Understanding the Biggest AI Security Vulnerabilities of 2025

Artificial intelligence is now embedded in everyday business operations, from automating workflows to powering customer interactions and analyzing large datasets. But as adoption grows, so does the amount of sensitive information these systems process. This includes proprietary business data, financial records and personally identifiable information.

AI models learn from and depend on this data to function, which makes them an increasingly attractive target for cybercriminals. Attackers are always finding new ways to compromise AI platforms in order to steal data or manipulate outputs. To stay protected, businesses need to understand what their vulnerabilities are and how to secure their AI environments before they become points of failure.

The adoption of artificial intelligence across businesses has accelerated rapidly. According to one survey by McKinsey, 78 percent of respondents say their organizations use AI in at least one business function. This widespread integration spans various departments, with the most common deployments being in IT, and marketing and sales departments, followed by service operations.

However, many hackers are already taking advantage of new vulnerabilities introduced by these systems, as well as utilizing their own AI tools to target businesses more efficiently. For example, Darktrace has found that 74 percent of cybersecurity pros say AI-powered threats are already a major challenge for their organization.

Common issues include adversarial inputs that can cause AI systems to make incorrect decisions or leak sensitive data, while data poisoning can corrupt the training data, leading to flawed outcomes. As businesses continue to integrate AI into their operations, understanding and mitigating such security challenges becomes essential to protect organizational assets and maintain trust.

As businesses adopt AI to support operations and decision-making, they also inherit new categories of risk. AI systems process data differently than traditional software and introduce unique vulnerabilities that many organizations are not yet prepared to manage. Below are some of the most critical AI-specific data security threats businesses should understand and address:

This involves attackers crafting malicious inputs designed to trick AI models into making incorrect decisions. This can be used to bypass content filters, trick image recognition systems or disable fraud detection tools by manipulating how the model interprets data.

In these attacks, hackers target AI models in the development phase by inserting malicious data into training datasets to influence a model’s behavior. Hackers can use poisoning to introduce backdoors, cause misclassifications or reduce accuracy, often without immediate detection.

These techniques involve querying an AI model in ways that reveal sensitive information from the training data or the model itself. If successful, attackers may be able to make inferences about how the model was built, reconstruct private data or steal intellectual property embedded in the platform.

Prompt injection is a similar technique to model inversion, as it targets generative AI systems by inputting harmful instructions disguised as legitimate user prompts. However, the aim here is to manipulate the output in order to leak data or manipulation of the model’s intended behavior.

AI models are often accessed through exposed APIs that may lack strong authentication, rate limiting or monitoring. Indeed, according to research by Wallarm, 57 percent of AI-powered APIs are externally accessible, while 89 percent rely on insecure authentication mechanisms. Hackers can exploit these weaknesses to hijack requests, inject malicious payloads or overload the system.

AI systems present new risks not just because of what they do, but because of how they are built and used. For example, one major challenge is the lack of transparency and accountability. Many models operate as ‘black boxes’, which refers to systems where the internal workings are not visible. This makes it difficult for teams or even the system’s own developers to understand or explain how decisions are made. This reduces visibility and complicates risk management.

Models are also highly complex, often built on layers of data and logic that evolve over time. Their constant need for fresh input creates a large and dynamic attack surface, giving hackers more opportunities to manipulate outcomes or extract data.

The lack of standardized security practices also creates more problems for businesses. While traditional systems are often built with security by design, AI development is moving faster than most governance and policy frameworks can keep up with. This creates gaps in protection and leaves many organizations exposed to attacks they are not equipped to handle.

Securing AI platforms against cyberattacks requires more than patching to the latest versions and limiting access. Because AI systems behave differently than traditional software, organizations need to apply targeted strategies that reflect the way these models are built, trained and used. The following practices can help reduce exposure and make AI more resilient against modern threats:

Exposing models to adversarial inputs during development teaches them how to recognize and resist manipulation. Doing so helps build resilience against attempts to confuse or mislead systems through subtle changes in input data.

It’s important to deploy solutions that can detect unusual outputs, performance drift or unauthorized prompt responses in real-time. Continuous monitoring helps identify manipulation or misuse early, before it results in compromised data or corrupted outcomes.

Restrict access to training datasets, APIs and deployed models using role-based controls, multifactor authentication, encryption and regular auditing. Limiting who can interact with or modify AI systems reduces the risk of insider threats or hackers using stolen credentials to interact with systems.

Conduct exercises such as penetration testing tailored to AI in order to look for specific weaknesses in areas like prompt handling, model responses or inference behavior to identify areas that could be exploited by attackers.

AI models require their own focus when it comes to data governance and responsibility. Establish clear oversight and accountability for AI risk, including documentation of training data sources, approval workflows and model changes. Embedding governance makes it easier to respond to incidents and meet regulatory expectations.

AI models are increasingly coming under scrutiny from regulators, with their large-scale handling of sensitive data a particular concern. Regulations like GDPR apply fully to AI systems, especially when handling personal or sensitive customer data, while dedicated rules such as the EU AI Act will set out specific rules for how systems must operate, including what data can and cannot be used, and how this should be protected.

Using sensitive information to train or operate AI without proper safeguards can result in ethical breaches and regulatory penalties. This includes bias, lack of explainability and improper data retention. Businesses must therefore always ensure data is collected with clear consent and their use of AI respects privacy rights.

Accountability and ethics must be built into every stage of AI development and deployment. As these systems take on greater responsibility in business processes, strong governance, clear documentation and ethical practices are essential. These considerations should sit at the core of every data protection and security strategy going forward.