Generative AI security risks every enterprise should prepare for
Over time, we have become increasingly accustomed to using AI in our daily work. We send customer data to ChatGPT for analysis, share customer emails to draft responses, or ask Claude to generate code for enterprise applications.
All of this can happen within an enterprise environment.
This is what generative AI is designed to do. It creates content for us, from text and code to images and videos. In enterprises, generative AI can help complete tasks faster and automate them, helping employees be more productive and spend less time on repetitive work.
However, becoming too reliant on generative AI can also introduce risks. Without realizing it, we may overlook one important aspect: generative AI security. Despite its many benefits, generative AI can also introduce a range of security risks to organizations.
Generative AI security focuses on protecting applications and organizations from risks associated with generative AI and ensuring that AI is used responsibly as intended. The goal is simple: to help organizations gain the benefits of AI while minimizing the security risks that can come with it.
Why does generative AI create new security risks for enterprises?
The way we use generative AI can introduce security risks, especially when AI becomes part of everyday workflows. The risks can come from the content generated by AI, the information shared with it, or the systems and services connected to it.
For developers, generative AI can help them write code faster and at scale. But when developers ask AI to generate code, it may recommend code that contains vulnerabilities. Prompt injection attacks can also occur, causing AI to generate dangerous or insecure code. Furthermore, due to the overreliance on AI, developers may fail to adequately review the code generated by AI. They may simply trust the output and use the code as it is.
Other employees outside of development roles may face similar risks. When using AI tools, they may enter sensitive information into prompts or upload confidential files without realizing the potential security implications. If AI models or applications process sensitive data, they may mistakenly disclose that information. This exposure can lead to privacy violations and issues related to data protection and sovereignty regulations.
If we take a look at the big picture, generative AI can also introduce new attack vectors. Generative AI does not work in isolation, it integrates models, data, applications, APIs, users, and third-party services. This increases the number of components that organizations need to secure, increasing the potential for data breaches or sophisticated cyberattacks.
Why generative AI security is fundamentally different from traditional security
With traditional security, we control the infrastructure. We control who can access our systems, what data they can see, and where data is stored. We build firewalls, we monitor networks, and we manage identities.
With generative AI, we don't have the full control we have with traditional security. We cannot always predict what output AI will generate and what the AI model will do with the information we give. If an employee types a prompt into ChatGPT, we do not control what ChatGPT does with the prompt, how long they keep it, and what the output will be.
This kind of unpredictability introduces a layer of uncertainty. AI might expose sensitive information in ways we did not anticipate. It might be manipulated by a prompt that looks innocent.
We can say that with generative AI, we have to secure the input, secure the output, and secure the model itself. This is why generative AI security requires a new approach.
Why generative AI security is becoming so urgent
Enterprise adoption of generative AI is accelerating. According to McKinsey's survey from 2025, 79% of organizations worldwide are now using generative AI in at least one business function. That's up from just 33% in 2023.
However, even though adoption is happening everywhere, security and governance are not always keeping up with it. IBM's report in 2026 found that among organizations that experienced an AI-related breach, 92% lacked proper AI access controls.
This is concerning because security risks related to generative AI are constantly evolving and increasing. In 2026, IBM's research revealed that AI-related breaches increased from 13% to 21%, a 61% increase from the previous year. This rise also came with significant financial consequences. According to IBM, prompt injection attacks, which is one of the most common AI security threats, cost $5.89 million on average. Incidents involving shadow AI also more than doubled, increasing from 20% to 43%, and resulted in an average breach cost of $5.39 million.
These findings show that as AI becomes more deeply integrated into business operations, organizations need to strengthen security, access controls, and governance alongside AI adoption.
Where generative AI security risks can enter
Generative AI does not work on its own. It relies on different components that can introduce security risks if not properly secured and monitored.
1. Models and model providers
When enterprises use AI models from external providers like OpenAI, Google, Anthropic, or others, they depend on those providers to secure the models and protect the data. But enterprises have limited visibility into how these providers operate.
