Get to private data
• Damage AI model integrity
• Set up assaults that go undetected

These attacks don’t happen very often. They are assault pathways that may be repeated and are linked to new threats.

People that seek to hurt others could edit AI-generated material, add false information to AI training data, or use automated social engineering tactics to sway public opinion in a calm manner.

To deal with these security risks, it’s necessary to think in an organized, lifecycle-based way. Organizations must reevaluate access control, security posture, sensitive information, and accountability from the perspective of AI security measures.

That work should start with early development and training and continue through deployment and continuous operation. Ignoring these security issues will create potential security risks.

Data Leakage

Autonomy affects the rules of the game in many ways. Without any help from people, generative AI can already:

• Summarize reports
• Write code
• Approve processes
• Make decisions

Many AI programs work with private information, such personally identifiable information and other sorts of sensitive data. Data leakage mistakes spread fast when they happen, decreasing an organization's security posture.

There may not be any visible warning signs before sensitive data leaks, sensitive content goes public, or big data breaches occur and let identity theft happen. The old ways of keeping information secure weren’t made to cope with these new kinds of AI security risks.

When generative AI is present, it is tougher to maintain data security. These AI systems do more than merely keep track of data. They learn from it, change it, and bring it to the surface in ways that are hard to predict.

Even if no one wants to hack the system, prompts, context windows, and AI outputs might accidentally give out sensitive information. That’s how using generative AI in your daily life might put your safety at risk.

When information is shared or hosted in the cloud, the situation becomes worse. A lot of generative AI platforms are multi-tenant services, which means that more than one company may utilize the same models.

Providers spend a lot of money on isolation, yet there is always a danger that private data may get out. Data exposure may occur if settings aren’t set up correctly, prompts are too open, or users engage with them in ways that aren’t intended by the original creators.

Most of the time, these failures don’t seem like normal breaches. They look like little alterations or suspicious behavior that are easy to ignore.

I noticed this problem on a project where everyone thought the data was safe. Using databases was safe, and people’s access was restricted. It was safe on paper to save intellectual property and customer data.

After a security program, we looked at the logs and prompts. That’s where the shock came from. There was a lot of personal information in ordinary conversations, as well as debugging prompts and feedback loops. AI developers supplied “temporary” answers to make responses better.

It didn’t seem like any of it was bad at the time. But once that knowledge impacted the AI model, it got around.

The amount of information that AI systems can gather and the lack of transparency may make our society less safe. There are so many diverse locations and methods that people use AI apps that no one can see the complete picture.

Data Poisoning

Every generative AI system is built on training data. It affects how generative AI models think, what they care about, and how they react to the environment.

Systems are updated when the training data is changed. That is why data poisoning is a much-ignored AI security risk in artificial intelligence today and a core concern in generative AI security.

Most companies are more concerned with safeguarding their outputs, but few companies look at what data gets into their AI tools.

When training AI models, there should be a lot of input data from internal repositories, third-party sources, or open datasets. The danger goes up fast if the data involves:

• Sensitive information
• Proprietary content
• Records that aren’t well-managed

Once AI systems learn information, it is hard to remove from the system. That creates long-term security challenges for generative AI systems that are used on a large scale.

I learnt this lesson the hard way during a model review that I thought wasn’t very important at first. The system was up and operating, and the outputs looked good.

Then, someone saw a trend that didn’t make sense. There was nothing broken, but there was pressure.

Data poisoning attacks don’t make systems crash. However, emerging threats are ever changing.

Threat actors take advantage of this flaw. They introduce malicious data into training or retraining cycles and then wait for the results.

Model and user behavior shifts over time, and responses are skewed. Outputs quietly help someone on the outside and evade detection unless a threat actors knows exactly where to look.

Hallucination

Hallucination is one of the most misunderstood threats in generative AI. AI systems can give you confident, well-written responses that are wrong, don’t have all the facts, or are misleading.

Wrong information can be embarrassing or even dangerous. In complex AI systems, hallucinated outputs might quietly add errors to training data, reports, judgments, or automated procedures that rely on trust instead of checking.

The next thing to worry about is trusting too much in AI systems, which introduces security risks. People often trust AI-generated responses without inquiry because they sound like they came from an expert, which can be hazardous.

Often, people don’t manage private data or other sensitive information properly. Bad information from AI may make information security weaker or even go around current protections. AI security algorithms that think that accuracy is enough are likely to fail in little ways.

Threat actors who want to do harm on purpose take advantage of this gap. Using prompt injection, compromised dependencies, and supply chain attacks, attackers may modify outputs without setting off conventional alerts.

These methods make it feasible for AI to:

• Create convincing phishing messages
• Propagate false information on its own to manipulate public opinion
• Lead people to do things that are dangerous

These AI-specific threats frequently seem real, so they go undetected until after harm is done.

When generative AI security risks are present, it is tougher to maintain data security. These AI systems do more than merely keep track of data. They learn from it, change it, and bring it to the surface in ways that are hard to predict.

AI Model Theft

The most severe security threats associated with generative AI don't often appear bad, but they are. There are no flashing warnings and no clear mistakes.

