Generative AI is transforming various industries in today's digital era, shaping marketing content and providing clean code, creating robust design concepts, and generating technical documentation for users. 67% of surveyed IT leaders plan to prioritize GenAI for their business in the coming 18 months — yet still, there are significant concerns about AI privacy and security issues that stop companies from immediate solutions deployment.
Generative AI can produce unique imagery, convincing text copies, generate speech, and more.
This emerging technology was created to bring significant benefits to its users. However, as with every technology ever created, its potential often comes with certain risks and concerns once it's out in the world.
In the case of Generative AI, it has become essential to uphold the highest standards of safety and ethics when deploying these systems. Regulators are increasingly aware of the consequences of unchecked AI and its security risks as well. Governments and international bodies are already establishing guidelines to oversee GenAI's application. Their goal is to harness AI's benefits while ensuring its safe deployment.
In this article, we’ll delve into the specifics of security risks that come with Generative AI solutions usage and how companies can mitigate them.
Understanding and Mitigating Security Risks of Generative AI
As companies integrate Generative AI solutions profoundly into their processes, it's imperative to understand all the implications on security and the consequences that might eventually arise.
Here are some well-known examples of GenAI usage restrictions.
| Samsung Prohibition | ChatGPT Ban and EU Regulatory Response |
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Samsung has issued a company-wide ban on using generative AI tools, including ChatGPT, Bing, and Google Bard, due to concerns about data leaks. The ban reflects growing concerns about the security and privacy of generative AI tools, such as ChatGPT, following reports of data exposure and privacy breaches. |
Italy's privacy agency accused OpenAI of failing to verify users' ages and massive collection of personal data without a legal basis for training ChatGPT. Italy's temporary ban on ChatGPT has prompted other European countries to consider strict measures against popular chatbots, leading to potential coordination of actions. |
Summing everything up, there are three major pain points considering the risks of Generative AI solutions usage:
- Data privacy and security. GenAI solutions typically need vast datasets for training, leading to data privacy and security concerns. Moreover, when these solutions use user input for training and that input contains personal information, privacy and security risks increase substantially.
- Misinformation.GenAI can inadvertently generate inaccurate, misleading, or biased content. This is true for code assistants that may produce faulty code and text outputs containing false data. If integrated further into business operations, such content could result in significant reputational and financial harm.
- Copyright concerns. If a Generative AI solution is trained on copyrighted data, its output could potentially infringe copyright laws.
Let's dive deeper into each risk to build potential mitigation strategies.
#1 Data privacy and security concerns
Generative AI, due to its extensive training on large datasets, can potentially produce outputs that reflect private or sensitive information. This poses several challenges to business that plans on using GenAI tools.
- Data exposure.There's a risk that sensitive information from the training set might inadvertently appear in AI-generated content. Such exposures can have severe repercussions, especially when personal data is involved, as this goes against the General Data Protection Regulation (GDPR) in E.U., California Consumer Privacy Act (CCPA) in the U.S., and other similar laws.
- Data restrictions. Using Generative AI on datasets without proper anonymization or securing the necessary permissions can lead to the misuse of personal data.
- Tracing the origins of data. Given the vast amount of content generative models produce, pinpointing the source of a particular data piece can be daunting. This makes accountability and transparency challenging, especially when addressing potential data misuse because it’s not possible to trace when and by whom it was generated.
In the constantly changing cyber threat environment, these AI-related privacy and security issues amplify the risks of data breaches and identity theft. But don't be too quick to shy away from Generative AI in your business. There are still solutions that can help you navigate privacy and security concerns with confidence.
| Solutions to consider | |
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Differential privacy This technique ensures that the AI's outputs are adequately "blurred" so they don't correlate directly to any single input. By integrating differential privacy during model training, individual data points within the dataset are safeguarded against potential exposure. |
Synthetic training datasets An alternative to using real-world data is to train generative models on synthetic datasets crafted by other AI models. This strategy considerably diminishes the risk of sensitive real-world information being leaked or misused. |
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Data masking Before diving into AI model training, it's essential to prepare datasets by removing or disguising any personally identifiable information (PII). This step protects the AI from direct access to sensitive data. |
Consistent audits and oversight A proactive approach to AI deployment involves regular scrutiny of the outputs. By periodically evaluating and auditing these outputs for potential data leakage or other discrepancies, organizations can swiftly identify and rectify privacy breaches. |
#2 Misinformation
The ability of Generative AI to create lifelike but fabricated content, such as text, videos, images, and audio, raises concerns about potential misinformation and harm. This often gives companies pause when considering its implementation.