If a model provider has a security issue, it could affect all organizations using that model. Additionally, if the model itself has vulnerabilities, it can be exploited by attackers to generate malicious content or expose information.
2. Training and retrieval data
The data used to train AI models and generate responses must be properly secured. If sensitive information gets into training data, it can eventually surface in AI outputs. For example, if customer data or source code is included in training datasets, an attacker could craft a prompt that causes the model to reveal that information.
Data also needs to be classified to identify what is sensitive and what is not. Sensitive data should then be anonymized or excluded from training datasets.
3. APIs, plugins, and AI agents
Many enterprises connect generative AI to their internal systems through APIs and plugins. This allows AI agents to perform tasks on behalf of users, but it also introduces new security challenges.
If an API is not properly secured, an attacker could manipulate it to access unauthorized data or perform unauthorized actions. If an AI agent is given too many permissions, a compromised prompt or malicious request could cause serious damage.
4. Open-source libraries and frameworks
Generative AI applications are built using open-source libraries and frameworks. While these can speed up development, they may also introduce vulnerabilities if components are outdated, poorly maintained, or have known security flaws.
5. Third-party infrastructure and services
Generative AI applications often rely on third-party infrastructure and services, such as cloud platforms, model hosting providers, databases, and external APIs. Security issues in these services can affect the availability, confidentiality, or integrity of the AI applications and data that depend on them.
Common generative AI security risks
As the adoption of generative AI in enterprises grows, so do the security risks associated with it. Here are some common generative AI security risks that enterprises may face today:
1. Prompt injection attacks
This is one of the most common AI security threats. In a prompt injection attack, an attacker inserts malicious instructions into prompts to manipulate the model into generating unintended or dangerous outputs that do not align with the model's intended behavior.
2. Sensitive data exposure
Employees may input sensitive data while they interact with AI tools. For example, an employee may include customer information in a prompt without fully understanding how the information will be processed or stored. This creates data exposure risks.
3. Data poisoning
Data poisoning occurs when attackers intentionally introduce inaccurate, manipulated, or malicious information into datasets used to train or improve an AI model. This will eventually corrupt the training data set, leading to biased or unexpected model behavior.
4. Model and third-party dependency risks
Enterprise AI applications may depend on models and services provided by external vendors. They may also rely on third-party APIs, hosted AI platforms, or other external components. A security issue affecting one of these dependencies could potentially affect the applications and data connected to it.
5. Insecure APIs and plugins
Employees use APIs and plugins to connect generative AI applications with other systems and expand their capabilities. However, poorly secured integrations can create additional entry points for attackers. Weak authentication, inadequate input validation, or improper authorization could allow attackers to manipulate requests, access information, or interfere with AI-related processes.
6. Excessive permissions and unauthorized actions
Some AI applications and agents need access to enterprise systems or data to complete tasks on behalf of users. Giving them broader permissions that are unnecessary can increase the potential impact of a compromised account or malicious request.
7. Vulnerable open-source dependencies
Open-source libraries and frameworks are commonly used to build and operate AI applications. While they can speed up development, they may also introduce vulnerabilities if components are outdated, poorly maintained, or compromised.
8. Insecure AI-generated code
Developers use generative AI to write code faster, but the code it produces may contain security flaws or weaknesses. The code may include improper input validation, insecure configurations, or outdated dependencies that can introduce vulnerabilities into an application.
9. Shadow AI
The unauthorized use of AI tools by employees within an organization without the oversight of IT or security teams is known as shadow AI. Even with good intentions, for instance, to improve productivity, shadow AI might reveal private information and cause problems with compliance.
10. Unreliable outputs
Sometimes AI generates content that is false, misleading, or completely made up.
In an enterprise context, if employees rely on incorrect information to make business decisions, or if AI generates false outputs that are used in critical systems, it can cause real business harm. This also creates security risks if hallucinated information is used in access control decisions or security policies.
How to secure generative AI in the enterprise
Managing generative AI security risks requires organizations to apply security controls across the entire AI environment. This includes controlling who can access AI apps, protecting sensitive data, and maintaining visibility into activities across the environment.
