Top 12 AI Security Risks You Can’t Ignore

Generative AI became a mainstream phenomenon in 2022-2023 with the public launch of ChatGPT by OpenAI. Since then, AI has been adopted across various industries, used for everything from generating dad jokes to translating code between programming languages.

While AI offers significant benefits in terms of time-savings, cost reduction, and innovation, it also introduces new security risks. The question for businesses is not “if” but rather “when,” “where,” and “which type” of vulnerability AI is creating in your IT ecosystem. Often, there is insufficient oversight and control over the data that fuels language learning models (LLMs) and their implementation. 

But all hope isn’t lost. By understanding the different types of AI security risks, the vulnerabilities and biases present in datasets, and best practices for mitigating those issues, you can significantly reduce the threat to your businesses.

Top 12 AI security risks

Just as we’re applying AI to make our businesses faster and more efficient, so too are threat actors. AI threats are more varied and advanced, and can occur at a significantly increased volume than we’ve previously seen. To protect your business, you must first become familiar with the different types of AI security risks.

#1 Adversarial attacks 

Adversarial attacks occur when threat actors manipulate inputs to cause the AI systems to misinterpret or misclassigy them. These subtle tweaks — often imperceptible to humans — can fool AI models into producing incorrect or even harmful outputs. For example, adding noise to an image might cause a facial recognition system to misidentify someone. 

#2 Data poisoning

Data poisoning happens when threat actors corrupt the training data used to build AI models, whether through user data, open source datasets, or machine learning research, to alter the model’s future output.

#3 Inference attacks

Inference attacks involve using strategic prompts to infer sensitive information about individuals or datasets. This can include deducing private attributes or reverse-engineering statistical characteristics of training data.

#4 Model inversion attacks 

Model inversion attacks involve reconstructing sensitive data by analyzing a model’s output. With enough queries, attackers can extract representative samples of individuals from the training set — including potentially private or identifiable attributes.

#5 Model theft

Sometines, the AI model is just as valuable as the training data (if not more so). In a model theft attack, bad actors attempt to steal intellectual property (IP) by hacking proprietary code repositories or by reverse-engineering the model via systematic queries.

#6 Lack of explainability  

AI models often have complex processes and decision-making mechanisms that are difficult to understand. This lack of explainability makes it hard to trace the logic and verify the accuracy of the model’s output.

#7 Shadow AI

Shadow AI refers to AI-enabled tools that employees or departments adopt without IT oversight. In these cases, employees are trying to gain insights or automate mundane tasks while unknowingly bypassing security controls and‌ exposing sensitive data.

#8 Supply chain risks

AI-based risk is also prevalent throughout third-party supply chains. If an organization works with a vendor using an AI model that’s at risk for one or more of the risks on this list, any negative repercussions could trickle down to the organization itself. 

#9 Deepfakes and AI-powered social engineering

Threat actors now can use generative AI to create convincing deepfakes, voice clones, and synthetic personas to manipulate victims. Combined with AI-assisted phishing tactics, these techniques drastically increase the scale and sophistication of social engineering attacks.

#10 Data leakage

Data leaks commonly occur when sensitive data is inadvertently exposed by the model’s output, behavior, APIs, or logs. These leaks can occur during training, deployment, or usage and are prime targets for threat actors.

#11 Privacy violations

Privacy violations can occur when an AI model collects, processes, stores, or analyzes sensitive data without sufficient security measures in place. Many of the risks above could result in a privacy violation as defined by industry standards and regulations. 

#12 Code vulnerabilities

In software or application development, AI can speed up the development process, identify vulnerabilities, and make security suggestions. However, AI-generated code snippets may contain common weakness enumerations (CWEs) from open-source code, and AI models often fail to account for contextual nuances and interdependencies, leading to errors and security vulnerabilities.

Challenges in AI systems

Despite their capabilities, generative AI tools aren’t infallible. They reflect the assumptions, gaps, and biases of the data and people who built them.

For example, AI hallucinations — when a model generates information or data that was not explicitly present in its training data — are a known issue. AI models are programmed to provide an answer, regardless of whether that answer exists in their training data, which can result in inaccurate or misleading information. This problem is compounded by the “lack of explainability” mentioned above. It’s nearly impossible to retrace the model’s steps to understand how it came to its conclusions, making it difficult to verify the output. 

Consider a financial institution that uses AI to automate mortgage approvals. If a frustrated customer calls the organization to dispute their rejection, the staff would have little or no way to explain or justify the reasoning behind the rejection. This decision could prevent the customer from purchasing a new home and cause them to seek a loan elsewhere.

Privacy concerns related to AI 

Privacy concerns and AI security risks are closely related. AI models are trained on vast datasets, containing individuals’ sensitive data, proprietary data, IP, and unmanaged open source data. How this data was gathered, what it’s being used for, and how it’s being stored all have privacy implications.