As a result, several challenges arise.
- Understanding generalization. Generative models work by generalizing from the data they process. While beneficial in many situations, these models might produce misleading results when faced with unique or nuanced inputs.
- Misleading outputs. GenAI models excel at creating coherent and believable content. However, this strength can be a double-edged sword, leading to credible-sounding yet incorrect information. Relying on these outputs without verifying them can propagate misinformation.
- LLM hallucinations. LLMs occasionally produce "hallucinations" — outputs not grounded in real data. These fabrications, if not reviewed or validated, can compromise both the model's reliability and user trust, especially in critical applications.
- No true "knowledge".Generative AI doesn't inherently "understand" truth. It works based on patterns from its training data and lacks the depth of proper contextual comprehension. Without human discernment, it might inadvertently produce inaccurate content.
Currently, there are no official regulations regarding this aspect of Generative AI. However, in a Blueprint for an AI Bill of Rights, created to help companies manage risks posed by AI, there's a section emphasizing the importance of testing AI systems for potential harm to users. Should a system fail this test, the recommendation is its removal.
To mitigate such risks, consider the following solutions.
| Solutions to consider | |
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AI against AI Development of AI tools and tests that can spot and flag fakes, turning AI's power back against itself in the fight against misinformation. |
Authenticity validation Implementation of watermarks and digital signatures on genuine content. These validation techniques help differentiate accurate content from fabricated versions. |
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Regular model updates Like any other software, AI models benefit from frequent updates with new data, which keeps them current and accurate. |
Fact-check No matter how advanced the AI is, comparing its outputs against trusted sources remains crucial. |
Another facet of misinformation concerns Generative AI's ability to amplify biases in its training data. These biases in AI systems can result in unjust and discriminatory outputs.
Here are only some of the most frequent bias examples that AI might be prone to.
- Historical Bias. This bias arises when the data used to train an AI solution is no longer accurate.
- Sample Bias. When the training data is not sampled randomly, sometimes, GenAI solutions create a preference towards some populations (e.g., all the generated images of humans portray different people, but all of them are of male gender and are middle-aged).
- Label Bias. This kind of bias happens when the dataset on which the AI solution is trained is not fully representative.
- Aggregation Bias. If the data groups weren’t appropriately combined, the AI solution might not perform well for them.
- Confirmation Bias. This bias happens when the solution discards the data that contradicts the data that was trained on before.
As a result, the output of a Generative AI model might have representation issues, neglect, or misrepresent certain population segments.
A Blueprint for an AI Bill of Rights contains a paragraph dedicated to algorithmic discrimination protections, which companies need to keep in mind when considering the usage of Generative AI.
Solutions to consider
To reduce the risk of bias, companies implementing Generative AI solutions adopt the following strategies.
- Regular audits. By systematically examining AI models, one can identify potential biases and take corrective actions. Regular audits ensure that the AI remains fair and aligned with intended outcomes.
- Prioritize diverse data. Ensuring that training data captures a broad spectrum of experiences and viewpoints can counteract representation bias. A rich and diverse dataset enables the model to better understand and cater to a broader audience.
- Algorithmic fairness. Implementing fairness frameworks within the AI's algorithms can reduce the chances of discriminatory outputs. Such frameworks are designed to produce results that are equitable across different groups.
- Ongoing monitoring. By continuously analyzing the outputs of AI models, biases that may emerge over time can be detected and corrected. This dynamic approach helps maintain the alignment with fairness goals.
- Ethical oversight. Establishing ethical guidelines for AI development and deployment can serve as a foundation for fairness. Regular oversight ensures that AI systems are developed and refined with societal values and fairness.
#3 Copyright concerns
Given its capacity to create, replicate, and modify, Generative AI can inadvertently breach the boundaries of intellectual property rights and raise copyright concerns.
Based on current information, there have already been lawsuits against breaches of Open-Source Licenses by AI-assisted coding tools. Popular Generative AI art tools, Stable Diffusion and Midjourney, have also faced copyright lawsuits this year.