1. Establish visibility into generative AI usage
Just like every other IT infrastructure, enterprises also need to have full visibility into generative AI usage. They need to know what AI tools employees are using, who is using them, what prompts are being submitted, how much data is being transferred, and whether employees are using unsanctioned AI apps.
Enterprises should also be able to identify unusual generative AI activity, such as the use of AI tools outside normal business hours. This way, security teams can identify shadow AI and understand how generative AI is being used across the organization.
2. Protect sensitive data shared with AI
When working with generative AI tools, employees may unintentionally share confidential information. Once this sensitive information is shared with an external AI service, organizations may have limited control over how that information is processed, stored, or retained.
Enterprises must then establish clear policies that define what information can and cannot be shared with AI tools, monitor data transfers, and apply controls to prevent sensitive information from being sent to unauthorized AI applications. Regular data audits and appropriate security measures, such as encryption, can also help protect sensitive data.
These controls also need to be supported by employee awareness. Enterprises should provide regular training to help employees understand how to use AI safely and responsibly, so they will know what information should not be shared with AI tools.
3. Control access to generative-AI-related tools
Generative AI applications and AI agents may need access to enterprise systems, applications, databases, or other resources to perform tasks on behalf of users. However, excessive permissions can increase the impact of compromised accounts, malicious prompts, or unauthorized actions.
Organizations also need to manage who can access and use generative AI applications. Access should be limited to authorized personnel based on their roles and business requirements.
If possible, enterprises should apply the principle of least privilege by providing users, AI applications, agents, and other identities with only the access required to perform their intended tasks. Privileged credentials and access to critical systems should also be monitored and reviewed regularly to identify unnecessary or outdated permissions.
4. Detect suspicious activity and security incidents
Even with preventive controls in place, enterprises still need to detect suspicious activity that may indicate an attack or compromised system. This is particularly important when generative AI applications interact with enterprise systems, users, and sensitive data.
Security teams should collect and analyze relevant security events across endpoints, applications, users, and other infrastructure. Correlating events from different sources can help identify unusual patterns that may otherwise go unnoticed.
5. Manage vulnerabilities in AI-related infrastructure
Security risks do not come only from the AI model itself. Generative AI applications also depend on operating systems, software libraries, frameworks, applications, and other infrastructure. Vulnerabilities in these components can create opportunities for attackers to compromise the systems supporting AI workloads.
Organizations should also maintain an inventory of the software and infrastructure used in their AI environments, regularly assess them for known vulnerabilities, and apply security updates when necessary.
How ManageEngine supports enterprise AI security
ManageEngine provides solutions that help enterprises implement the controls in generative AI security. Here is how:
Monitor generative AI usage and shadow AI:DataSecurity Plus provides centralized monitoring across all the major generative AI platforms employees use, such as ChatGPT, Claude, Google Gemini, and others. It allows enterprises to monitor generative AI usage, including the AI applications employees access, prompts they submit, data transfer activity, and unsanctioned generative AI applications.
Protect sensitive data shared with AI: DataSecurity Plus helps enterprises monitor data shared with generative AI applications and enforce controls over cloud and AI application usage. Its generative AI insights reports provide visibility into prompts, users, applications, bandwidth usage, and activity outside business hours.
Control privileged access:PAM360 helps enterprises manage privileged access to critical systems and resources. It supports granular access controls and least-privilege practices.
Detect suspicious activities and manage vulnerabilities: Log360 provides SIEM capabilities for security monitoring, threat detection, behavioral analytics, and incident response across the IT environment. Meanwhile, Vulnerability Manager Plus helps identify and remediate vulnerabilities in operating systems and third-party applications that support generative AI environments.
Understanding generative AI cybersecurity risks does not mean that enterprises should stop using generative AI. On the contrary, adopting AI can give enterprises a competitive advantage, especially when it is used safely and responsibly. Implementing these ManageEngine solutions can help enterprises put the right security controls in place while continuing to make the most of generative AI.