Aside from malicious attacks, such as model inversion or model theft attacks, the presence of shadow AI poses significant privacy concerns. Research shows that 15% of employees paste company data into ChatGPT, a quarter of which is categorized as sensitive.

There are also instances when AI models unintentionally collect individual user data, which can be integrated into their LLM and/or stored in an insecure location. Or, an individual may have consented to sharing their data for one purpose, but if the AI model owner goes on to repurpose that data for another use, they may be violating the user’s privacy.

Bias and ethical considerations

AI models are simply pattern recognition and prediction tools at their core, meaning training data quality significantly impacts output. An AI tool trained using New England rainfall patterns, for example, wouldn’t be much help to a farmer in Arizona optimizing his planting schedule.

However, the implications of training data go far beyond output usability. Who the data represents, how it was collected, when it was collected, and what values were assigned to various data points can unfairly skew the LLM’s predictions. Unfortunately, our societal biases are often reflected in the data we generate. For example, if an AI model that diagnoses a specific condition was trained only on the data of young men, it would likely fail to recognize symptoms in women or patients over a certain age.

If the mortgage approval tool from the example above was trained on historic loan data, without adjusting for socioeconomic or legislative factors, it could associate low-income neighborhoods with increased default rates. As a result, the tool could unfairly reject applicants from these areas, regardless of their credit score and debt-to-asset ratio. 

Those building and utilizing training models have an ethical responsibility to be conscious of and do their best to mitigate these biases.

AI security measures and best practices 

As AI becomes more integrated into our workflows and infrastructure, businesses must work to proactively mitigate the associated risks. To build secure, resilient, and equitable AI deployments, you must practice:

1. Data security and risk management: Ensure the integrity and confidentiality of training and input data by implementing security controls that prevent unauthorized access, modification, or usage. Whenever possible, restrict data retention to minimize exposure risk.

2. Encryption and access control: Identity and access controls and encryption should be applied to AI training data, LLMs, and outputs, similar to other apps or tools that access your company’s data.

3. AI governance and compliance: As regulations and industry standards evolve to manage AI-related risk, businesses will need to integrate AI usage into their overarching security frameworks, policies, and reporting.

4. AI ethics and governance: Companies must also document their stance on data bias, equality, transparency, and accountability, as well as implement mechanisms to enforce these standards across the organization. 

5. Compliance and security audits: AI tools, usage, and data must be systematically evaluated to ensure compliance with internal security policies, relevant regulations, and best practices.

6. Incident response strategies: To minimize the impact of a data breach, any processes involving sensitive data and the creation or use of AI-enabled tools should be integrated into the organization’s incident response strategy. This includes regular monitoring to enable quick response times, controls to contain the breach, recovery processes, and post-incident analysis.

Methods for identifying, assessing, and managing AI security risk 

AI threat detection and risk management go far beyond identifying potential threats. You must also contextualize AI threat data to illuminate the potential blast radius and inform remediation decisions.

AI-enabled tools can play a pivotal role in threat detection and risk assessments, despite the vulnerabilities and risks outlined above. 

• Implement a vulnerability scanning solution to monitor for common vulnerabilities for more proactive remediation.

• Establish a data classification and governance system to organize and highlight your most sensitive assets, paying special attention to which users and applications can access each.

• Create a structured framework for scoring and prioritizing risks based on their likelihood and potential impact, enabling more effective remediation prioritization.

• Perform regular AI security audits on all AI tools and datasets being created or utilized by the company, evaluating them based on vulnerabilities unique to their platforms.

• Establish a culture of AI security awareness and training on the risks associated with AI, how to use AI securely, and the proper channels for assessing and integrating new AI-enabled solutions into company workflows.

Leveraging AI in DevSecOps 

Today, AI is playing an exciting role in software and application development, representing both potential competitive advantages and security risks. AI-enabled tools can accelerate the software development lifecycle (SDLC) by automating repetitive tasks, suggesting code snippets, continuously monitoring for vulnerabilities, and recommending fixes.

To meet this rapidly changing landscape, security must evolve to manage the new risks being introduced into the SDLC. One of the ways to avoid malicious tampering and ethical concerns is to privately host and train your own LLM using proprietary data or code. However, this can be resource- and skill-intensive. Additionally, there’s a risk the LLM could amplify any vulnerabilities present in your existing codebase.

There are AI security tools designed to support software developers, such as static application security testing (SAST) tools that detect vulnerable code patterns the moment developers copy them into their IDE.

Protect your AI application with Snyk 

To keep pace with the rate technology and threats are evolving, AI-enabled security tools are quickly becoming a requirement for IT security and development teams. Snyk enables developers to adopt AI coding and testing solutions safely.

Snyk Code is an AI-powered security tool that scans your code for vulnerabilities in real time. Our self-hosted DeepCode AI, which is built upon 25M+ data flow cases, 19+ supported languages, and multiple AI models, powers Snyk Code, offering immediate remediation suggestions. 

Learn more about how Snyk empowers developers to increase productivity while securing AI-generated code.