Instead, the harm occurs silently, occurring in normal-looking answers or minor changes in behavior that are simple to ignore. That is what makes these threats so hazardous.

Model inversion is a nice example. Attackers may get an AI model to show them bits of what it learned throughout training by using well-worded prompts.

For a threat actor, that information piles up over time and patterns become apparent. Sensitive or private information gets out.

From an AI security point of view, there is no problem with the coding. Instead, the safeguards cannot distinguish who is trustworthy and who is not.

I recall looking at access records on a system that hadn’t been “breached” in the usual way. There were no alerts and no logins that didn’t work. The system just had a sequence of strange, recurring questions that didn’t seem like typical usage.

It didn't appear dangerous at first. That was when we figured out what was going on. That’s how model theft usually happens.

Generative AI models are the result of years of hard work and a lot of money, but their openness may be exploited against them. Attackers may get a good idea of how a model works by methodically asking it questions over and over again without ever altering the underlying infrastructure.

No boundaries are crossed. Technically, nothing is taken. But valuable information keeps leaking out.

Prompt Injection and Modification of AI-Generated Content

Another misunderstood danger of generative AI is prompt injection. It doesn’t depend on viruses or malfunctioning systems. It depends on language.

An attacker just tells the system to do something different, which is usually not what the creators had in mind. Unfortunately, this tactic works all too frequently.

At its most basic level, prompt injection happens when user commands change or ignore the system’s initial regulations. A chatbot can be instructed to disobey safety measures or an AI assistant like Microsoft’s Copilot® could be persuaded to share its internal thinking.

An AI assistant is told to only make certain kinds of output. These assaults from attackers blend in because they sound like regular conversation. That’s what makes them so deadly in generative AI settings.

There are real-life examples all around us. Even though chatbots can do a lot, they have been misled into showing system instructions. AI helpers have been forced to provide sensitive answers.

AI assistants that are part of workflows have been pushed to do risky things only by changing the way a request is worded. When user inputs aren’t well regulated, they leave behind hidden attack pathways that standard defenses can’t perceive.

Social Engineering Attacks and Security Incidents

Social engineering has always targeted people, but generative AI makes these kinds of attacks far more effective. It's even possible to automate social engineering attacks.

Threat actors now use AI to write messages that sound calm, informed, and personal. Emails from these attackers feel legitimate, chat messages mirror real tone and timing, and small details are right.

That realism lowers defenses and increases the chance of a mistake. Many recent AI security incidents involving confidential data can be traced back to AI-assisted social engineering. Some examples include:

• Phishing campaigns generated at scale
• Fake support chats
• Impersonation that feels almost human

In several near misses, attackers didn’t break systems at all. They convinced users to do the work for them. That pattern shows up again and again.

What these incidents reveal is not just technical failure. They expose gaps in data governance and oversight.

AI tools were deployed quickly, often without clear ownership or risk assessment. Controls focused on infrastructure, not behavior. Training lagged and monitoring was thin. When something went wrong, teams struggled to understand how the AI had been used or misused.

Securing Generative AI Systems Starts with Stricter Access Control

It sounds simple to restrict access, but that is not the case with generative AI. These systems are at the heart of organizational processes, affecting code, data, and decisions all at the same time.

AI security concerns grow quickly when access is too wide. It’s now vital to give AI systems the least amount of access possible and use continuous monitoring. This type of monitoring helps to ensure that organizations and users are properly protected, especially when it's necessary to process sensitive data.

Access restrictions based on identity and data encryption help bring risks back to normal. They put limits on what users, services, and apps may do within generative AI systems.

AI software developers should carefully define the scope of prompts, model outputs, and configuration changes and create safeguards against threats. When used with solid identity management, these safeguards cut down on both unintentional and intentional exploitation.

Visibility is equally as important. Logging and monitoring users and AI systems provide security teams with the information they need to figure out how AI is really being utilized.

Security tools that keep an eye on access patterns, prompt activity, and system changes make it simpler to find and look into abuse. Auditability reduces the security risk.

AI-Generated Content Is Everywhere

AI-generated content is everywhere. As generative AI systems become faster, cheaper, and more convincing, the line between what’s real and what’s fabricated keeps getting thinner.

The hardest part isn’t creation, but trust. Many people struggle to distinguish legitimate outputs from malicious ones, especially when generative AI can mimic someone’s tone, writing style, voices, and even faces with unsettling accuracy.

Deepfakes and synthetic identities are no longer fringe security threats. They are practical tools for impersonation, fraud, and social engineering, often deployed at scale.

That directly impacts authentication and digital forensics. Traditional signals – visual cues, audio artifacts, and metadata – are less reliable when generative AI can polish them away. Investigators and security teams now face a world where attribution is uncertain and evidence can be convincingly fabricated, sometimes faster than it can be verified.

The Bachelor of Science in Cybersecurity at APU

For students interested in learning more about cybersecurity, the creation of AI systems, and the risks of generative AI, American Public University (APU) offers an online Bachelor of Science in Cybersecurity. In this program, students can study topics such as IT security planning and policy, cybersecurity, computer and network security, and biometrics. Other courses include cyber warfare, hardening operating systems, and red and blue team security.

For more information, visit APU’s information technology degree program page.

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