Top AI security risks every business should know in 2025

Artificial Intelligence (AI) has become intertwined with our daily lives. Not just across the personal front, but for businesses across domains as well. From simplifying complex tasks to automating mundane processes, they have become an indispensable tool that drive a real difference for businesses. According to a report by McKinsey, about 88% of organizations have implemented AI in at least in one of their business functions.

But this rapid, widespread prevalence also brought hidden liabilities which most business organizations and AI-users are unaware of. The same AI which brings many advantages can also be the reason for certain implications if not secured or utilized responsibly. If you are using AI for your business, it is important to know what AI security risks are and how they affect your business. And that is what we shall discuss in this blog.

Secure Every Endpoint from the evolving landscape of AI security risks.

What Does AI Security Mean for Businesses?

Automating or leveraging tools to enhance workflows has never been new for businesses. Earlier, it was Microsoft Excel. A tool which changed the landscape of how people organized, analyzed, and visualized data. Today, AI is the new player in the market that has dynamically changed the perspective of how businesses fundamentally operate. In this scenario, AI security emerges as a critical discipline.

AI security is the practice of protecting AI systems, their data and usage environment, while safeguarding businesses from the operational, compliance, ethical, and privacy risks that emerge when AI is used in real business context.

Whether you are planning on adopting AI systems for your business or building one yourself, understanding this security landscape gives you the necessary edge to safeguard and protect your business integrity.

The Top AI Security Risks Businesses Face in 2025

As AI becomes indispensable for business operations, understanding the threats and risks they bring in have become a necessity. Even the most sophisticated and virtually secure AI systems remain susceptible to security threats. And when we talk about AI security risks, we are essentially dealing with two critical aspects:

• Risks to AI systems themselves, which revolves around the AI models, algorithms, and infrastructure that can be manipulated, corrupted, or exploited.
• Risks from using AI, which revolves around how businesses interact with AI, provide data, and analyze information, that when not managed properly can lead to data leaks, compliance breaks, and unintended breaches.

Risks to AI Systems (Core model, Algorithms, and Infrastructure)

Let’s first understand the threats that directly target the AI structure itself. These threats are engineered to corrupt how the model learns, decides, and behaves. These risks are not easily identifiable and reside deep with the workflows, slowly causing damage to the model and subsequently, the businesses.

Data Poisoning

Data poisoning occurs when attackers inject harmful or incorrect data into the training data set used by AI models. The “ConfusedPilot” attack test by University of Texas researchers shows how this works. The researchers demonstrated how injecting malicious data into the documents used by AI systems like Microsoft 365 Copilot could cause the AI to produce false information, even after the poisoned data was deleted. As for businesses, poisoned AI-models could lead to reputational damages and operational disruptions.

Model Inversion and Extraction

In model inversion, attackers reverse engineer the model’s internal logic or training data by repeatedly querying it. Model extraction takes it a step further by duplicating the model itself through extensive input querying and output probing. This model-related challenges are especially concerning for businesses that deploy proprietary IP solutions or handle sensitive data, leading to exposed data and loss of integrity.

Prompt Injection Attacks

A type of social engineering attack, this type of threat targets AI models that rely on natural language instructions (specifically LLMs). Attackers plant malicious prompts or hidden commands in search queries or while training which manipulates the system into giving information they need or mislead LLM output. These attacks compromise trust and data confidentiality, making them particularly dangerous for businesses that integrate AI into customer support, coding tools, or document analysis systems.

Backdoor Attacks

Backdoors are loopholes created intentionally or unintentionally by developers, which attackers use as an entry point to gain unauthorized access, steal sensitive data, or perform ill activities. These backdoors can occur at software, hardware, or at network-levels. And these threats remain undetected for a longer period of times slowly corroding the integrity of the AI model and leading to loss of data.

API Exploits

APIs are the backbone of modern software. They act as a bridge to fetch information and communicate between client and server. In certain cases, they become a key target for attackers to exploit AI systems. A business with a single vulnerable API can cause severe backdoor into entire data, giving attackers the entry they needed to get into business-critical systems that can potentially result in large scale data breaches.

These common security risks to the AI systems clarify that even technology can be weaponized in a variety of ways. Not just attacking the AI model but injecting harmful data, targeting APIs, and compromising the algorithm. But the picture is only half complete. The below-explained category of risks doesn’t target the internal systems, rather it emerges from how organizations use AI.

