Before we get into the risks, let's quickly define generative AI or GenAI. GenAI is a type of AI that generates content like text, code, and images using machine learning (ML). Tools like ChatGPT and Google Gemini are everyday examples. GenAI makes content based on patterns in its training data, meaning two similar inputs can produce very different results. 

GenAI security risks are the threats that emerge from how these tools are used. This applies to your own employees as well as threat actors looking for an opening. 

AI security concerns vs. AI security vulnerabilities

These terms get used interchangeably, but they describe different problems. 

AI security concerns focus on how people within your organization use AI tools. When an employee pastes a client contract or internal source code into a public AI tool, they're creating a data exposure risk without realizing it.

Models might use this sensitive input as training material. Then, internal documents might show up as responses when people outside your organization use the same AI tool. Security policies haven’t kept pace with adoption, and that gap is where most real-world risks live. 

AI security vulnerabilities are weaknesses in the AI systems themselves. Hackers can modify AI systems so they expose private information or produce skewed, inaccurate results.

How can generative AI be used in cybersecurity by defenders and attackers?

On the defensive side, security teams use AI to process log data faster and cut the false positive noise that pulls analysts away from real threats.

On the offensive side, threat actors use generative AI to execute attacks faster and more convincingly. Phishing emails that once required hours of manual research can now be generated at scale and tailored to specific targets.

Most generative AI security focuses on sophisticated model-level attacks. But the threats that hit organizations are usually simpler. Let’s take a look at four of the most common vulnerabilities companies face.

Shadow AI and uncontrolled tool usage

Shadow AI refers to employees using AI tools that IT never approved and has little or no visibility into. ChatGPT, Claude, and dozens of other tools are free and available to anyone with a browser, so the issue is widespread.

The problem is that in many cases, IT has no way to see what data is going in or coming out. There are no access controls and no audit trail. From a security standpoint, an unsanctioned AI tool connected to your organization's data is an open door that IT doesn’t know exists. 

Sensitive data exposure through AI prompts

When employees use AI tools to speed up their work, they often paste in whatever context the tool needs to give a useful answer. That might be a client contract, internal financial data, or even patient records. It feels no different from emailing a document to a colleague, but the privacy implications are completely different. 

Data entered into many public AI tools can be used to improve future model outputs or stored in ways outside your organization's control. Many employees have no idea this is happening, and that lack of awareness is one of the ways AI-related data breaches start.

AI-enhanced phishing & social engineering

Traditional phishing is a numbers game. Threat actors send thousands of generic emails and count on a small percentage landing. Generative AI changes that. According to our Huntress 2026 Cyber Threat Report, generative AI has become a force multiplier, putting sophisticated attack tools in more peoples’ hands. Threat actors can now produce convincing, personalized emails at scale without the typos or awkward phrasing that security awareness training teaches employees to look for. 

Deepfake voice and video calls take this further. In 2024, Hong Kong police reported a case in which a finance employee was deceived by deepfaked colleagues during a video call into wiring roughly $25M to fraudsters. These attacks don't require a sophisticated threat actor; they just require accessible tools and a specific target. 

Prompt injection & AI-generated malicious content

Prompt injection occurs when someone hides hidden instructions in content that an AI system will process, tricking it into doing something it was never meant to do. A malicious document or email might instruct an AI assistant to share sensitive data or bypass policy checks. 

Generative AI also lowers the bar for creating malicious AI content. Writing functional exploit code or generating malware variants once required technical skill. With the right prompting, an AI tool can produce in minutes what previously took hours, putting capabilities within reach of far less sophisticated threat actors than before.

The generative AI security risks that hit organizations don’t require a purpose-built AI security tool. Here are a couple of strategies for stopping AI security risks in their tracks.

Establish AI usage policies & employee training

The first step is knowing what AI tools your organization is using. A simple policy that defines which tools are approved, what types of data can and can’t be entered, and who to contact with questions gives your team clear guardrails and a point of contact when they’re unsure.

That policy only works if people know about it. Weaving AI-specific risks into your existing security awareness training is the most practical way to close the gap. Employees don't need a technical deep dive. They need to understand that pasting client data into a public AI tool carries real risk.

Use a managed security platform

Most firewalls weren't built to catch an employee opening a browser and pasting data into ChatGPT. The connection appears to be normal web traffic. Nothing triggers an alert. That's the visibility gap where shadow AI risks live and why log correlation matters.

Huntress Managed Security Platform pulls log data from endpoints, identity systems, firewalls, VPNs, and other infrastructure into a single view. Then, it applies Smart Filtering so the Huntress SOC can focus on security-relevant events instead of raw noise. 

Managed SIEM handles the log correlation layer, while Managed EDR and Managed ITDR cover endpoint and identity activity. Teams see the full picture, not just one slice of it.

By correlating that telemetry over time, Huntress can spot behavioral patterns that don’t line up with a user’s normal activity—for example, a spike in access to sensitive data followed by new connections to unsanctioned cloud or AI services—so potential shadow AI usage or data-exfiltration paths show up as investigation leads instead of staying buried in individual logs. This kind of behavioral analytics (often described as UEBA) focuses on deviations from baseline activity rather than static signatures. 

The difference with Huntress is that your team doesn’t have to write correlation rules or review reports by hand. Instead, the human-led, AI-Centric SOC tunes detection, triages suspicious activity, and escalates only clear, actionable incidents with response recommendations.

Generative AI is evolving faster than most security policies, and the risks that matter most aren't always the ones making headlines. 

You don't need a purpose-built AI-only security product to address these risks. But you do need clear AI usage policies plus visibility into what's happening across your environment and a team watching those signals around the clock.

Huntress gives your team exactly what they need:

• Visibility across endpoints, identities, and logs

• Behavioral analytics that surface what individual tools miss

• A 24/7 human-led SOC that spots patterns