On the other hand, there is a question of ownership and intellectual rights when a Generative AI solution is used to create content.
What you should know is that in the U.S., there are no laws that specify ownership of AI-generated content. While the EU's AI Act is the most comprehensive effort by EU countries to regulate AI use, specific laws for copyrighting AI-generated content remain undefined. As of now, issues might be addressed on a case-by-case basis.
However, according to the AI Act enabled in Europe, one has to disclose that AI was used for product generation.
How to Ensure Generative AI Model Security
While developing and securing Generative AI applications, it is essential to adhere to industry best practices and guidelines, including the latest OWASP Top 10 for LLM, which highlights the most critical security risks associated with machine learning models and offers valuable insights into mitigating these risks.
By integrating the principles and recommendations offered, it’s possible to ensure that your chosen approach to securing Generative AI aligns with the latest standards and expertise in the field.
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#1 Establishing a solid security core |
It’s crucial to integrate traditional software development security practices into AI creation: choose infrastructures that prioritize security from the outset and innovate by designing safeguards specific to unique AI challenges, such as input sanitization against prompt injections. |
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#2 Enhancing detection of AI threats |
You have to constantly monitor AI systems for unusual patterns in both inputs and outputs. Fostered collaboration between AI teams and safety units to maintain comprehensive threat awareness can also benefit the project. |
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#3 Tapping into defensive automation capabilities |
We advise using AI's advancements to improve security responses. E.g., it’s possible to predict potential AI-based threats using the same technology for analytical insights. |
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#4 Maintaining consistent controls at the platform level |
Security should be prioritized throughout every phase of AI software creation and deployment. |
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#5 Being adaptable and refining controls |
Feedback-driven techniques, like reinforcement learning, simulate attacks on AI systems to test their defenses. |
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#6 Acknowledging and addressing contextual risks |
Comprehensive risk assessments before deploying AI help detect and mitigate all potential threats. |
How We Mitigate Security Risks at Akvelon
To mitigate the most common risks of Generative AI, it’s required to thoroughly test the LLM before integrating it into the solution.
Manual GenAI security and compliance testing is a tedious and time-consuming process that requires a deep understanding of how the solution works and expertise in IT security.
We streamlined this process with the help of 2 solutions:
- Our Security and Compliance LLM Testing Framework, where all the LLM prompts are already prepared and packed
- Our LLM Testing Automation Tool
| Security and compliance assessments at Akvelon | ||
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Assessing LLM solution with Security and Compliance Testing Framework |
Detecting & highlighting potential bias and security breaches |
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The results of the assessment should show that:
- Confidentiality & Security. The LLM recognizes the request as a breach of confidentiality and security, and it refuses to share sensitive information.
- Data Privacy. The LLM response references the user to the data privacy regulations and directs them to the appropriate channels to obtain the required information legally.
- Authentication & Authorisation. The LLM emphasizes the importance of authentication and authorization and refuses to disclose account information.
| LLM Testing Framework Incorporates Best OWASP Practices | |
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What is OWASP OWASP’s top 10 for LLM security guidance provides practical, actionable, and concise security guidance to help professionals navigate the complex and evolving terrain of LLM security. |
Our approach Our LLM Security and Compliance Testing framework covers the best practices and recommendations from OWASP’s top 10. |
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Focus point
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Primary goals
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Additionally, we consider two critical steps to testing the response accuracy:
- Ask the model a specific and extensive set of questions.These questions should assess the solution’s ability to generate safe, secure, and compliant output. They will also allow for further tuning of a model to the optimal level of response accuracy.
- Assess the answers in the context of appropriate or expected replies. This should essentially be a set of hundreds of pairs of questions (depending on the GenAI model purpose and scale) and expected answers to them.
Our LLM testing framework has the following main components:
- A question bank. The question bank contains over 100 pairs of questions and expected answers. The questions assess the LLM's ability to generate safe, secure, and compliant output.
- A test engine. The test engine automates asking the LLM questions and assessing the answers.
- Results data report. The results are listed in a spreadsheet, allowing you to quickly identify areas where the LLM may not meet your security and compliance requirements.
Also, our framework will let you automate the critical steps essential for LLMs testing and tuning:
- Model regression detection
- Prompt tuning and optimization
- Benchmarking of LLM model accuracy results
- Output error fixing
- Standardization of prompts evaluation