Risks from Using AI (Human-AI Interaction and Operational Misuse)

Unlike risks to AI systems which seem to appear later and longer in the system, a more frequent and damaging risk comes from how businesses use AI. Even secure models when used incorrectly or irresponsibly, from everyday task simplification to content generation, can open pathway to data leaks, compliance failures, and misinformation. These threaten the integrity of businesses far worse than the security risks to the AI themselves.

Data Privacy Risks

AI systems rely on input data to understand the context, answer questions, and deduce conclusions. Sometimes this data can be personal, sensitive or confidential. When fed into public systems or unmanaged AI tools, they can be stored, reused, or even surface later. This is what happened with Clearview AI, which illegally held billions of facial databases from social media and internet and faced severe consequences. In a general business context, this risk can have an adverse effect on the intellectual properties, even in well intentioned cases, and can lead to compliance violations and damaged customer trust.

Bias and Discrimination

AI models are trained on historical data sets. And just like with human biases, AI models trained on biased data can also reflect the same. This could lead to polarization, favouritism, and prioritization by swaying to a personal opinion, rather than logical and reasonable outcomes. A notable example of this risk and its effects is when Amazon’s AI recruiting tool proved a bias against women, which led to severe ethical backlashes, reputational damages and a hit on the brand integrity.

Hallucinations and Misinformation

AI-generated outputs are not always factually right. Sometimes, they do provide inaccurate or misleading information, in a rather convincing way. This is referred to as “hallucination.” When users blindly trust AI and the answers they provide as a final word or for decision-making, this can result in them acting on inaccurate information without proper human oversight or verification. A serious business implication occurred with Air Canada when its chatbot provided falsified information to a passenger, leading to severe legal issues and loss of customer trust.

Lack of Transparency, Accountability, and Explainability

AI systems provide information on what they synthesize, either from the information they are trained, information fed, or from the context provided. But, often, this becomes a question about the accountability. Thoughts like “Where is this answer coming from?” or “Is this factually right?”, become a key differentiator when making business decisions. And AIs “black box” behaviour adds to this risk, where users can only perceive the input and output but not the decision-making transparency.

Social Engineering and Manipulation

Social engineering is a malicious tactic that uses psychological manipulation to deceive people into giving up sensitive information and compromise security. Ever since the advent of AI and gen AI, these tools have become a key contributor for such attacks like phishing, deepfakes, and honey trapping. Today, businesses face this heightened risk of social engineering. One study suggests that about 82% of phishing emails are AI-generated, making it harder to detect. If users are not responsible or aware, this can lead to identity fraud, significant financial loses, and reputational compromise.

Mitigating Risk at the Human-AI Interface

In most cases, AI security risks don’t emerge from technical errors alone. They emerge at the points where employees interact with AI tools/platforms, devices connect to AI systems, and workflows integrate with AI models. This space is often overlooked and is easy to exploit. Sometimes an unmonitored plugin, a misconfigured device, or a user providing confidential data to an open chatbot becomes an unseen gateway for threats to seep in and harm the business. IT admins would ideally be tasked with mitigating this risk factor, but when AI usage scales across hundreds or thousands of endpoints, managing, monitoring and keeping the integrity at check becomes an ordeal.

Ensuring safety among numerous endpoints needs a unified solution. One that can provide the visibility, control, and policy enforcement to protect every device, user, and application in the ecosystem interacting with AI.

A Unified Endpoint Management solution like Hexnode.

Strengthening AI Security in Endpoints Across the Businesses with Hexnode

Endpoint Visibility and Compliance Monitoring

In a business setup with multiple endpoints and convoluted workflows, it is a challenge for IT admins to keep track of devices and see which AI applications are installed, what permissions they hold, and how they’re being used. Hexnode helps admins have complete visibility over the endpoints to identify unauthorized tools and integrations and mark them non-compliant before they become a threat.

Application and Website Control

The increasing integration of AI into business workflows means users might resort to unauthorized AI platforms, browser extensions, or apps to quickly complete their tasks. Hexnode allows admins to blocklist or allowlist applications, extensions, or platforms, providing authorized and acceptable usage of AI tools aligning with internal policies.

Data and Network Safeguards

Protection against AI risks is not limited to safeguarding apps and websites. It must also address the network and data connectivity which exposes the endpoints. Hexnode strengthens this layer of security by enabling the IT team setup and configure private VPNs, APNs, authentications, and restrictions. Together, these controls prevent sensitive data from leaking between managed and unmanaged environments, even when users interact with AI tools.

Policy Enforcement and Access Management

Hexnode allows for complete granular control of access for the tools, APIs, and configurations depending on the actual necessity of the tool for device or user groups. This means that IT admins can grant access and permission to users who need such tools and withhold access for the rest.

Inherent AI Feature Restrictions

Apart from external apps and platforms, some operating systems offer built-in AI features like Siri inherent with them. Hexnode enables IT teams to disable or restrict these AI-powered features on managed devices, reducing the risk of unintentional access and usage.

Patch and Update Management

Outdated operating systems, missing security patches, misconfigurations and unsecured data sync becomes an easy target for hackers to exploit the vulnerabilities. The same can be said when using AI tools and platforms. With Hexnode, admins can keep track of and control OS-level and app updates and make sure patches are properly managed to mitigate vulnerabilities arising due to security gaps.

Whitepaper

The Cybersecurity Blueprint: How to adopt the right cybersecurity strategy for your business

Adopt the right cybersecurity strategy for your business. Learn how to manage the chaos of data security, choose the right framework, and implement a complete security blueprint within your organization.

Get the Whitepaper

AI Security Risk Management is a Collective Responsibility

While UEM solutions like Hexnode strengthen the operational layer by bringing visibility and control of devices and workflows, they alone cannot be the solution to the entire AI security risk management. It also extends to people, processes, and the organizational culture, making it more of a collective effort than one’s individual enforcement.

As AI interacts with every part of the business and must be managed collectively across roles.

• Leadership and management: Leaders define the organization’s AI vision, ethical boundaries, and acceptable use guidelines. Their direction sets the tone for responsible, risk-aware adoption of AI across departments.
• Security and IT teams: IT teams translate that vision into control by monitoring usage, managing endpoints, approving tools, and ensuring AI systems operate within secure compliant restraints.
• Users: They form the first line of AI interaction and security defence. Their awareness of data security, responsible usage, and validation of AI outputs significantly influences the organization’s overall risk posture.

In addition to everyone’s roles, to support this shared responsibility, organizations need structured mitigation practices like:

• AI Usage Policies: Set clear guidelines on what data can be shared with AI tools and which platforms are approved for work use.
• Awareness Training: Equip employees to recognize data sensitivity, AI limitations, and risks like hallucination or misuse.
• Continuous Policy Review: Conduct regular audits for AI tools, workflows, and endpoints to ensure they align with evolving regulations and business needs.
• Incident Response Preparedness: Establish a clear protocol for handling AI-driven errors, data leaks, or misbehaviours before they escalate. Hexnode provides this capability with its new XDR solution, to help businesses stay a step ahead of ever-evolving cyber risks.
• AI Output Validation: Make sure critical content is reviewed or verified before use in decisions or customer facing materials.

Wrapping Up

AI adoption, despite its advantages, introduces significant security risks at the point of usage. Data leakage, misuse of unapproved tools, inaccurate outputs, and compliance often arise from how they are accessed, and applied inside the organization.

However, these risks don’t imply limiting innovation, but to enforce better governance and control across endpoints. To achieve this, UEM solutions like Hexnode provide the essential operational support by managing the endpoints. It ensures that sensitive data is protected, only approved AI tools are accessed, and business integrity is maintained, thereby turning the uncertainty of AI usage into a managed, compliant process.

Frequently Asked Questions (FAQs)

1. What are the top AI security risks business face in 2025?

The biggest AI security risks include data poisoning, prompt injection, and model manipulation which directly compromise the AI model’s integrity. On the usage side, businesses face data privacy violations, hallucinations, and bias when AI tools are not used responsibly or deployed without governance.

2. What are the main security risks associated with Generative AI in businesses?

Using generative AI tools for businesses can unintentionally expose sensitive data, reproduce copyright material, or generate misleading or inaccurate content. When employees feed confidential data into public models, that data can be stored or reused by the provider, creating major data privacy and compliance risks.

3. How can businesses mitigate misuse of AI tools by employees or departments?

Businesses can mitigate misuse of AI tools by implementing clear AI usage policies, providing regular awareness training, enforcing endpoint control via UEM tools, and continuously reviewing how AI is used for business.